Paolo Stegagno

Massimo Basile

Heinrich H Bülthoff

Antonio Franchi

A semi-autonomous UAV

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Abstract-We present the development of a semi-autonomous quadrotor UAV platform for indoor teleoperation using RGB-D technology as exceroceptive sensor. The platform integrates IMU and Dense Visual Odometry pose estimation in order to stabilize the UAV velocity and track the desired velocity commanded by a remote operator though an haptic interface. While being commanded, the quadrotor autonomously performs a persistent pan-scanning of the surrounding area in order to extend the intrinsically limited field of view. The RGB-D sensor is used also for collision-safe navigation using a probabilistically updated local obstacle map. In the operator visual feedback, pan-scanning movement is real time compensated by an IMU-based adaptive filtering algorithm that lets the operator perform the drive experience in a oscillationfree frame. An additional sensory channel for the operator is provided by the haptic feedback, which is based on the obstacle map and velocity tracking error in order to convey information about the environment and quadrotor state. The effectiveness of the platform is validated by means of experiments performed without the aid of any external positioning system.

I. INTRODUCTION

Micro UAVs constitute the ideal platform for many robotic task, such as exploration, mapping, and surveillance. The unconstrained workspace and versatility allow to use them as flying sensors and actuators to reach and operate on places that are out of the range of more classical ground mobile robots. On the other hand many real-world tasks require (because of their nature or due to governmental regulation) that one or more humans participate to the mission in the quality of either simple supervisors or skilled operators, e.g., in the case of search-and-rescue missions [START_REF] Murphy | Search and rescue robotics[END_REF],

Recent works have investigated the role of haptic feedback and the fact that it can be successfully used in order to increase the operator situational awareness (see, e.g., [START_REF] Lam | Artificial force field for haptic feedback in UAV teleoperation[END_REF] and references therein) and therefore to have a positive impact on the human decisions. For this reason haptic shared control of UAVs represents an emerging topic attracting the attention of many research groups in the recent years.

Concerning the single-UAV case, an extensive study has been already done, with special regard on the theoretical point of view. The authors of [START_REF] Lam | Artificial force field for haptic feedback in UAV teleoperation[END_REF] have studied how to properly design artificial force fields for the haptic cue when bilaterally teleoperating a UAV, while [START_REF] Schill | Admittance mode framework for haptic teleoperation of hovering vehicles with unlimited workspace[END_REF] presented the design of an admittance control paradigm from the master side P. Stegagno, M. Basile, H. H. Bülthoff and A. Franchi are with the Max Planck Institute for Biological Cybernetics, Department of Human Perception Cognition and Action, Spemannstraße 38, 72076 Tübingen, Germany { paolo.stegagno, mbasile, hhb}@tuebingen.mpg.de.

A. Franchi is also with Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Analyse et d'Architecture des Systèmes (LAAS), 7 Avenue du Colonel Roche, 31077 Toulouse CEDEX 4, France.

antonio.franchi@laas.fr with position feedback. Single-UAV teleoperation control based on the port-Hamiltonian approach has been presented by [START_REF] Stramigioli | A novel approach to haptic tele-operation of aerial robot vehicles[END_REF] and extended by [START_REF] Mersha | Switching-based mapping and control for haptic teleoperation of aerial robots[END_REF]. In [START_REF] Rifa | Haptic-based bilateral teleoperation of underactuated unmanned aerial vehicles[END_REF] a strategy to generate the haptic feedback as a virtual force based on both telemetric and optic flow sensors is designed. A novel force feedback user interface for mobile robotic vehicles with dynamics has been shown by [START_REF] Hou | Representation of vehicle dynamics in haptic teleoperation of aerial robots[END_REF], and a novel force feedback algorithm that allows the user to feel the texture of the environment has been recently presented by [START_REF] Omari | Bilateral haptic teleoperation of VTOL UAVs[END_REF]. Finally in [START_REF] Masone | Interactive planning of persistent trajectories for human-assisted navigation of mobile robots[END_REF], [START_REF] Masone | Semi-autonomous trajectory generation for mobile robots with integral haptic shared control[END_REF] the authors have shown how single-robot haptic teleoperation can be seamlessly integrated with complex path planning techniques.

Concerning the, more challenging, multi-UAV case, [START_REF] Franchi | Shared control: Balancing autonomy and human assistance with a group of quadrotor UAVs[END_REF], [START_REF] Franchi | Modeling and control of UAV bearingformations with bilateral high-level steering[END_REF], [START_REF] Franchi | Bilateral teleoperation of groups of mobile robots with time-varying topology[END_REF], [START_REF] Lee | Semi-autonomous haptic teleoperation control architecture of multiple unmanned aerial vehicles[END_REF] presented an extensive framework to control a group of UAVs that can be interfaced with multiple operators by means of haptic devices, e.g., to control some generalized velocity of the group formation, and to receive a feedback that is informative of the tracking quality, the swarm status, and properties of the surrounding environment, such as presence of obstacles or wind gusts. In [START_REF] Riedel | Experiments on intercontinental haptic control of multiple UAVs[END_REF], the authors show how that framework can be applied to perform teleoperation over intercontinental distances.

The majority of the works never addressed the problem in a real world scenario, either employing simulation or external motion capture systems. Even though in [START_REF] Omari | Bilateral haptic teleoperation of VTOL UAVs[END_REF] the obstacles are detected through a laser scanner the state for control purpose is still retrieved by an external camera system. Similarly, in [START_REF] Franchi | Modeling and control of UAV bearingformations with bilateral high-level steering[END_REF] on-board cameras are used to measure the relative bearings, but the velocities were obtained through an external motion capture system. At the best of our knowledge none of the approaches dealing with haptic-teleoperation of UAVs have been experimentally proven on a platform that uses onboard sensors only.

The goal of this paper is therefore to present a UAV platform designed for haptic teleoperation that can be easily operated using velocity control in real unstructured scenarios providing safety against obstacles and relying on onboard sensor only, namely IMU and RGB-D measurements. In fact, this essential sensor equipment, thanks to the presence of a depth camera, is relatively richer with respect to the standard IMU-camera integration setting.

While being commanded, the quadrotor autonomously performs several tasks to improve safety and ease of operation.

First, it extends the intrinsically limited field of view (FOV) of the exteroceptive sensor executing a persistent pan-scanning of the local surrounding area using its yaw degree of freedom. In addition, the camera sensor is used for collision-safe navigation, enacting obstacle avoidance on a local obstacle map attached to the quadrotor body frame, that is probabilistically updated while pan-scanning, thus fully exploiting the augmented FOV.

The operator will receive two feedbacks from the UAV: visual feedback from the camera and haptic feedback. From the point of view of the operator, the pan-scanning movement is realtime compensated by an IMU-based adaptive filtering algorithm that lets the operator perform the drive experience in a oscillation-free frame that is subject to the operator commands only. Haptic feedback is based on velocity tracking error and the obstacle map, thus creating and additional sensory channel for the operator in order to convey enhanced information about the environment and quadrotor state. The effectiveness of the platform is validated by means of experiments performed without the aid of any external positioning system.

The choice of using an RGB-D camera brings several advantages and also some drawbacks. First, depth measurements are extremely useful because they allow a metric estimation of the velocity. Monocular camera methods, as, e.g., the ones based on PTAM [START_REF] Scaramuzza | Vision-controlled micro flying robots: from system design to autonomous navigation and mapping in GPS-denied environments[END_REF], do not provide metric information directly and typically need additional sensor fusion with the accelerometer reading, thus requiring a persistently accelerated motion to properly work metrically. In addition, the measurements coming from an RGB-D sensor can be easily used to perform reliable obstacle avoidance. On the other side, RGB-D sensors are usually sensible to natural light, so our system is specifically designed for indoor navigation. To overcome this issue in future, we may take into consideration the possibility to substitute the RGB-D sensor with a stereo camera in the outdoor phases.

The rest of the paper is organized as follows. Section II presents the system setup. Section III presents the generation of the reference velocity and its application in the flight controller, Section IV and Section V address the velocity estimation and the generation of a local obstacle map respectively, Section VI addresses the visual and haptic feedback provided to the human operator. Section VII presents some experimental results and Section VIII concludes the paper.

II. SYSTEM SETUP

The hardware configuration of the system is based on the mechanical frame, actuators, microcontrollers, and inertial measurement unit (IMU) of the MK-Quadro from MikroKopter.Its actuation system consists of four plastic propellers with a diameter of 0.254 m, and a total span and weight of the frame of 0.5 m and 0.12 kg, respectively. The propellers are driven by four brushless controllers connected to a control board through a standard I 2 C bus. The core of the control board is a 8-bit Atmega1284p microcontroller operating at 20 MHz. The board also includes three 3D LIS344alh accelerometers (0.0039g0 m/s2 resolution and 2g0 m/s2 range) and three ADXRS610 gyros (0.586 deg/s resolution and 300 deg/s range), directly connected to the analog to digital 10-bit converters of the low-level microcontroller and a pressure sensor MPX4115A.

The manufacturer provides the board pre-installed with its own firmware to drive the quadrotor with a remote control, which we have substituted by a different software that has new features and a different interface that allows to control the robot through serial interface. In detail, we have established two xBee serial connections operating at a baud-rate of 115 200 Bd on the two serial ports offered by the board. While one connection is employed to send and receive commands and data to and from the microcontroller, the second connection operates as a one-way channel to retrieve IMU and gyro measurements at high-frequency (500 Hz). The whole system is powered by a 2600 mAh LiPo battery which guarantees an endurance of around 10 min of flight in normal regimes.

In addition, we have retrofitted the MK-Quadro frame with an Asus Xtion RGB-D sensor to obtain exteroceptive measurements of the environment. The RGB-D sensor, from now on referred to simply as 'camera', is rigidly attached to the frame through three 5 mm diameter plastic bars, heading approximatively at 45°on the right of the quadrotor and tilted by approximatively 30°downward, vertically mounted to increase the vertical FOV. At current state of development, since the control board does not provide any USB channel nor the computational power to handle the image stream, the camera is connected to the base station through a wired USB connection system. We are almost ready to integrate in the near future a quad core Odroid-family board able to interfacing with the camera and perform the whole control onboard. The complete system, depicted in Fig. 1, has a weight of approximately 1.000 kg.

