Laura Kallmeyer

Rainer Osswald

Sylvain Pogodalla

For-Adverbials and Aspectual Interpretation: An LTAG Analysis Using Hybrid Logic and Frame Semantics

Keywords: aspectual coercion, for-adverbials, iteration, Frame Semantics, Hybrid Logic, Lexicalized Tree Adjoining Grammar

In this paper, we propose to use Hybrid Logic (HL) as a means to combine frame-based lexical semantics with quantification. We integrate this into a syntax-semantics interface using LTAG (Lexicalized Tree Adjoining Grammar) and show that this architecture allows a fine-grained description of event structures by quantifying, for instance, over subevents. As a case study, we provide an analysis of for-adverbials and the aspectual interpretations they induce. The basic idea is that for-adverbials introduce a universal quantification over subevents that are characterized by the predication contributed by the verb. Depending on whether these subevents are bounded or not, the resulting overall event is then an iteration or a progression. We show that by combining the HL approach with standard techniques of underspecification and by using HL to formulate general constraints on event frames, we can account for the aspectual coercion triggered by these adverbials. Furthermore, by pairing this with syntactic building blocks in LTAG, we provide a working syntax-semantics interface for these phenomena.

Introduction

For-Adverbials and Aspectual Reinterpretation

An important topic for theories of aspectual composition and coercion is the interaction of lexical aspect (Aktionsart) and temporal adverbials. On the one hand, in-and for-adverbials have been used since Vendler ( : f) as indicators for distinguishing between activities and accomplishments. On the other hand, there are many types of sentences in which a temporal adverbial is not compatible with the lexical aspect of the verb but which have nevertheless a regular interpretation (see, e.g., Egg

). For example, while in ( a), the verb cry denotes an activity and is thus immediately compatible with the for-adverbial, the verb cough in ( b) is semelfactive, that is, it denotes a punctual event, and, hence, calls for additional adjustments in order to be compatible with for-adverbials.

( ) a. Peter cried for ten minutes. b. Peter coughed for ten minutes.

In the case of ( b), the adjustment consists in interpreting the sentence as describing a sequence or iteration of coughings.

The semantic composition of for-adverbials with atelic predicates such as sit, cry or swim can be modeled straightforwardly by letting the foradverbial assign a certain time span to the denoted state, process or activity. Punctual and telic predicates (semelfactives, achievements, accomplishments), on the other hand, do not satisfy the sortal requirements of for-adverbials and, hence, need to undergo aspectual coercion when combined with such adverbials. Dölling (

) presents an elaborate approach along these lines, which provides various coercion mechanisms for turning telic predicates into atelic ones, including the iteration coercion and the habitual coercion. For example, the iterative coercion is realized by means of the following second-order term (cf. Dölling : ):

( ) λPλe[∀e (is-constituent-of(e , e) → P(e ))]

When applied to P, the resulting predicate denotes events whose constituents satisfy P. Dölling's model requires the constituents to be temporally adjacent in order to constitute a process or activity. This assumption has the consequence that semelfactives (cough, knock, jump), which are analyzed as moments without duration, need to get "stretched" to episodes before the iteration coercion can apply. That is, iterative interpretations of semelfactives require a two-step coercion in Dölling's approach. While Dölling does not say much about the impact of coercion on cognitive processing costs, the approaches of Deo & Piñango (

) and Cham-pollion ( ) aim at being more predictive in this respect. Deo & Piñango suggest that the main processing issue for an iterative interpretation of a telic predicate, when combined with a for-adverbial, lies in the identification of a contextually determined regular temporal partition of the specified time span. For instance, the iterative interpretation of the sentence in ( ) depends on a regular partitioning of the three months into reasonably small subintervals, each of which is associated with an event of John biking to the office.

( )

John biked to the office for three months.