Figure 2 represents the relevant frames used in this work. Denote with W : {O W , X W , Y W , Z W } the inertial (world) frame defined with the North-West-Up (NWU) convention, hence with Z W pointing in the opposite direction of the gravity vector, and with Q

: {O Q , X Q , Y Q , Z Q } a frame
attached to a representative point of the quadrotor (ideally its center of mass), which conforms to the North-East-Down (NED) convention as common in the aerospace field. In general, we will denote with A p B the position of the origin of a frame B in another frame A and with R A B ∈ SO(3) the rotation matrix expressing the orientation of the frame 3) represent the position and orientation of Q in W, respectively. Finally, denote with φ, θ, ψ respectively the roll, pitch and yaw angles that represent the orientation of the quadrotor in W, i.e., such that

B in A. Hence, W p Q ∈ R 3 and R W Q ∈ SO(
R W Q = R x (π)R z (ψ)R y (θ)R x (φ), where R x (•), R y (•), R z (•)
represent the canonical rotation matrices about the axes X, Y, and Z respectively. Therefore, e.g., R x (π) is the rotation matrix matrix transforming the NED coordinates in NWU.

In order to express the human commands we introduce the (NED) horizontal frame H : A human operator interfaces with the system and remotely operate the UAV through an omega.6 haptic device, shown in Fig. 3, and a standard 22" screen. The device provides a handle with six degrees of freedom (DOFs), three translational and three rotational, offering complete motion to a 3D rigid body. However, we have limited our system to use only the three translational DOFs.

{O H , X H , Y H , Z H } such that O H ≡ O Q and Z H -Z W . Then, the rotation matrix between H and Q is R H Q = R y (θ)R x (φ).
In order to express all omega.6-related quantities, we define D : {O D , X D , Y D , Z D } as the inertial NED frame of reference whose origin is located in the steady position of the end effector, placed at the center of its Cartesian workspace. The translational dynamics of the haptic device can be modeled through the Lagrangian equation:

M (q)q + C(q, q) q = τ + f (1)
where q = (q x q y q z ) T ∈ R 3 is the position of the handle in D, M (q) ∈ R 3×3 is the positive-definite/symmetric inertia matrix, C(q, q) ∈ R 3×3 is the Coriolis matrix, and τ , f ∈ R 3 are the control and human forces respectively.

III. REFERENCE VELOCITY GENERATION AND EXECUTION Apart from the attitude of the quadrotor, represented by φ, θ, the position W p Q and the yaw ψ are not observable without performing a full SLAM approach, since the robot has only relative measurements of the environment and no absolute positioning capabilities or compass. Hence, it is not possible to regulate the motion on a specific desired position or yaw orientation, and the UAV can only follow a reference velocity vector and reference yaw rate. Clearly one could integrate the measurements and the motion to obtain an odometrylike estimation of the relative position and yaw from the starting pose. However this method can be employed only for a short time since it would unavoidably drift due to the aforementioned observability issue. Another way would be to implement a computationally expensive full SLAM approach. However, the focus of this work is to develop a basic, solid, and adaptable platform to perform indoor aerial bilateral teleoperation in which the decision on high the level motion strategies are delegated to the human operator.

Denote with H v r and ψr the reference velocity vector and yaw-rate, respectively. The velocity H v r is conveniently expressed in the horizontal frame H in order to abstract from the current attitude of the quadrotor. Note also, that driving the UAV in the frame H instead of the frame B is much more convenient for the human operator. These reference quantities are provided to a flight controller (referred to as 'tracker' in the following) that uses the estimated state of the robot (computed as per Sec. IV) to regulate the tracking error to zero. The tracker, described in [START_REF] Lee | Semi-autonomous haptic teleoperation control architecture of multiple unmanned aerial vehicles[END_REF], is a simple PID controller with gravity compensation computing the required thrust, roll and pitch angles that are needed to accelerate as requested and therefore applying the torques that are necessary to track tht desired angles. Note that the integral term is not performed using the position of the UAV, as usual when dealing with localization issues, being that information unobservable. Hence, the integral term is computed simply integrating the error of the velocity. Yaw rate is tracked in a similar fashion using the yaw torque command.

Some steps are performed in order to generate H v r and ψr on the basis of the commands of the human operator and other autonomous actions. In fact, although the robot tries to reproduce the velocity and yaw rate commanded by the operator, it also carries on some tasks autonomously in order to guarantee safety during normal operation and improve the capability of the sensors, as described in the following.

The first step to obtain H v r and ψr is the computation of the desired velocity H v d and yaw rate ψd commanded by the human operator by means of the haptic device. This is done mapping the 3D haptic device position as follows:

H v d = k v   q x cos α q x sin α q z   (2) ψd = -k ψ q y ( 3
)
where k v and k ψ are positive gains and α is a parameter expressing the direction of the desired forward motion of the quadrotor on the horizontal plane of H. The effect of (2-3) is that whenever the operator pushes forward the end effector of the haptic device, the robot will move on the horizontal plane the direction α, whenever the operator lifts the end effector the UAV will increase its altitude (and vice versa), and whenever the operator moves the end effector sideways the UAV will rotate its yaw angle accordingly. No motion command is then allowed on the direction orthogonal to α.

The more meaningful way to chose α would be to select the yaw angle of the camera in Q as α = atan2(r 21 , r 11 ). where R Q C = [r ij ] i=1,...,3, j=1,...,3 . However, in the following paragraphs we shall show a more sophisticated method related to the pan-scanning motion of the quadrotor aimed at increasing the virtual FOV of its exteroceptive sensor.

The first and most important task performed autonomously by the robot is obstacle avoidance. The RGB-D camera is the only exteroceptive onboard sensor, but is particularly suited for this purpose. Our obstacle avoidance module divides the depth camera images retrieved by the camera into n × m sectors, and extracts the closest point in each sector. Let be C p ij the selected point of sector (i, j), expressed in the camera frame. Using extrinsic calibration parameters, i.e., Q p C and R Q C , together with the roll and pitch angles of the UAV, i.e., φ, θ, it is possible to compute the position H p ij in the horizontal frame H. Then H p ij will be considered an obstacle, and a repulsive artificial potential will be computed for each (i, j). The weighted sum of all contributions produces an obstacle avoidance velocity term H v o that is then summed to the operator desired velocity.

The main drawback of this obstacle avoidance algorithm is the very limited FOV of the sensor. However, we can take advantage of the fact that the execution of the yaw rate is independent from the execution of the UAV Cartesian velocity. Therefore, even though it is not possible to extend the instantaneous FOV of the sensor, we extend it over time by adding a sinusoidal signal ψs on the commanded yaw rate

ψs =A sin(ωt) (4)
where A and ω represent parameters opportunely chosen and t represents the time of the experiment. If the yaw dynamics perfectly tracks ψs then the result in the motion of the UAV is a co-sinusoidal oscillation of the yaw angle ψ with amplitude A/ω and pulsation ω. However, under action of the tracker, the actual closed loop yaw dynamics is not a perfect integrator but a first order linear system which then generates a yaw angle ψ that will oscillate with amplitude A ω A/ω, pulsation ω and phase ϕ ω . It is not difficult to compute both A ω and ϕ ω from the controller parameters or to identify them through a few empirical measurements. Addition of the term (4) will cause the camera to span a broad angle and the robot to avoid obstacles which would be otherwise ignored due to the limitation of the sensor. Nevertheless, the oscillation causes a serious threaten to the ease of operation because of two side effects.

First, if the UAV is commanded in the pointing direction of the yaw angle of the camera, it would oscillate with it. Hence, the operator trying to drive the UAV on a straight planar line will result in the execution of a sinusoid on the plane. Second, the visualization of the camera stream is now very uncomfortable for the operator, because of the continuous oscillation of the camera frame.

It is worth to mention that the first issue would be avoided

Dense Visual Odometry

1€-filter

complementary filter p C C 0 k p C C 0 k ˆvQ Q vC C ˆk k φ ψ v Q H low-pass filter Q ωQ ω Q Q a IMU RGB-D Q ω R C C 0 k ˆRQ H pC C0 k ˆpC C0 k- RC C0 k ˆ( ) - ∆T v C Ck k ˆpC Q R C Q -Q ω x
Fig. 4: A block scheme representation of the velocity estimation system.

if the camera was mounted on a pan actuator, rotating independently from the orientation of the UAV. Nevertheless this solution implies new hardware addition, a new actuator and ultimately more weight and less time of flight. Hence, we found it more convenient to exploit the intrinsic characteristic of the quadrotor at its best.

However we have conceived and developed solutions for both the actuation and visual issue. We describe the first in the following and we shall defer the description of our solution for the second issue to Sec. VI-B.

In the considered situation, the best solution to drive the UAV is to have the robot moving always in the direction of the center of the oscillation. In this way, the operator has the opportunity of 'taking a look' to what happens on the two sides of the direction of its motion. This can be easily done by rotating the first two components of (2) by the additional angle ψs introduced by the oscillation:

ψs = A ω A ω cos(ωt + ϕ ω ) (5)
Hence, in this situation α can be conveniently chosen as α = atan2(r 21 , r 11 ) + ψs

Wrapping up, the reference velocity H v r for the controller is computed as

H v r = H v d + H v o = k v   q x cos α q x sin α q z   + H v o (7)
with α given by ( 6), and the reference yaw rate is computed as

ψr = ψd + ψs = -k ψ q y + A sin(ωt) (8)
Other additional autonomous tasks can be added in the proposed scheme. For example we easily integrated autonomous take off and landing subroutines in our system that are not described here for the sake of space.

Finally, in order to take full advantage of the sinusoidal oscillation introduced in the yaw rate of the UAV, a filtering on the measured obstacles H p ij is needed in the obstacle avoidance module. In fact, an appropriate filter would give the opportunity to maintain an estimate of close obstacles that are not in the FOV of the camera but have been measured in the immediate past. We actually implemented such a filter and we describe it in Sec. V.

IV. STATE ESTIMATION

The scheme presented in Sec. III needs some quantities that must be retrieved by the UAV onboard sensors. The working principle of our estimation system is summarized in Fig. 4, and uses both measurements of the IMU and of the depth-camera. The first ones are used in a complementary filter to compute estimates φ, θ of the roll and pitch angles as described in [START_REF] Mahony | Nonlinear complementary filters on the special orthogonal group[END_REF], [START_REF] Martin | The true role of accelerometer feedback in quadrotor control[END_REF]. In addition, a low-pass filter improves the angular velocity measurements Q ωQ from the gyros producing an estimate denoted by Q ωQ .