Deo & Piñango do not assume that iterative readings of for-adverbial constructions depend on telic or atelic properties of the event description. In fact, they explicitly deny the need for inserting a coercion operator for the interpretation of expressions like ( ) and ( b). However, the logical representation proposed by Deo & Piñango does not differ so much from Dölling's coercion operator in ( ), except that they quantify over subintervals instead of event constituents. The crucial point is that for Deo & Piñango, the quantification is already introduced by the for-adverbial, irrespective of the type of predicate it applies to. Deo & Piñango distinguish between iterative and continuous readings of for-adverbials, where a continuous reading requires an atelic predicate as in ( a). They assume that iterative readings call for a contextually determined partition of the time interval while continuous readings go along with a context-independent "infinitesimal" partition. Under this analysis, iterative readings do indeed not depend on the telicity or atelicity of the predicate. Continuous interpretations, however, are apparently sensitive to the aspectual properties of the verb since they are licensed by atelic predicates only. Champollion ( ) takes up this issue and provides further evidence for the fact that the missing aspectual sensitivity in Deo & Piñango's approach leads to undesired consequences. Champollion tries to remedy these problems by the following modifictions: first, he postulates a silent iteration operator, which means "once or repeatedly," that turns semelfactive and telic predicates into atelic ones. Second, he assumes that for-adverbials introduce a vague but contextindependent partition short(I) I of the specified temporal interval I into reasonably short subintervals The meaning of an adverbial like for three months is then represented as follows (cf. Champollion : ):

( ) λPλI[months(I) = 3 ∧ AT(P, I) ∧ ∀J[J ∈ short(I) I → AT(P, J)]]
Here, AT(P, I) roughly means that P holds at I, which in the case of event predicates comes down to saying that there is an event of type P whose runtime is I. Since ( ) requires P to hold at the whole interval and at each cell of the partition, it follows that P is not quantized (in the sense of Krifka ); hence, it is not telic. That is, for-adverbials select atelic predicates according to Champollion's analysis, which is the reason for applying the iteration operator in the case of telic predicates. Note that the partition of the interval can be coarser than the decomposition of the iteration into elementary events; repetitions may occur within a single cell of the partition. If, for example, I is an interval of minutes and short(I) I consists of cells of seconds then ( b) is true if one or more coughings of Peter occur within each of the second cells (under the above assumption that the silent iteration operator has been applied to the semelfactive predicate).

The described analysis is problematic for examples like ( ) since the partition short(I) I is independent of the context. In ( ), it is not clear whether John biked to the office every day, twice a day, every second day, every week, or according to another schedule. It can thus happen that there is no biking of John to the office in some of the cells of short(I) I . This is why the partition operator of Deo & Piñango has a contextual parameter. Champollion ( : ) also postulates a separate, contextdependent partition operator, but only for situations where the reference of indefinites covaries with the cells of the partition, as in example ( a).

( )

a. We built a huge snowman in our front yard for several years. b. She bounced a ball for twenty minutes.

Zucchi & White ( ) and Kratzer ( ), among others, observed that for-adverbials tend to take narrow semantic scope with respect to the quantifiers in their syntactic scope. This implies a non-covarying interpretation of indefinites as in ( b). The narrow-scope covariation of the indefinite in the preferred reading of ( a) is thus an exception that calls for an explanation. Champollion's suggestion to put the burden at least partly on an additional, contextually specified partition seems problematic since the contextual parameter is already required for examples like ( ), as mentioned above.

Goals and Outline

In this paper, we present a revised analysis of for-adverbials and develop a formal model of their compositional integration at the syntax-semantics interface. The proposed semantic representation combines several aspects of the approaches discussed in the previous section: like Dölling, we directly refer to event components instead of temporal subintervals. Similar to Deo & Piñango and Champollion, we assume that the universal quantification over event components is already contributed by the for-adverbial. Like Dölling and Deo & Piñango, we do not postulate an iteration operator. Like Dölling and Champollion, we take into account the aspectual sensitivity of for-adverbials. The semantic representations used in this paper are motivated by frame-semantic considerations and will be formalized in the language of Hybrid Logic (HL). This language allows us to express constraints over event types and to quantify over event components. The syntax-semantics interface is modelled within the framework of Lexicalized Tree Adjoining Grammar (LTAG) combined with underspecification on the level of HL formulas.

The structure of the paper is as follows. Section introduces HL as a language for describing frame structures. Section describes the architecture used for modelling the syntax-semantics interface. Within this framework, section develops then an analysis of for-adverbials in combination with a uniform treatment of iteration and progression, along the lines sketched above. Section concludes.

Semantic Frames and Hybrid Logic

Semantic Frames

Frames emerged as a representation format of conceptual and lexical knowledge (Fillmore , Barsalou , Löbner ). They are commonly presented as semantic graphs with labelled nodes and edges, as in figure , where nodes correspond to entities (individuals, events, . . . ) and edges to (functional or non-functional) relations between these entities. In figure all relations except part-of are meant to be functional. This representation offers a fine-grained and systematic decomposition of meaning that goes beyond what is usually represented in FrameNet frames (Osswald & Van Valin Jr. ). Frames can be formalized as extended typed feature structures (Petersen , Kallmeyer & Osswald ) and specified as models of a suitable logical language, the labelled attribute-value description language (LAVD language). Such a language allows for the composition of lexical frames on the sentential level by means of an explicit syntax-semantics interface (Kallmeyer & Osswald ). Yet, this logical framework does not provide means for the lexical items to introduce explicit quantification.

As Blackburn ( ) points out, attribute-value structures can also be described using another logical language: Hybrid Logic (HL, see Areces & ten Cate

), an extension of the language of modal logic, well-suited to the description of graph structures like the one of figure . HL introduces nominals, that is, node names, that allow the logical formulas to refer to specific nodes of the graph. The nominal n 0 for instance refers to the locomotion node in figure . It is then possible, for example, to specify that the and the edges from the node n 0 should meet on the same node in figure . This additional expressiveness of HL over modal logic allows one to express node sharing in attribute-value structures (Blackburn ). HL is an established logical formalism which has been extensively studied, in particular with respect to the addition of variables for nodes, and the associated quantifiers, that can appear in the logical formulas. Its relation to attribute-value structures and its expressiveness make it a natural candidate to relate quantified expressions and frame semantics. Compared to Kallmeyer & Osswald , the approach we propose here does not consider frames as "genuine semantic representations." The oneto-one equivalence between the logical formulas of the LAVD language of Kallmeyer & Osswald (

) and the frames as graph (or relational) structures relies on the existence of minimal models for such formulas. While HL with nominals, but without variables and binders, is very close to the LAVD language, it is not obvious what the notion of minimal model becomes when using quantification. Thus, we have a more traditional view where the sense of an expression is a hybrid logical formula and its reference is computed against models. The latter are the frames we wish to consider. But, contrary to what happens with minimal models, they are then not fully specified by the logical formulas which serve as frame descriptions.

Frame Description with Hybrid Logic

Before giving the formal definition of Hybrid Logic as used in this paper, let us illustrate the different possibilities HL offers to express properties of frames. Consider the model 1 given in figure . In this model, we have edges labeled with functional relations ( , etc.) and one edge labeled with a non-functional relation, part-of, indicated by lowercase. (Note that HL formulas do not distinguish between the two types of edge labels. That is, functionality has to be enforced by additional contraints.) As in standard modal logic, we can talk about propositions holding at single nodes. This allows for specifying types, in the Frame Semantics sense, assigned to single nodes as proposition. For instance, in 1 , the formula region is true at the two nodes in the bottom-right corner but false at all other nodes of 1 . Furthermore, we can talk about the existence of an attribute for a node. This corresponds to stating there exists an edge originating at this node using the Q modality in modal logic. In frames, there may be several relations, hence several modalities, denoted by 〈R〉 where R is the name of the relation. For example, 〈 〉man is true in 1 at the locomotion node n 0 because there is an edge from n 0 to some other node where man holds. But it is false at all other nodes. Finally, we can have conjunction, disjunction, and negation of these formulas. For example, locomotion ∧ 〈 〉walking ∧ 〈 〉〈 〉 is also true at the locomotion node n 0 . HL extends this with the possibility to name nodes in order to refer back to them without following a specific path, and with quantification over nodes. Let us exemplify this again with formulas evaluated with respect to 1 . In the following, we use a set of nominals, that is, of node names, and a set of node variables. n 0 is such a nominal, the node assigned to it is the locomotion node in 1 . x, y, ... are node variables. The truth of a formula is given with respect to a specific node w of a model , an assignment V from nominals to nodes in the model and an assignment g which maps variables to nodes in .