Once the attitude (i.e., roll and pitch) of the quadrotor is known, the images from the depth camera are used to obtain an estimate of the velocity of the quadrotor in the frame H.

At each time-step k the images are used to feed the dvo 1 algorithm [START_REF] Kerl | Robust odometry estimation for RGB-D cameras[END_REF] which provides the estimates C0 pC k , RC0

C k of the position C0 p C k and orientation R C0 C k of the camera frame C k at time-step k w.r.t. the camera frame C 0 at time-step 0. Obviously, since dvo performs a visual odometry algorithm, the estimates will eventually diverge from the true value and cannot be used for a long time to obtain absolute position and orientation measurements. Nevertheless, it is possible to extract a noisy but non-drifting measurement of the velocity C k v C k , i.e., the velocity of the origin O C k of the frame C k expressed in C k , through the equation:

C k v C k = R C k C0 ( C0 p C k -C0 p C k-1 ) ∆T (9)
where C k-1 denotes the camera frame at time k -1 and ∆T is the elapsed time between time-steps k -1 and k. However, since (9) corresponds to a first order numerical derivation of the position C0 p C it would be considerably affected by noise. For this reason, instead of (9), we use

C k vC k = RC k C0 ( C0 pC k -C0 pC k-1 ) ∆T ( 10
)
where C0 pC k , and RC0 C k are the 1¤-filtered [START_REF] Casiez | 1 euro filter: a simple speedbased low-pass filter for noisy input in interactive system[END_REF] versions of C0 pC k , and RC0

C k respectively. The velocity C v C of O C in C can be written as

C v C = R C Q Q v C = R C Q ( Q v Q + Q ω Q × Q p C ). (11)
Therefore we compute an estimate of Q v Q at time-step k as

Q vQ = R Q C C k vC k -Q ωQ × Q p C (12)
Finally, given the estimates φ, θ, we obtain the sought velocity in the H frame

H vQ = RH Q Q vQ ( 13
)
which is then used in the velocity tracker in order to follow the velocity commanded by the operator and the obstacle avoidance module.

1 https://github.com/tum-vision/dvo

V. LOCAL OBSTACLE MAPS

In order to further extend the FOV of the camera and increase the safety of the flights, the system builds and propagate in time a local map of the obstacles. The obstacle avoidance will be then performed on the estimates provided by this module. Nevertheless, this module does not perform a SLAM algorithm, since there is no localization purpose in its operation and the produced obstacle map will be limited both in time and space.

The working principle is that of a Bayesian filter. A measurement step initializes and improves the current estimates, while a time update estimate propagate current estimates in time using the state of the UAV.

The main idea is to divide the world surrounding the UAV in cells by a discretization of azimuth and zenith distance angles. Whenever a measurement occurs (i.e., a set of closest points is extracted by a frame from the camera as explained in Sec. III), each measured point is assigned to the correct cell based on its relative azimuth and zenith distance, and overwrites any other point possibly present in that cell. This is to obtain a reactive behavior of the estimates. More copies of the same obstacle point are introduced in the cell, in order to be used as particles.

The time update uses as input the estimated velocity H vQ and yaw rate ψ of the UAV which is used to propagate each estimated obstacle points pj according to the following:

pk+1 j = R T k k+1 (p k j -u) (14)
where

pk+1 j is the new estimate, u = ∆T ( H v k+1 Q -H v k Q + n v )/2 is the estimated linear displacement of the UAV between time k and time k + 1, R k k+1 = R z (ψ k k+1
) is the rotation matrix about the vertical axis due to the yaw displacement ψ k k+1 = ∆T ( ψk+1 -ψk + n ψ )/2 between time k and time k + 1, ∆T is the elapsed time between k and k+1, and n v , n ψ are two samples randomly extracted on the model of the noise over the estimates of the velocity and the yaw rate, both assumed to be gaussian random variables with zero mean and known covariance.

Examples of obstacle maps can be observed in the middle column of Figs. 9 and 10, which shows the image plane of the onboard camera with reprojection of the the 3D obstacle points (red dots). Note the particles in the right part of the third row, middle column, Fig. 9, which are propagated and not directly measured, since the obstacles are not in the FOV.

VI. VISUAL/HAPTIC FEEDBACK FOR THE OPERATOR

A. Haptic Feedback

The main focus of this work is to enable a human operator to remotely control the UAV using only onboard sensors. At this aim, great importance assumes the feedback that the human receives from the UAV and his interface with the system. Hence, we propose a dual haptic and visual feedback in order to enable the operator to safely control the robot.

The haptic feedback algorithm resembles the one proposed in [START_REF] Lee | Semi-autonomous haptic teleoperation control architecture of multiple unmanned aerial vehicles[END_REF] but with a special adaptation to our particular input paradigm and pan-scanning scheme. The force feedback provided by the device to the operator is governed by:

τ (t) := -B q -K f q -K(q -q) (15)
where B, K, K f ∈ R 3×3 are the positive-(semi)definite diagonal gain matrices, and ṽ is defined as the vector q = (q x qψ qz ) T is such that

qx = 1 k v (cos α sin α) H vx Q H vy Q (16) qψ = 1 k ψ ψ -ψs (17)
qz = 1 k v H vz Q (18)
where 13) and ψ is the estimate of the yaw rate obtained using the IMU.

H vx Q , H vy Q , H vz Q are the 3 components of H vQ in (
The force in ( 15) is composed of three terms: the first is a damping term, the second is a spring which tries to bring the end effector back in O D , and the third provides the real haptic cue, with a force proportional to the error between the commanded and actual velocity/yaw-rate. Note the presence of α and ψs in ( 16) and ( 17), needed to cancel the effect of persistent pan-scanning thus making this additional movement transparent to the operator.

B. Visual Feedback

The visual feedback is more articulated due to the oscillatory (pan-scanning) component added to the reference yaw rate. As stated before, the simple visualization of the image stream coming from the camera would produce an unpleasant effect for the operator, since the image would continuously rapidly change, even in hovering condition. Moreover, this effect would make it difficult to identify at each time instant the forward direction of motion of the quadrotor in the image.

A much better visualization would be achieved if the image moved on the screen so that the forward direction of motion (the center of oscillation of the yaw) was still on the screen. Based on this principle, we have implemented a filter (Fig. 5) which is able to estimate the actual frequency and the center of the oscillation and prints the images on the display so as to keep always the center of oscillation in the center of the screen.

In particular, since a direct measurement of the yaw is not available, the filtering is performed on the yaw rate measured by the IMU. Clearly a simple integration of the measured yaw rate would not work, because of the presence of noise and unavoidable bias. This signal is passed through a bandpass filter which selects only the frequency f = ω/2π of the oscillation, erasing all other components. Note that the filter is designed to have unitary gain and zero phase shift at the selected frequency. In principle, we would like the frequency of the oscillation to be not known and variable during operation. For this reason, the band-pass filter is tuned accordingly to the output of a peak-to-peak frequency estimation filter.

The output of this two block system is an estimate ψs of the yaw rate due to the additional oscillation, which integrated over time returns an estimate of the yaw displacement with respect to the direction of motion. Nevertheless, this estimate is affected by a bias that can be estimated low-pass filtering the initial signal, hence subtracted in order to obtain an unbiased estimate ψs . Then, the images on the screen are printed with a horizontal displacement in pixel equal to px = k px ψs , where k px is a conversion factor computed through the knowledge of the image size and the angular FOV.

Similar compensations have been enacted also for the pitch and roll angles, but no filtering is needed because they are directly estimated by the UAV. Additional features to improve the pleasantness of the remote operation are the exponential decay of the images in the background and the re-projection on screen of the obstacle points estimated by the filter. Some example frames are given in Figs. 9 and 10, however to fully appreciate its dynamics we suggest to watch the attached video of the experiments.

VII. EXPERIMENTAL VALIDATION

The main framework in which the platform is developed is TeleKyb [START_REF] Grabe | The TeleKyb framework for a modular and extendible ROS-based quadrotor control[END_REF], a ROS-based project specifically designed for the development of applications on UAVs and oriented to multi-robot execution. In addition to TeleKyb other general purpose tools as Matlab and Openni have been used in order to accomplish preliminary tasks, as the calibration of the camera and quadrotor frames, and online camera stream acquisition.

We have conducted several experiments in order to evaluate the performance of the proposed system. In all the experiments we used the estimated quantities in the flight controller. Additionally, in order to numerically evaluate the accuracy of the estimation algorithm, we used an external motion capture system as ground truth.We report here the results of a representative experiment. A clip of this and other experiments can be watched in the accompanying video and at http://antoniofranchi.com/videos/onboard_haptele_icra.html.

The experiment has been performed in a 10 m × 10 m arena with a tall obstacle placed approximately at its center. During the experiment, the operator is not able to see directly the UAV and uses only the provided feedback to control the system. Since the arena is completely black and the estimation system exploits vision, it was necessary to add features to the environment, as a textured carpet, a desk, several boxes and other objects.

In Fig. 6 z velocity components expressed in H. Commanded and measured yaw rate from the onboard gyroscope are shown in Fig. 7. The part of the rotation rate commanded by the operator is the mean of the sinusoidal plot. All plots show that the velocity estimate is very similar to the ground truth counterpart. In addition, even if not perfectly, the quadrotor reproduces quite faithfully the commanded velocity and yawrate. This precision level is definitely enough, to perform teleoperation, and constitutes a great achievement considering that it is obtained using only onboard measurements.

At the beginning the quadrotor is on the ground and at 2 s it takes off. Then the human operator commands the UAV to go straight in one direction until 25 s. Then the human operator rotates the UAV in the direction of the obstacle (in fact the mean of the sinusoidal plot increases from zero to 0.4 rad/s) and finally drives the UAV toward the central obstacle at constant speed (the phase lasts approximately from 36 s to 43 s).

At time 43 s the UAV comes close enough to the obstacle and the obstacle potential starts rising thus adding to the commanded velocity a repulsive component that lets the actual velocity greatly deviate from the commanded one. Between 43 s and 47 s the operator pushes the UAV against the obstacle twice thus generating two peaks in the actual velocities, which also significantly differs from commanded velocity. In this phase the operator feels high opposing forces informing him/her about the presence of the obstacle. The experiment concludes with the UAV landing on the ground.