There are different ways to state existential quantifications in HL, for instance, Eφ and ∃x.φ. Eφ is true at w if there exists a node w in at which φ holds. In other words, we move to some node w in the frame and there φ is true. Ehouse is, for instance, true at any node in 1 . As usual, we define

A φ ≡ ¬ E(¬φ). Then A (path → 〈 〉 ) holds at any node in 1 .
In contrast to Eφ, ∃x.φ is true at w if there is a w such that φ is true at w under an assignment g x w which maps x to w . In other words, there is a node that we name x but for the evaluation of φ, we do not move to that node. For example, the formula ∃x.〈 〉〈 〉〈part-of 〉(x ∧ region) ∧ E(house ∧ 〈 -〉x) is true at the locomotion node in 1 . Besides quantification, HL also allows us to use nominals or variables to refer to nodes: @ n φ specifies the moving to the node w denoted by n before evaluating φ. n can be either a nominal or a variable. The ↓ operator allows us to assign the current node to a variable: ↓ x.φ is true at w if φ is true at w under the assignment g x w . That is, we call the node we are located at x, and, under this assignment, φ is true at that node. For example, 〈 〉〈

〉〈part-of 〉(↓ x.region ∧ E(house ∧ 〈 - 〉x)
) is true at the locomotion node in 1 .

By employing this logic, we can characterize the frame of figure by the formula ( ). More precisely, any model that satisfies formula ( ) can is the proposition that is true at any node. So 〈 〉〈 〉 is true at a node if we can reach from it some node following first a edge then a edge.

be unified with the frame of figure at node n 0 .

( )

@ n 0 locomotion ∧ (∃x.〈 〉(x ∧ man) ∧ 〈 〉x) ∧ 〈 〉walking ∧ (∃x.〈 〉(path ∧ 〈 〉(region ∧ 〈part-of 〉(x ∧ region))) ∧ E(house ∧ 〈 - 〉x))
Alternatively, as shown in ( ), we can use the ↓ operator instead of the two ∃ operators since we know how to reach the two nodes we want to refer to several times. The first time we talk about them, we give them some name via the ↓ operator and this allows to refer to them again at some later point.

( ) @ n 0 locomotion ∧ 〈 〉(↓ x.man ∧ @ n 0 〈 〉x) ∧ 〈 〉walking ∧ 〈 〉(path ∧ 〈 〉(region ∧ 〈part-of 〉(↓ x.region ∧ E(house ∧ 〈 - 〉x))))
As can be seen from this example, HL allows us to express path equations (see the 〈 〉 and 〈 〉 attributes of n 0 ). However, the way these path equations are expressed is rather tedious compared to other feature logics. Therefore we define

〈 1 1 〉 . . . 〈 1 k 〉 . = 〈 2 1 〉 . . . 〈 2 l 〉 ≡ ∃x(〈 1 1 〉 . . . 〈 1 k 〉x ∧ 〈 2 1 〉 . . . 〈 2 l 〉x)
Using this notation, the HL characterization of 1 is ( ).

( )

@ n 0 locomotion ∧ 〈 〉 . = 〈 〉 ∧ 〈 〉man ∧ 〈 〉walking ∧ 〈 〉(path ∧ 〈 〉(region ∧ 〈part-of 〉(↓ x.region ∧ E(house ∧ 〈 - 〉x))))

Hybrid Logic

We slightly adapt the notations of Areces & ten Cate ( ).

Definition (Formulas

Forms ::= | p | n | ¬φ | φ 1 ∧ φ 2 | 〈R〉φ | Eφ | @ n φ | ↓ x.φ | ∃x.φ where p ∈ Type, n ∈ Node, x ∈ Nvar, R ∈ Rel and φ, φ 1 , φ 2 ∈ Forms.
Moreover, we define:

• A φ ≡ ¬ E¬φ • [R]φ ≡ ¬〈R〉¬φ • φ → ψ ≡ ¬φ ∨ ψ • 〈 1 1 〉 . . . 〈 1 k 〉 . = 〈 2 1 〉 . . . 〈 2 l 〉 ≡ ∃x(〈 1 1 〉 . . . 〈 1 k 〉x ∧ 〈 2 1 〉 . . . 〈 2 l 〉x) We call
A and [R] universal operators, and E and 〈R〉 existential operators. The elements of Func will be written in small caps.

Definition (Model, assignment)

A model = 〈M , (R ) R∈Rel , V 〉 is a triple such that
. M is a non-empty set, . each R is a binary relation on M , and . the valuation

V : Type ∪ Nom -→ ℘(M ) is such that if i ∈ Nom then V (i) is a singleton.
An assignment g is a mapping g : Nvar -→ M . For an assignment g, g x m is an assignment that differs from g at most on x and g x m (x) = m. For n ∈ Node, we also define [n] ,g to be the only m such that

V (n) = {m} if n ∈ Nom and [n] ,g = g(n) if n ∈ Nvar.
As can be seen from these definitions, nominals are, on the one hand, similar to variables since they allow us to access nodes via the @ operator, and on the other hand, they are similar to propositions, that is, to types, except that they are special propositions that hold only at a single node. Now we can define satisfaction of a formula at a specific node in a model, given some assignment g.

Definition (Satisfaction)

. Let be a model, w ∈ M , and g an assignment for . The satisfaction relation of a formula φ by the model , with the assignment g at the node w ( , g, w φ) is defined as follows:

, g, w , g, w p iff w ∈ V (p) for p ∈ Type , g, w n iff w = [n] ,g for n ∈ Node , g, w @ n φ iff , g, [n] ,g φ for n ∈ Node , g, w ¬φ iff , g, w φ , g, w ↓ x.φ iff , g x w , w φ , g, w φ 1 ∧ φ 2 iff , g, w φ 1 and , g, w φ 2 , g, w ∃x.φ iff ∃w , g x w , w φ , g, w 〈R〉φ iff ∃w R (w, w ) and , g, w φ , g, w Eφ iff ∃w , g, w φ
. A formula φ is:

• satisfiable if there is a model , and assignment g on , and a node w ∈ M such that , g, w φ; • globally true in a model under an assignment g, that is, , g, w φ for all w ∈ M . We write , g φ.

With these definitions, we also obtain

, g, w A φ iff ∀w , g, w φ 2.

Expressive Power

According to the satisfaction relation definition, ↓ and ∃ bind node variables without changing the current evaluation node. In addition to E,

Blackburn & Seligman ( ) introduce another quantifier Σ for which the satisfaction relation also changes the evaluation node:

, g, w Σx.φ iff ∃w , g x w , w φ

Blackburn & Seligman (

) call E the somewhere operator, and write it Q, and A

is the universal modality, written P.

This defines two independent families of operators: ↓ and ∃, and E and Σ. However, using any operators of both families (for instance ↓ and E, the "weakest" ones) is expressively equivalent to using the most expressive fragment of the hybrid languages (the full hybrid language). It is usual to refer to the hybrid languages (θ 1 , . . . , θ n ) as the extension of the modal language with nominals and the operators θ 1 , . . . , θ n ∈ {↓, @, E, ∃}. It is worth noting that even using the simplest binder ↓already causes the satisfiability problem for (↓) to be undecidable (Areces et al.

). There are, however, syntactic restrictions on formulas that make the satisfiability problem decidable. In particular, formulas of the full hybrid language that do not contain the pattern "universal operator scoping over a ↓ operator scoping over a universal operator" have a decidable satisfiability problem (ten Cate & Franceschet ). All of the formulas we build in our account of iteration and progression in combination with foradverbial avoid this pattern. This might not be the case in the general use of HL for quantification by Kallmeyer et al. ( ) in sentences such as every politician in every city. . . However, for every hybrid language, testing a given formula against a given model remains decidable (Franceschet & de Rijke ).