In Fig. 8 we show the evolution over time of the distance between the center of mass of the quadrotor and the surrounding obstacles, comprising the obstacle, the walls, the floor, and the ceiling. The distance obtained from the ground truth is shown with a red plot and the distance obtained fusing the depth-camera readings and the attitude estimation is shown with a blue plot. It is possible to see how the estimated distance always underestimates the real distance, which represents a good feature for the safety of the platform. In the second phase, when the UAV is flying, i.e., after approx. 42 s, as soon as the distance reaches the minimum admissible distance (which is set to 0.45 m), the quadrotor is pushed back from the obstacle. This happens in correspondence of the spikes in the plots of the velocities. Figure 9 presents some significant snapshots of the experiment with both global and onboard views, plus the haptic interface operated by the human.

In order to thoroughly validate the approach we conducted also experiments in real office environment, in which the UAV is driven over several desks. Some significant snapshots of one of these experiments are given in Fig. 10.

VIII. DISCUSSION AND FUTURE WORK

In this paper we have presented a semi-autonomous UAV platform that is used for indoor haptic teleoperation control and is able to exploit only onboard sensors, thus being independent from any motion capture system. No assumptions on the environment are needed, such, e.g., the presence of planar surfaces or objects of known sizes. The limitations of the sensor in terms of small FOV have been overcome by exploiting a pan-scanning action that also improves the detection of surrounding obstacles, estimated in a local moving map through filtering.

In the near future we are planning to install a single-board Odroid-based PC, which will be able to handle the camera images. Other improvements will consider different filtering strategies for the angular velocities and for the whole state. Once the platform is complete, we plan to employ it to perform teleoperation experiments over the internet, hence introducing significant delay on the commanded velocities.

Fig. 1 :

 1 Fig. 1: The quadrotor setup.

Fig. 2 :

 2 Fig. 2: A representation of all frames involved in the control and estimation of the velocity of the quadrotor.

Fig. 3 :

 3 Fig. 3: The haptic device and its frame of reference.

  Finally, we consider the camera frame C : {O C , X C , Y C , Z C }. Since the camera is rigidly attached to the quadrotor, Q p C and R Q C are constant extrinsic parameters.

  block scheme of the system for the estimation of ψs.

Fig. 6 :

 6 Fig.6: Comparison between the 2 components of: the estimated velocity (blue plots), the ground truth velocity measured by an external motion capture system (red plots), and the velocity commanded by the human operator (green plots). All the velocities are expressed in the horizontal body frame H.

Fig. 7 :

 7 Fig. 7: Comparison between the measure of the yaw rate obtained from the gyroscope (blue plot) and the commanded yaw rate (green plot), i.e. the sum of the operator command and the pan-scanning sinusoidal carrier.

Fig. 8 :Fig. 9 :

 89 Fig. 8: Real (red plot) and measured (blue plot) distance between the quadrotor and the surrounding environment. When in flight (after 15 s) the minimum safe distance for the obstacle avoidance is set to 0.45 m

Fig. 10 :

 10 Fig. 10: Snapshots of a typical experiment in a real office environment. Each row refers to a different time instant. Left column: global views of the environment. Middle column: onboard views from the visualizer (red dots are detected obstacles). Right column: haptic interface used by the human operator. For safety reasons, a loose rope secures the UAV and is moved by a person following the UAV motion.

  Preprint version, final version at http://ieeexplore.ieee.org/ 2014 IEEE Int. Conf. on Robotics and Automation, Hong Kong, China A Semi-autonomous UAV Platform for Indoor Remote Operation with Visual and Haptic Feedback Paolo Stegagno, Massimo Basile, Heinrich H. Bülthoff, and Antonio Franchi

Preprint version, final version at http://ieeexplore.ieee.org/ 2

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IEEE ICRA

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Thomas Degueule
email: thomas.degueule@inria.fr

Towards Language Interfaces for DSLs Integration

Developing software-intensive systems involves many stakeholders who bring their expertise on specific concerns of the developed system. Model-Driven Engineering (MDE) proposes to address each concern separately with a dedicated Domain-Specific (possibly modeling) Language (DSL) closely tied to the needs of each stakeholder [START_REF] France | Model-driven development of complex software: A research roadmap[END_REF]. With DSLs, models are expressed in terms of problem-level abstractions. Associated tools are then used to semiautomatically transform the models into concrete artifacts. However, the definition of a DSL and its tooling (e.g., checkers, editors, generators, model transformations) still requires significant development efforts for, by definition, a limited audience.

DSLs evolve as the concepts in a domain and the expert understanding of the domain evolve. A mere example is the addition, refinement or removal of features from a DSL, with possibly the intent to ensure the compatibility between the subsequent versions. Additionally, the current practice in industry has led to widespread use of small independently developed DSLs leading to challenges related to the sharing of languages and corresponding tools [START_REF] Whittle | The state of practice in model-driven engineering[END_REF]. For example, the core concepts of an action language can be shared by all DSLs that encompass the expression of actions. Finally, while more and more DSLs are developed in various domains, recurrent paradigms are observed (e.g., state-transition, classifiers) with their own syntactic and semantic variation points reflecting the domain specificities (e.g., family of finite-state machines).

Given the DSL development costs, redefining from scratch a new ecosystem of tools for each variant of a DSL is not scalable. Instead, one would like to leverage the commonalities of these languages to enable reuse of existing tools. An underlying challenge is the modular definition of languages, i.e., the possibility to define either self-contained or incomplete language components (in terms of syntax and semantics) that could be recomposed afterwards for the definition of new DSLs. To support modularity, DSLs designers should be able to define proper provided and required interfaces for each language component, together with composition operators.

To improve modularity and abstraction capabilities in software language engineering and support the aforementioned scenarios, we advocate the definition of explicit language interfaces on top of language implementations. Language interfaces allow to abstract some of the intrinsic complexity carried in the implementation of languages, by exposing meaningful information concerning an aspect of a language (e.g., syntactical constructs) and for a specific purpose (e.g., composition, reuse or coordination) in an appropriate formalism. In this regard, language interfaces can be thought of as a reasoning layer on top of language implementations. The definition of language interfaces relies on proper formalisms for expressing different kinds of interfaces and binding relations between language implementations and interfaces. Using language interfaces, one can vary or evolve the implementation of a language while preserving tools and analyses defined over its interface. Language interfaces also facilitate the modular definition of languages by enabling the description of required and provided interfaces of a language (or language component). Syntactical or semantical composition operators can then be defined upon these interfaces. Languages interfaces may be crafted manually or automatically inferred from an implementation.

Model types [START_REF] Steel | On model typing[END_REF] are an illustration of such kind of interfaces. Model types are interfaces on the abstract syntax of a language (defined by a metamodel). Models are linked to model types by a typing relation. Most importantly, model types are linked one to another by subtyping relations, providing model polymorphism, i.e., the ability to manipulate a model through different interfaces. Model polymorphism enables the definition of generic tools that can be applied to any model matching the interface on which they are defined, regardless of the concrete implementation of their language. Model types can also be used to filter the information exposed from the abstract syntax of a language. Doing so, they can define language viewpoints by extracting the appropriate information on a system for one specific development task of a stakeholder. Model types are supported by a model-oriented type system that leverages family polymorphism [START_REF] Ernst | Family polymorphism[END_REF] and structural typing to abstract the conformance relation standing between models and metamodels with a typing relation between models and model types.

We incorporated these concepts into Melange [START_REF]The Melange Language[END_REF], a new language for DSLs designers and users. Melange is a language-based, model-oriented programming language in which DSLs designers can manipulate languages definitions with high-level operators (e.g., inheritance, composition, slicing) and express their relations through the definition of metamodels, language interfaces, and transformations. Melange provides DSLs users with an action language where models are first-class typed citizens and embeds a model-oriented type system that natively provides model polymorphism through model typing. We applied Melange on two industrial use cases to maximize the reuse of DSLs ecosystems: managing syntactic and semantic variation points in a family of FSM languages; providing an executable extension of Capella [1], a large-scale system engineering modeling language.


Robin Cooper

Jonathan Ginzburg

Negative inquisitiveness and alternatives-based negation

Keywords: interrogatives, negation, dialogue, type theory

 where negative propositions are identified in terms of the syntactic or morphological properties of the sentences which introduce them.

Introduction

In the classical formal semantics treatments for questions the denotation of a positive polar interrogative (PPInt) p? is identical to that of the corresponding negative polar (NPInt) ¬p? [15, 14, for example]. This is because the two interrogatives have identical exhaustive answerhood conditions. Indeed [START_REF] Groenendijk | Questions[END_REF], p. 1089 argue that this identification is fundamental.

However, other evidence calls the identification of PPInt and NPInt denotations into question. (1a,b) based on examples due to [START_REF] Hoepelmann | On questions[END_REF] seems to describe distinct cognitive states. Hoepelmann, in arguing for this distinction, suggests that (1a) is appropriate for a person recently introduced to the odd/even distinction, whereas (1b) is appropriate in a context where, say, the opaque remarks of a mathematician sow doubt on the previously well-established belief that two is even. (1c,d) seem to describe distinct investigations, the first by someone potentially even handed, whereas the second by someone tending towards DSK's innocence.

(1) a. The child wonders whether 2 is even.

b. The child wonders whether 2 isn't even. c. Epstein is investigating whether DSK should be exonerated. d. Epstein is investigating whether DSK shouldn't be exonerated.