3 The Syntax-Semantics Interface for LTAG and HL

Introduction to LTAG

A Lexicalized Tree Adjoining Grammar (LTAG; Joshi & Schabes , Abeillé & Rambow

) consists of a finite set of elementary trees. Larger trees can be derived via the composition operations substitution (replacing a leaf with a tree) and adjunction (replacing an internal node with a tree). An adjoining tree has a unique non-terminal leaf that is its foot node (marked with an asterisk). When adjoining such a tree to some node n, in the resulting tree, the subtree with root n from the original tree ends up below the foot node. A sample LTAG derivation is given in figure . The subject and object NP slots in the ate tree are replaced with the Peter and pizza trees respectively (substitution) and the always tree adjoins at the VP node of the ate tree.

Note that ↓ can be defined in terms of ∃ by ↓ x.φ ≡ ∃x.x ∧ φ and that E can be defined in terms of Σ by Eφ ≡ Σz.φ with z not occurring in φ. Peter S VP [ = ] V to come

NP

NP [ = ] VP [ = ] VP * V [ = [ =sg, = ]] wants Y S VP[ = [ =sg, = ]] VP V to come V[ = ]
wants

NP[ = ]
Peter Figure 3 LTAG derivation with feature structures

In order to capture syntactic generalizations, the non-terminal node labels are enriched with feature structures (Vijay-Shanker & Joshi

). Each node has a top and a bottom feature structure (except substitution nodes, which have only a top). Nodes in the same elementary tree can share features. Substitutions and adjunctions trigger unifications: in a substitution step, the top of the root of the substituted tree unifies with the top of the substitution node. In an adjunction step, the top of the root of the adjoining tree unifies with the top of the adjunction site and the bottom of the foot of the adjoining tree unifies with the bottom of the adjunction site. Furthermore, in the final derived tree, top and bottom must unify in all nodes. Figure provides an example (top feature structures are superscripts and bottom feature structures are subscripts). The feature of the V node of wants is passed to the root of the auxiliary tree. Then, by adjunction and subsequent top-bottom unification on the highest VP node, its value unifies with in the to come tree and thereby gets passed to the subject node. By substitution and subsequent top-bottom unification at the NP slot, it unifies then with the feature at the root of the Peter tree. The tree on the right is the one we obtain after derivation and top-bottom unification on all nodes.

The Syntax-Semantics Interface

Our architecture for the interface between TAG syntax and frame semantics builds on previous approaches which pair each elementary tree with a semantic representation that consists of a set of formulas, in this case, HL formulas. An example is given in figure . We use interface features on the syntactic nodes that are responsible for triggering semantic composition via the feature unifications during substitution and adjunction. These features are, for instance, (for "individual") and (for "event"). Their values can be nominals or variables from the HL formula linked to the elementary tree they occur in. If their values are not yet known, we can use a boxed number as a variable and indicate structure sharing via this variable. These boxed numbers can also occur in the HL formulas. Once a value is assigned to them via syntactic composition, their occurrence in the HL formula is also replaced with this value. This unification-based assignment is the only mechanism for semantic composition.

NP [ =i]

John

@ i person ∧〈 〉John S VP NP [ = ] V ate NP [ = ] eating ∧〈 〉 ∧〈 〉 NP [ =j]
pizza @ j pizza The example in figure is rather simple. The elementary tree of ate and its associated HL formula tell us that the nominal or variable of the node is contributed by whatever is substituted at the subject node while the node will be further specified by the object NP. Both NP trees contain a nominal and contribute this nominal via the interface feature. Substitution and final top-bottom unification unify [ = ] with [ =i] and [ = ] with [ =j]. As a consequence, i is assigned to and j to and we obtain a collection of three HL formulas, eating ∧ 〈 〉i ∧ 〈 〉 j, @ i person∧〈 〉John and @ j pizza. These are then interpreted conjunctively.