That root PPInts and NPInts are appropriate in distinct contexts is well recognized in the literature since Hoepelmann and [START_REF] Ladd | A first look at the semantics and pragmatics of negative questions and tag questions[END_REF]. However, it is not merely the background that differs, it is also the responses triggered that are markedly and systematically different. A corpus study of the British National Corpus, whose results are displayed in Table 1, reveals that the two types of interrogatives exhibit almost a mirror image distribution:3 it suggests that PPInts p? are significantly biassed to eliciting p, whereas NPInts ¬p? are almost identically biassed to eliciting ¬p: [START_REF] Ginzburg | Interrogative Investigations: the form, meaning and use of English Interrogatives[END_REF], who developed a view of questions as propositional abstracts, showed how such an account, combined with a theory of negative situation types developed in [START_REF] Cooper | Austinian propositions, Davidsonian events and perception complements[END_REF], can distinguish between PPInts and NPInts denotations and presuppositions while capturing the identity of resolving answerhood conditions. Their account relied on a complex ad hoc notion of simultaneous abstraction. In this paper we consider a number of phenomena relating negation and dialogue, on the basis of which we develop an account of propositional negation in the framework of Type Theory with Records (TTR) [START_REF] Cooper | Records and record types in semantic theory[END_REF][START_REF] Cooper | Type theory and semantics in flux[END_REF]. This account extends the earlier results in a type theoretic framework, based on standard notions of negation and abstraction. An important part of the analysis is that we distinguish semantically between positive and negative propositions. This is possible because our type theory is intensional and we have a more fine-grained notion of proposition developed from the conception of propositions as types than, for example, the notion of proposition in inquisitive semantics which is developed from the conception of propositions as sets of possible worlds. Part of our argument for making the distinction between positive and negative propositions is based on data which Farkas and Roelofsen [START_REF] Farkas | Polarity particles in an inquisitive discourse model (ms)[END_REF] analyze in terms of inquisitive semantics where they rely on syntactic features of utterances in order to distinguish those propositions which are to count as negative. From (2a,b,c) one can extract some fundamental requirements for a theory of negation in dialogue. In (2a(1)) B's initial action provides the background for A's initial utterance of 'No', in which A ultimately expresses a wish for the negative situation type ¬StickIn(B,nail,socket). More generally, we argue that this type of use ('Neg(ative)Vol(itional) 'No') involves the specification of a negative situation/event type, thereby providing motivation for (3a). Additional motivation for this is provided by complements of naked infinitive clauses discussed below and the large body of work on the processing of negation, reviewed recently in [START_REF] Kaup | What psycholinguistic negation research tells us about the nature of the working memory representations utilized in language comprehension[END_REF]. Kaup offers experimental evidence that comprehending a negative sentence (e.g. Sam is not wearing a hat) involves simulating a scene consistent with the negated sentence. She suggests that indeed initially subjects simulate an "unnegated" scene (e.g. involving Sam wearing a hat). [START_REF] Tian | Why we simulate negated information: A dynamic pragmatic account[END_REF] offer additional evidence supporting the simulationist perspective. However, they argue against the "two step" view of negation (viz. unnegated and then negated), in favour of a view driven by dialogical coherence, based on QUD.

In the aftermath of (2a(1)), (2a(2)) would be a reasonable question to ask, whereas (2a(2')) would be grounds for summoning the social services. This, together with our earlier discussion on PPInts and NPInts motivates (3b). Assuming (2a(2)) were uttered, B's response asserts the negation of the proposition p Want(B,electr(B)) . A can now agree with B by uttering 'No'. That is, 'propositional' No always resolves to a negative proposition. This partly motivates (3c). Additional motivation for this is the existence in many languages, such as French and Georgian, of dialogue particles which presuppose respectively a positive (negative) polar question as the maximal element in QUD (MaxQUD), as in (2c).

In (2b(2)) B retorts with ¬p 1 (p 1 = Threaten(Merkel,Papandreou)), whereas in (2b(3)) A disagrees with B and affirms ¬¬p 1 . Clearly, we need (2b(3)) to imply p 1 , but this should not be identified with p 1 -C's utterance (2b(4)) can be understood as agreement with A, not with B, hence motivating (3d).

(3)

Informal intuitive desiderata for a theory of positive and negative situation types and propositions a. Negative situation types evoke precluding positive types: If a situation s is of a negative type ¬T , then s is of some positive type T which precludes T , that is no situation can be both of type T and T b. Positive/negative polar question distinction: If p is a proposition and p? is the question whether p then p? should query whether p is true; The question derived from the corresponding negative proposition ¬p, ¬p?, should query whether ¬p is true; these questions are distinct though have equivalent resolving answerhood conditions. c. Negative propositions: negative propositions are recognizably distinct from positive propositions. d. Equivalence but non-identity of p and ¬¬p: The propositions p and ¬¬p should be distinct but nevertheless truthconditionally equivalent.

In the following we will attempt to characterize a system which meets these informal criteria and makes the notions involved more precise.

Negation and types

Our discussion builds on what Luo [START_REF] Luo | Contextual Analysis of Word Meanings in Type-Theoretical Semantics[END_REF] calls "modern" type theory and what we call rich type theory, since it presents a much larger selection of types than the simple type theory used, for example, by Montague. Central to type theory is the notion of a judgement that objects are of certain types. The judgement that a is of type T is written in symbols as a : T . We will also express this by saying that a is a witness for T . Ranta [START_REF] Ranta | Type-Theoretical Grammar[END_REF] suggested that non-mathematical declarative sentences in natural language correspond to types of Davidsonian events. This idea has been taken up and elaborated in [START_REF] Cooper | Austinian truth, attitudes and type theory[END_REF] and elsewhere where the term situation . The discussion here builds on the type theoretical dictum: "propositions as types". The idea is that we can consider propositions to be types of situations (possibly among other things). If a type has at least one witness it corresponds to a true proposition. A type with no witnesses corresponds to a false proposition.

In [START_REF] Cooper | Negation in dialogue[END_REF] we considered various options for treating negation in TTR considering negation as complement in possible worlds, intuitionistic negation, classical negation as a variant of intuitionistic negation, infonic negation, and negation in simulation semantics.

In our version of intuitionistic negation the negation of type T is viewed as the type of functions (T → ⊥) where ⊥ is a necessarily empty type. In terms of TTR we say that {a | a : ⊥} = ∅ no matter what is assigned to the basic types, thus giving ⊥ a modal character: it is not only empty but necessarily empty. If T is a type then ¬T is the function type (T → ⊥). This works as follows: if T is a type corresponding to a proposition it is "true" just in case there is something of type T (i.e. a witness or proof) and "false" just in case there is nothing of type T . Now suppose there is a function of type ¬T . If there is something a of type T then a function f of type ¬T would have to be such that f (a) : ⊥. But ⊥, as we know, is empty. Therefore there cannot be any function of type ¬T . The only way there can be a function of type ¬T is if T itself is empty. Then there can be a function which returns an object of type ⊥ for any object of type T , since, T being empty, it will never be required to return anything. This gives us a notion of negative type, that is a function type whose range type is ⊥, which can be made distinct from positive types (which could be anything other than a negative type, though in practice we use record types as the basis for our propositions). In this way we fulfil (3) by making negative types distinct from non-negative types. However, the proposals made in [START_REF] Cooper | Negation in dialogue[END_REF] did not yet give us a type of negative propositions. The problem is that for any type T there are infinitely many corresponding negative types (T → ⊥), ((T → ⊥) → ⊥) and so on. All of these are types and therefore, if we allow a type Type of types4 they will all be of type Type. Things become a little more complicated when we want to talk of some particular collection of types closed under negation as we do below. If T is a type of types then we shall use cl ¬ (T ) to represent the type of types whose witnesses are the closure of the set of witnesses of T under ¬. We shall also use map ¬ (T ) to represent the type T such that ¬T : T iff T : T . This gets us a type whose witnesses involve one iteration of negation over the types belonging to T , leaving out the types we started with, that is, a type of negative types.

Given this, and following [START_REF] Ginzburg | The Interactive Stance: Meaning for Conversation[END_REF], we introduce situation semantics style Austinian propositions into TTR [START_REF] Cooper | Austinian truth, attitudes and type theory[END_REF][START_REF] Cooper | Type theory and semantics in flux[END_REF]. These are objects of type (4a). (4a) is a record type, that is a set of pairs consisting of a label (represented to the left of the colon) and a type (represented to the right of the colon). 5 An object is of a record type if it is a record containing fields with the same labels as in the record type with objects in those fields which are of the types specified in the record type. An example of an Austinian proposition of this type would be (4b). Here RecType is the type of record types as defined in [START_REF] Cooper | Austinian truth, attitudes and type theory[END_REF][START_REF] Cooper | Type theory and semantics in flux[END_REF] and 'run(sam)' is the type of situation in which 'sam' runs. Denoting (4a) by AusProp, the type of Austinian propositions, we can say that NegAusProp, the type of negative Austinian propositions, is (4c). Truth for these Austinian propositions involves a notion of Austinian witness which in turn involves a notion of incompatible types. Two types T 1 and T 2 are incompatible just in case for any a not both a : T 1 and a : T 2 , no matter what assignments are made to basic types. Incompatibility thus means that there is necessarily no overlap in the set of witnesses for the two types. In order to be fully viable incompatibility needs to be further restricted using a notion of alternativehood [4]. In some cases what the alternatives amount to is fairly straightforward and even lexicalized-classifying the table as not black requires evidence that it is green or brown or blue, say. But in general, figuring out the alternatives, as Cohen illustrates, is of course itself context dependent, relating to QUD (Questions Under Discussion).

Using the notion of "model" defined in [START_REF] Cooper | Type theory and semantics in flux[END_REF], that is, an assignment of objects to basic types and to basic situation types constructed from a predicate and appropriate arguments, we can characterize the set of witnesses for a type T with respect to "model" M , [ˇT ] M , to be {a | a : M T } where the notation a : M T means that a is a witness for type T according to assignment M . We can then say that two types T 1 and T 2 are incompatible if and only if for all M ,

[ˇT 1 ] M ∩ [ˇT 2 ] M = ∅.
We define a notion of Austinian witness for record types closed under negation:

(5) a. If T is a record type, then s is an Austinian witness for T iff s : T b. If T is a record type, then s is an Austinian witness for ¬T iff s : T for some T incompatible with T c. If T is a type ¬¬T then s is an Austinian witness for T iff s is an Austinian witness for T

The intuitions behind clauses (5b) and (5c) are based on the intuitive account of intuitionistic negation. (5b) is based on the fact that a way to show that s being of type T would lead to a contradiction is to show that s belongs to a type that is incompatible with T . (5c) is based on the fact that if you want to show that a function of type (T → ⊥) would lead to a contradiction requires finding a witness for T . We say that an Austinian proposition p is true iff p.sit is an Austinian witness for p.sit-type. Notice that if p is true in this sense then p.sit-type will be nonempty, that is, "true" in the standard type-theoretical sense for propositions as types. If p is an Austinian proposition as in (6a), then the negation of p, ¬p, is (6b): [START_REF] Cooper | Austinian truth, attitudes and type theory[END_REF] a. sit = s sit-type = T b. sit = s sit-type = ¬T

We obtain the desideratum (3) in virtue of the requirement involving an incompatible type in (5b). We obtain the desideratum (3) because negative propositions are distinct from positive propositions. We obtain (3) because double negations of propositions will be distinct from the original proposition but they will now (contrary to intuitionistic propositions) be truth-conditionally equivalent (that is, an Austinian proposition will be true just in case its double negation is true in virtue of (5c)).

Negation and inquisitiveness

In unpublished work Farkas and Roelofsen [START_REF] Farkas | Polarity particles in an inquisitive discourse model (ms)[END_REF] and Brasoveanu, Farkas and Roelofsen [START_REF] Brasoveanu | Polarity particles and the anatomy of n-words (ms)[END_REF] deal with a range of examples related to (2c). In order to treat these examples it is important to distinguish between negative and positive assertions and questions. One initial problem that arises is that questions correspond to sets of sets of possible worlds corresponding to the positive possibility and the negative possibility and thus the positive and negative questions are not distinguished. In order to solve this they make use of highlighting as introduced in [START_REF] Roelofsen | Disjunctive questions, intonation, and highlighting[END_REF]. According to this view compositional semantics introduces something more like what we might represent as a record structure, that is, an interpretation which is divided into two components: a highlighted proposal and a set of possibilities. However, there is still a remaining problem of determining which of the highlighted possibilities are negative. As they point out sets of possible worlds do not distinguish between negative and positive propositions. For example, they discuss John failed the exam and John did not pass the exam as corresponding to the same set of possible worlds. For us these would be two distinct propositions. In order to make the distinction between positive and negative propositions, they use the syntax of the sentences which introduce them. This we see as problematic. As we point out in [START_REF] Cooper | Negation in dialogue[END_REF], languages use various ways of expressing negation. In addition to standard negative particles, languages have a variety of ways of expressing negation and we run the risk of listing an arbitrary set of morphemes or constructions if we cannot characterize semantically the fact that they engender negative propositions.

Alternatives

It is widely recognized that positive Naked Infinitive (NI) sentences describe an agent's perception of a situation/event, one which satisfies the descriptive conditions provided by the NI clause, as in (7a,b). More tricky is the need to capture the 'constructive' nature of negation in negative NI sentences such as (7c,d). These reports mean that s actually possesses information which rules out the descriptive condition (e.g. for (7c) Mary avoiding contact with Bill), rather than simply lacking concrete evidence for this (e.g. Ralph shutting his eyes.). As [START_REF] Cooper | Austinian propositions, Davidsonian events and perception complements[END_REF] points out, Davidsonian accounts (e.g. [START_REF] Higginbotham | The logic of perceptual reports: An extensional alternative to situation semantics[END_REF]), are limited to the far weaker (7f): [5] provides axioms on negative SOAs (infons) in situation semantics that attempt to capture this, as in (8a,b). (8a) states that if a situation s supports the dual of σ, then s also supports positive information that precludes σ being the case. (8b) tells us that if a situation s supports the dual of σ, then s also supports information that defeasibly entails that σ is the case. (5) accounts for (8a). In order to cover (8b) we could refine [START_REF] Cooper | Austinian propositions, Davidsonian events and perception complements[END_REF] as in [START_REF] Cooper | Negation in dialogue[END_REF].

(

) 9
If T is a record type, then s is an Austinian witness for ¬T iff s : T for some T incompatible with T and there is some T such that s : T and if any situation is of type T this creates the expectation that it is also of type T .

We do not at this point have a precise proposal for treating the notion "creates the expectation". However it is done, it would mean that T (the type that is incompatible with T ) is regarded as an alternative for T given T . One way of handling these defeasible inferences is in terms of Aristotelian enthymemes as discussed in [START_REF] Breitholtz | Enthymemes as rhetorical resources[END_REF]. We regard these as being resources available to agents either in particular limited types of contexts or as part of their general knowledge. For example, Fillmore's [11] examples [START_REF] Farkas | Polarity particles in an inquisitive discourse model (ms)[END_REF] We generally assume that people have teeth but not walnut shells and that humans have one nose but many toes. Such resources may also be local to a restricted domain or even a single dialogue or even part of a dialogue. So, for example, a previous turn in a dialogue is sufficient to create an association between husbands and walnut shells, thus making (10b) acceptable.

(11) A: My husband keeps walnut shells in the bedroom. B: Millie's lucky in that respect. Her husband doesn't have any walnut shells.

This particular resource is quite likely not going to be used beyond this particular dialogue.

Polar Interrogatives

We are left with the desideratum (3). We follow [START_REF] Ginzburg | Interrogative Investigations: the form, meaning and use of English Interrogatives[END_REF] in analyzing polar questions as 0-ary propositional abstracts. We rely on a standard type theoretic notion of abstraction, couched in terms of functional types. For instance, (2a(2)) and (2a(2')) would be assigned the 0-ary abstracts in (12a) and (12b) respectively. These are distinct functions from records of type [] (in other words from all records) into the corresponding Austinian propositions, which do not depend on the particular record chosen as an argument (that is, r does not occur in the notation for the resulting type). This use of vacuous abstraction corresponds to the proposal in [START_REF] Ginzburg | Interrogative Investigations: the form, meaning and use of English Interrogatives[END_REF] to treat polar questions as vacuous abstracts and whquestions as non-vacuous abstracts. This accords with the need to distinguish the distribution of their expected responses and the information states of questioners asking or agents investigating the corresponding issues: Given the witnessing conditions introduced in ( 9), wondering about the question λr:Rec ( sit = s sit-type = ¬T ) involves wondering about whether s has the characteristics that typically involve T being the case, but which-nonetheless, in this case-fail to bring about T . The simple answerhood relation of [START_REF] Ginzburg | Interrogative Investigations: the form, meaning and use of English Interrogatives[END_REF] recast in TTR will ensure that the exhaustive answer to p? are {p, ¬p}, whereas to ¬p? they are {¬p, ¬¬p}, so the exhaustive answers are equivalent, as needed. (Recall that p and ¬¬p are required to be truth-conditionally equivalent by (5c)). 6

Conclusion

We have proposed that positive and negative questions are semantically distinct and, in order to achieve this, that there is a corresponding semantic distinction between positive and negative propositions. On our analysis, for a negative proposition ¬p to be true, an alternative must be true, a proposition which precludes the truth of p. Distinguishing positive and negative propositions means that we can have a semantic account of the use of polarity particles in dialogue without relying on syntactic properties to characterize negative propositions. While positive and negative questions are distinct, we are still able to characterize an equivalence between their exhaustive answers.

  Rec sit-type : cl ¬ (RecType) b. sit = s sit-type = c run :run(sam) c. sit : Rec sit-type : cl ¬ (map ¬ (RecType))

( 7 )

 7 a. Ralph saw Mary serve Bill. b. Saw(R,s) ∧ s : Serve(m,b). c. Ralph saw Mary not serve Bill. d. Ralph saw Mary not pay her bill. e. Saw(R,s) ∧ s : ¬ Serve(m,b). f. Saw(R,s) ∧ s : Serve(m, b)

( 8 )

 8 a. ∀s, σ[s : σ implies ∃(Pos)ψ[s : ψ and ψ ⇒ σ]] b. ∀s, σ[s : σ implies ∃(Pos)ψ[s : ψ and ψ > σ]]

Table 1 :

 1 Distribution of responses to Positive ('Did..?')/Negative ('Didn't..?') polar interrogatives in the British National Corpus Question type Positive answer Negative answer No answer Total

	Positive polar 53%	31%	16%	n = 106
	Negative polar 23%	54%	22%	n = 86

  , uttered out of context, depend on such general knowledge. (10) a. Her father doesn't have any teeth. b. # Her husband doesn't have any walnut shells. c. Your drawing of the teacher has no nose/#noses. d. The statue's left foot has no #toe/toes.

Our pilot corpus study searched the BNC using SCoRE[START_REF] Purver | SCoRE: A Tool for Searching the BNC[END_REF]. For the NPInts the sample reported below consists of all the NPInts of the form 'Didn't . . . ?' that we found. For the PPInts we found 1500 hits of the form 'Did . . . ?'. From these we selected a random sample of 106. The 'no answer' category includes cases where either no response concerning the question was forthcoming or where it was difficult to understand how the information provided resolved the question.

We can do this if we are careful to avoid paradoxes, for example by stratifying the types as we do in[START_REF] Cooper | Austinian truth, attitudes and type theory[END_REF][START_REF] Cooper | Type theory and semantics in flux[END_REF].

The types may be dependent. See[START_REF] Cooper | Austinian truth, attitudes and type theory[END_REF][START_REF] Cooper | Type theory and semantics in flux[END_REF] for details.

In an extended version of this paper, we address the issue of how to accommodate an additional understanding/reading NPInts manifest, one that has been known since[START_REF] Ladd | A first look at the semantics and pragmatics of negative questions and tag questions[END_REF] as the outside negation reading, in which there is actually a positive bias to the question. We find the arguments of[START_REF] Reese | The meaning and use of negative polar interrogatives[END_REF] that such a reading has a metalinguistic nature convincing, though we do not adopt his proposal that such utterances are complex assertion/query speech acts.

Acknowledgements This research was supported in part by VR project 2009-1569, Semantic analysis of interaction and coordination in dialogue (SAICD) and by a visiting professorship to Cooper at the LabEx Empirical Foundations of Linguistics, Paris Sorbonne Cité. Some portions of this paper were presented at SemDial 2011 in Los Angeles. We thank the audience there as well as the reviewers for Amsterdam Colloquium for their comments.


Jonathan Ginzburg

Raquel Fernández

David Schlangen

On the Semantics and Pragmatics of Dysfluency

Keywords: repair, dysfluencies, dialogue semantics

). In this paper, we present a detailed formal account which: (a) unifies dysfluencies (self-repair) with Clarification Requests (CRs), without conflating them, (b) offers a precise explication of the roles of all key components of a dysfluency, including editing phrases and filled pauses, (c) accounts for the possibility of self-addressed questions in a dysfluency.

Introduction

Although dysfluent speech is pervasive in spoken conversation, dysfluencies have received little attention within formal theories of dialogue. The majority of work on dysfluent language has come from psycholinguistic models of speech production and comprehension (e.g. [START_REF] Levelt | Monitoring and self-repair in speech[END_REF][START_REF] Clark | Using uh and um in spontaneous speech[END_REF][START_REF] Bailey | The processing of filled pause disfluencies in the visual world[END_REF]) and from structural approaches designed to improve performance in speech applications (e.g. [START_REF] Shriberg | Preliminaries to a theory of speech disfluencies[END_REF][START_REF] Heeman | Speech repairs, intonational phrases and discourse markers: Modeling speakers' utterances in spoken dialogue[END_REF]).

Recent psycholinguistic studies have shown that both the simple fact that a dysfluency is occuring and its content can have immediate discourse effectslisteners interpret disfluent speech immediately and make use of the information it provides. E.g., [START_REF] Bailey | The processing of filled pause disfluencies in the visual world[END_REF] found that "filled pauses may inform the resolution of whatever ambiguity is most salient in a given situation", and [START_REF] Brennan | How listeners compensate for disfluencies in spontaneous speech[END_REF] found that in a situation with two possible referents (yellow v. purple square), the fact that a description was self-corrected (e.g. 'yel-uh purple square') enabled listeners to draw the conclusion that the respective other referent ('purple square') was the correct one, before the correction was fully executed. Moreover, dysfluencies yield information: (1-a) entails (1-b) and defeasibly (1-c), which in certain settings (e.g. legal), given sufficient data, can be useful. In this paper, we present a detailed formal account within the framework KoS [7, 6, for example] which: (a) unifies dysfluencies (self-repair) with Clarification Requests (CRs), without conflating them, (b) offers a precise explication of the roles of all key components of a dysfluency, including editing phrases and filled pauses, (c) accounts for the possibility of self-addressed questions in a dysfluency.

We start with background on dysfluencies and on KoS. We then describe in turn our accounts of the two main types of dysfluencies, and end with brief conclusions.

Background

Dysfluencies: structure and taxonomy

As has often been noted (see e.g. [START_REF] Levelt | Monitoring and self-repair in speech[END_REF], and references therein for earlier work), speech dysfluencies follow a fairly regular pattern. The elements of this pattern are shown in Figure 1, annotated with the labels introduced by [START_REF] Shriberg | Preliminaries to a theory of speech disfluencies[END_REF] (who was building on [START_REF] Levelt | Monitoring and self-repair in speech[END_REF]). Of these elements, all but the moment of interruption and the continuation are optional.

We partition the dysfluencies in two: (i) backward-looking dysfluencies (BLDs), as in (2-a,b)-the moment of interruption, which need not be followed by an explicit editing phrase, is followed by an alteration that refers back to an already uttered reparandum; (ii) forward-looking dysfluencies (FLDs), as in (2-c), where the moment of interruption is followed by a completion of the utterance which is delayed by a filled or unfilled pause (hesitation) or a repetition of a previously uttered part of the utterance (repetition).

(2) a. Flights to Boston I mean to Denver. [START_REF] Shriberg | Preliminaries to a theory of speech disfluencies[END_REF] b.

Have you seen Mark's erm earphones? Headphones. (British National Corpus, file KP0, l. 369-370) c. Show flights arriving in uh Boston. [START_REF] Shriberg | Preliminaries to a theory of speech disfluencies[END_REF]

Dialogue GameBoards

We start by providing background on the dialogue framework we use here, namely KoS (see e.g. [START_REF] Ginzburg | Computational models of dialogue[END_REF][START_REF] Ginzburg | The Interactive Stance: Meaning for Conversation[END_REF]). On the approach developed in KoS, there is actually no single context-instead of a single context, analysis is formulated at a level of information states, one per conversational participant. The dialogue gameboard represents information that arises from publicized interactions. Its structure is given in ((3))-the spkr,addr fields allow one to track turn ownership, Facts represents conversationally shared assumptions, Pending and Moves represent respectively moves that are in the process of/have been grounded, QUD tracks the questions currently under discussion:

4 (3) DGBType = def             
             
The basic units of change are mappings between dialogue gameboards that specify how one gameboard configuration can be modified into another on the basis of dialogue moves. We call a mapping between DGB types a conversational rule. The types specifying its domain and its range we dub, respectively, the preconditions and the effects, both of which are supertypes of DGBType.

Examples of such rules, needed to analyze querying and assertion interaction are given in [START_REF] Fernández | Non-Sentential Utterances in Dialogue: Classification, Resolution and Use[END_REF]. Rule (4-a) says that given a question q and ASK(A,B,q) being the LatestMove, one can update QUD with q as QUD-maximal. QSPEC is what characterizes the contextual background of reactive queries and assertions. (4-b) says that if q is QUD-maximal, then subsequent to this either conversational 4 We also note one fairly minor technical modification to the DGB field QUD, motivated in detail in [START_REF] Fernández | Non-Sentential Utterances in Dialogue: Classification, Resolution and Use[END_REF][START_REF] Ginzburg | The Interactive Stance: Meaning for Conversation[END_REF], assuming one wishes to exploit QUD to specify the resolution of non-sentential utterances such as short answers, sluicing, and various other fragments. QUD tracks not simply questions qua semantic objects, but pairs of entities: a question and an antecedent sub-utterance. This latter entity provides a partial specification of the focal (sub)utterance, and hence it is dubbed the focus establishing constituent (FEC) (cf. parallel element in higher order unification-based approaches to ellipsis resolution e.g. [START_REF] Gardent | Computing parallelism in discourse[END_REF].) Thus, the FEC in the QUD associated with a wh-query will be the wh-phrase utterance, the FEC in the QUD emerging from a quantificational utterance will be the QNP utterance, whereas the FEC in a QUD accommodated in a clarification context will be the sub-utterance under clarification. Hence the type of QUD is InfoStruc, as defined in (i):

(i) Info-struc = » q : Questn fec : set(LocProp)

participant may make a move constrained to be q-specific (i.e. either About or Influencing q). 5

(4) a. Ask QUD-incrementation       pre : I : InfoStruc LatestMove = Ask(spkr,addr,I.q) : IllocProp effects : qud = I.q,pre.qud : poset(InfoStruc)

      b. QSpec           pre : qud = i, I : poset(InfoStruc) effects : TurnUnderspec ∧ merge      r : AbSemObj R: IllocRel LatestMove = R(spkr,addr,r) : IllocProp c1 : Qspecific(r,i.q)               

Grounding and Clarification

Given a setup with DGBs as just described and associated update rules, distributed among the conversationalists, it is relatively straightforward to provide a unified explication of grounding conditions and the potential for Clarification Requests (or CRification). in the immediate aftermath of a speech event u, Pending gets updated with a record of the form of ( 5) of type locutionary proposition (LocProp). Here T u is a grammatical type for classifying u that emerges during the process of parsing u. The relationship between u and T u -describable in terms of the proposition p u given in (5)-can be utilized in providing an analysis of grounding/CRification conditions:

(5)

p u = sit = u sit-type = T u (6)
a. Grounding: p u is true: the utterance type fully classifies the utterance token. b. CRification: p u is false, either because T u is weak (e.g. incomplete word recognition) or because u is incompletely specified (e.g. incomplete contextual resolution-problems with reference resolution or sense disambiguation). 5 We notate the underspecification of the turn holder as'TurnUnderspec',, an abbreviation for the following specification which gets unified together with the rest of the rule: 2 We concentrate here on explicating the coherence of possible CRs. In the aftermath of an utterance u a variety of questions concerning u and definable from u and its grammatical type become available to the addressee of the utterance. These questions regulate the subject matter and ellipsis potential of CRs concerning u and generally have a short lifespan in context. To take one example, the non-sentential CRs in (7-a) and (7-b) are interpretable as in the parenthesized readings. This provides justification for the assumption that the context that emerges in clarification interaction involves the accommodation of an issue-one that for A's utterance in [START_REF] Ginzburg | Computational models of dialogue[END_REF], assuming the sub-utterance 'Bo' is at issue, could be paraphrased as (7-c). The accommodation of this issue into QUD could be taken to license any utterances that are co-propositional with this issue, where CoPropositionality is the relation between utterances defined in [START_REF] Heeman | Speech repairs, intonational phrases and discourse markers: Modeling speakers' utterances in spoken dialogue[END_REF]. 6 In other words, either a CR which differs from MaxQud at most in terms of its domain, or a correction-a proposition that instantiates MaxQud. (8) CoPropositionality a. Two utterances u 0 and u 1 are co-propositional iff the questions q 0 and q 1 they contribute to QUD are co-propositional.

(i) qud-contrib(m0.cont) is m0.cont if m0.cont : Question (ii) qud-contrib(m0.cont) is ?m0.cont if m0.cont : Prop (iii) q 0 and q 1 are co-propositional if there exists a record r such that q 0 (r) = q 1 (r).

Repetition and meaning-oriented CRs can be specified by means of a uniform class of conversational rules, dubbed Clarification Context Update Rules (CCURs) in ( [START_REF] Ginzburg | The Interactive Stance: Meaning for Conversation[END_REF]). Each CCUR specifies an accommodated MaxQUD built up from a sub-utterance u1 of the target utterance, the maximal element of Pending (MaxPending). Common to all CCURs is a license to follow up MaxPending with an utterance which is co-propositional with MaxQud. ( 9) is a simplified formulation of one CCUR, Parameter identification, which allows B to raise the issue about A's sub-utterance u0: what did A mean by u0? (9) underpins CRs such as those in [START_REF] Ginzburg | Computational models of dialogue[END_REF]. [START_REF] Kempson | Dynamic Syntax: The Flow of Language Understanding[END_REF] Parameter identification:

              pre :    Spkr : Ind MaxPending : LocProp u0 ∈ MaxPending.sit.constits    effects :       MaxQUD = q = λxMean(A,u0,x) fec = u0 : InfoStruc LatestMove : LocProp c1: CoProp(LatestMove.cont,MaxQUD.q)                    
3 From CRs to Dysfluency: Informal Sketch

We argue that dysfluencies can and should be subsumed within a similar account, a point that goes back to [START_REF] Schegloff | The preference for self-correction in the organization of repair in conversation[END_REF]: in both cases (i) material is presented publicly, (ii) a problem with some of the material is detected and signalled (= there is a 'moment of interruption'); (iii) the problem is addressed and repaired, leaving (iv) the incriminated material with a special status, but within the discourse context. Concretely for dysfluencies-as the utterance unfolds incrementally questions can be pushed on to QUD about what has happened so far (e.g. what did the speaker mean with sub-utterance u1?) or what is still to come (e.g. what word does the speaker mean to utter after sub-utterance u2?).

By making this assumption we obtain a number of pleasing consequences. We can:

-explain similarities to other-corrections: the same mechanism is at work, differentiated only by the QUDs that get accommodated. -explain internal coherence of dysfluencies: '#I was a little bit + swimming' is an odd dysfluency, it can never mean 'I was swimming' in the way that 'I was a little bit + actually, quite a bit shocked by that' means 'I was quite a bit shocked by that'. Why coherence? Because 'swimming' is not a good answer to 'What did I mean to say when I said 'a little bit' ?'.

-appropriateness changes implicate that original use unreasonable:

examples like (10) involve quantity implicatures. These can be explicated based on reasoning such as the following: I could have said (reperandum), but on reflection I said (alteration), which differs only in filtering away the requisite entailment.

(10) it's basically (the f-+ a front) leg [implicature: no unique front leg]

Dysfluency Rules

As we have seen, there are various benefits that arrive by integrating CRs and dysfluencies within one explanatory framework. In order to do this we need to extend pending to incorporate utterances that are in progress, and hence, incompletely specified semantically and phonologically. Conceptually this is a natural step to make. Formally and methodologically this is a rather big step, as it presupposes the use of a grammar which can associate types word by word (or minimally constituent by constituent), as e.g. in Categorial Grammar [START_REF] Steedman | The Syntactic Process[END_REF] and Dynamic Syntax [START_REF] Kempson | Dynamic Syntax: The Flow of Language Understanding[END_REF]. It raises a variety of issues with which we cannot deal in the current paper: monotonicity, nature of incremental denotations, etc. For our current purposes, the decisions we need to make can be stated independently of the specific grammatical formalism used. The main assumptions we are forced to make concern where pending instantiation and contextual instantiation occur, and more generally, the testing of the fit between the speech events and the types assigned to them. We assume that this takes place incrementally. For concreteness we will assume further that this takes place word by word, though examples like [START_REF] Levelt | Speaking: From intention to articulation[END_REF], which demonstrate the existence of word-internal monitoring, show that this is occasionally an overly strong assumption. [START_REF] Levelt | Speaking: From intention to articulation[END_REF] From [START_REF] Levelt | Speaking: From intention to articulation[END_REF] We can go straight on to the ye-, to the orange node.

BLDs are handled by the update rule in (( 12)). This indicates that if u0 is a sub-utterance of the maximally-pending utterance, QUD may be updated so that the issue is 'what did A mean by u0', whereas the FEC is u0, and the follow up utterance needs to be be co-propositional with MaxQud: [START_REF] Purver | The Theory and Use of Clarification in Dialogue[END_REF] Backwards looking appropriateness repair:

                           pre :         
spkr : Ind addr : Ind pending = p0,rest : list(LocProp) u0 : LocProp c1: member(u0, p0.sit.constits)

         effects :              MaxQUD =
q = λx Mean(pre.spkr,pre.u0,x) fec = u0 : Consider now [START_REF] Schegloff | The preference for self-correction in the organization of repair in conversation[END_REF]. This differs from (2-a) in one significant way-a different editing phrase is used, namely 'no', which has distinct properties from 'I mean'. [START_REF] Schegloff | The preference for self-correction in the organization of repair in conversation[END_REF] From [START_REF] Levelt | Speaking: From intention to articulation[END_REF]: From yellow down to brown -no -that's red.

InfoStruc LatestMove : LocProp c2: Copropositional(LatestMove content , MaxQUD)                                         Given (( 12
Whereas 'I mean' is naturally viewed as a syntactic constituent of the alteration, 'no' cannot be so analyzed. Arguably the most parsimonious analysis7 involves assimilating this use to uses such as: This use of 'no' involves the expression of a negative attitude towards an event and would, in particular, allow 'no' to be used to express a negative attitude towards an unintended utterance event. We could analyze [START_REF] Schegloff | The preference for self-correction in the organization of repair in conversation[END_REF] as involving the utterance 'brown'. Following this, the rule ( 12) is triggered with the specification QUD.q = what did A mean by FEC? and the FEC = 'brown.' The analysis then proceeds like the earlier cases. We specify FLDs with the update rule in (15)-given a context where the Lat-estMove is a forward looking editing phrase by A, the next speaker-underspecified between the current one and the addressee-may address the issue of what A intended to say next by providing a co-propositional utterance:8 [START_REF] Steedman | The Syntactic Process[END_REF] Forward Looking Utterance rule: [START_REF] Steedman | The Syntactic Process[END_REF] differs from its BLD analogue, then, in two ways: first, in leaving the turn underspecified and second, by the fact that the preconditions involves the LatestMove having as its content what we describe as an FLDEdit move, which we elucidate somewhat shortly. Words like 'uh', 'thee' will be assumed to have such a force, hence the utterance of such a word is a prerequisite for an FLD. To make this explicit, we assume that 'uh' could be analyzed by means of the lexical entry in ( 16):

                           preconds :          
                                   (
(16)                   phon : uh cat = interjection : syncat dgb-params :         
          cont = c1 : FLDEdit(spkr,addr,MaxPending) : Prop                  
We demonstrate how to analyze (17):

(17) From [START_REF] Shriberg | Preliminaries to a theory of speech disfluencies[END_REF]: A: Show flights arriving in uh Boston.

After A utters u0= 'in', she interjects 'uh', thereby expressing FLDEdit(A,B,'in'). This triggers the Forward Looking Utterance rule with MaxQUD.q = λx Mean-NextUtt(A,'in',x) and FEC = 'in'. 'Boston' can then be interpreted as answering this question, with resolution based on the short answer rule. Similar analyses can be provided for (18). Here instead of 'uh' we have a lengthened version of 'a', which expresses an FLDEdit moves:

(18)

From [START_REF] Levelt | Speaking: From intention to articulation[END_REF] This opens the way for a more 'pragmatic' account of FLDs, which we will sketch here, one in which (15) could be derived rather than stipulated. Once a word is uttered that introduces FLDEdit(A,B,u0) into the context, in other words has an import like (20), this leads to a context akin to ones like (21), that license inter alia elliptical constructions like sluicing and anaphora: Indeed a nice consequence of [START_REF] Steedman | The Syntactic Process[END_REF], whether we view it as basic or derived, is that it offers the potential to explain cases like (22) where in the aftermath of a filled pause an issue along the lines of the one we have posited as the effect of the conversational rule ((15)) actually gets uttered: On our account such utterances are licensed because these questions are copropositional with the issue 'what did A mean to say after u0'. Such examples also highlight another feature of KoS's dialogue semantics: the fact that a speaker can straightforwardly answer their own question, indeed in these cases the speaker is the "addressee" of the query. Such cases get handled easily in KoS because turn taking is abstracted away from querying: the conversational rule QSpec, introduced earlier as (4-b), allows either conversationalist to take the turn given the QUD-maximality of q.

Concluding Comment Finally, the account we provide has a strong methodological import: editing phrases like 'no' and 'I mean' select inter alia for speech events that include the discompetent products of performance. This means that the latter are also integrated within the realm of semantic competence.

Fig. 1 .

 1 Fig. 1. General pattern of self-repair

  Bo leaving? a. B: Bo? (= Who do you mean 'Bo' ?) b. B: Who? (= Who do you mean 'Bo' ?) c. Who do you mean 'Bo' ? d. B: You mean Mo.

  )), (2a,b) can be analyzed as follows: in (2-a) the alteration 'I mean to Denver' provides a direct answer to the issue what did A mean with the utterance 'to Boston'; in (2-b) we analyze 'headphones' as a bare fragment ('short answer') which gets the reading 'I mean headphones' given the QUD-maximality of the issue what did A mean with the utterance 'earphones'.

  A opens freezer to discover smashed beer bottle] A: No! ('I do not want this (the beer bottle smashing) to happen') b. [Little Billie approaches socket holding nail] Parent: No Billie ('I do not want this (Billie putting the nail in the socket) to happen')

  c1: member(u0, p0.sit.constits) LatestMove content = FLDEdit(spkr,u0) : IllocProp MeanNextUtt(pre.spkr,pre.u0,x) fec = u0 : InfoStruc LatestMove : LocProp c2: Copropositional(LatestMove content ,MaxQUD)

  : A woman phoned. introduces issue: 'who is the woman that phoned'. b. A: Max drank some wine. introduces issue: 'what wine did Max drink' .

  133 Well it's (pause) it's (pause) er (pause) what's his name? Bernard Matthews' turkey roast. (BNC, KBJ) b. Here we are in this place, what's its name? Australia. c. They're pretty ... um, how can I describe the Finns? They're quite an unusual crowd actually. http://www.guardian.co.uk/sport/ 2010/sep/10/small-talk-steve-backley-interview

  : A vertical line to a-to a black disk.

	Let us return to consider what the predicate 'FLDEdit' amounts to from a
	semantic point of view. Intuitively, (19) should be understood as 'A wants to
	say something to B after u0, but is having difficulty (so this will take a bit of
	time)':
	(19)	FLDEdit(A,B,u0)
	This means we could unpack (19) in a number of ways, most obviously by making
	explicit the utterance-to-be-produced u1, representing this roughly as in (20):
	(20)	∃u1[After(u1,u0) ∧ Want(A,Utter(A,B,u1))]

CoPropositionality for two questions means that, modulo their domain, the questions involve similar answers. For instance 'Whether Bo left', 'Who left', and 'Which student left' (assuming Bo is a student) are all co-propositional.

An extended version of this paper considers and rejects resolution based on a contextually available polar question or proposition.

[START_REF] Heeman | Speech repairs, intonational phrases and discourse markers: Modeling speakers' utterances in spoken dialogue[END_REF] This rule is inspired in part by Purver's rule for fillers, (91),

p. 92, ([START_REF] Purver | The Theory and Use of Clarification in Dialogue[END_REF]).

Acknowledgements Raquel Fernández acknowledges support from NWO (MEER-VOUD grant 632.002.001). David Schlangen acknowledges support from DFG (Emmy Noether Programme) Some portions of this paper were presented at Constraints in Discourse 2011 in Agay. We thank the audience there as well as the reviewers for Amsterdam Colloquium for their comments.