Underspecified Representations

In figure , the boxed variables in the HL formulas act like holes that are replaced with concrete formulas (here, the two nominals) once the syntax-triggered unifications are performed. In general, we want to be able to insert also other formulas into these holes, not just variables and nominals. Therefore, we introduce the possibility to label HL formulas, using labels l 0 , l 1 , etc. A label is the name of a unique HL formula. But it does not, as in the case of nominals, denote a single element in the frame; the formula can hold at several frame nodes. Using these labels as values in our interface features, we can insert these formulas in larger formulas via composition. Besides these labels, we also introduce the possibility to express dominance constraints of the form i ¡ * x where x is either a boxed variable (= a hole) or a label. The relation ¡ * is the dominance relation in the syntactic tree of the HL formula i occurs in, that is, it expresses a relation "is subformula of" on the HL formulas. This extension is an application of well-known underspecification techniques, in particular hole semantics (Bos ). Similar proposals for LTAG semantics but with standard predicate logic and not with frames and HL have already been made by Gardent ). As a basic example, consider the derivation given in figure . The every tree adjoins to the root of the dog tree and the derived tree substitutes into the subject slot of the barked tree. The interface feature determines the minimal scope for attaching quantifiers, and the feature stands for the event/predication contributed at a specific node. The syntactic unifications lead to = x, = l 2 , = l 1 . As a result of these equations, we

NP [ =x, = ] NP * [ = ] Det every A (↓ x. → ), ¡ * , ¡ * NP [ = l 2 ] N dog l 2 : dog S VP V barked NP [ = , = l 1 ] l 1 : E(barking ∧〈 〉 )
Figure 5 Derivation of every dog barked obtain the following underspecified representation:

( )

A (↓ x. → ), l 2 : dog, l 1 : E(barking ∧ 〈 〉x), ¡ * l 2 , ¡ * l 1
The representation in ( ) has a unique solution (i.e., a unique fully specified HL formula that satisfies the constraints in ( )) given by the mapping → l 2 , → l 1 , which leads to ( ).

( )

A (↓ x. dog → E(barking ∧ 〈 〉x))
Obviously, this way to underspecify subformula relations in the semantic representation allows standard underspecified representations for scope ambiguities.

In the next section, we will see that underspecification via dominance constraints also allows us to account for cases in event semantics where certain characterizations of events are underspecified as to whether they refer to the entire event or to subevents. The particular combination of frame description in HL and underspecification brings sufficient expressive power to (a) allow for a fine-grained event decomposition and for quantification over subevents, and (b) link embedded subevents and the entire event via dominance constraints and thereby enable adverbials to apply in-between. Furthermore, underspecification in the event types, in combination with appropriate HL constraints, allows us to underspecify the type of the event resulting from applying a for-adverbial while making this type dependent on the type of the embedded event.

Besides frame descriptions linked to elementary trees, our grammar also contains general constraints on frames that hold universally and independently of syntax. These constraints can, for instance, describe subtype relations of the form A locomotion → motion; mandatory attributes for certain types, such as A motion → 〈 〉 ; or mandatory path equations for certain types, for example

A locomotion → 〈 〉 . = 〈 〉.

Application to for-Adverbials

For-Adverbials and Atelic Events

We start with a basic case of a for-adverbial modifying an atelic event description:

( ) Peter swam for one hour.

We take swimming to be represented by a frame described by swimming ∧ 〈 〉 . Furthermore, we need an existential quantification over the event such that the semantic representation for Peter swam, for instance, is @ i (person∧〈 〉Peter)∧ E(swimming∧〈 〉i). This existential quantifier does not necessarily immediately embed the event characterization coming from the verb since some adverbial taking this event node into its scope could attach to it. Therefore, we assume a kind of event-internal scope window between the existential quantification and the event node. Figure shows the swam tree with its HL formula. In the formula, there is a hole in the scope of the existential E, and the formula labeled l 2 , which describes the swimming node, has to be below (constraint ¡ * l 2 ). If no adverbial is added, then l 2 gets assigned to .

We assume that swimming is a subtype of the event type progression, which characterizes continuous nonbounded events:

( ) A (swimming → progression) A (progression → nonbounded)
We prefer "nonbounded" over "unbounded" in order to avoid the connotation of limitlessness that comes with the latter term (see also Jackendoff ). For purposes of this paper, we do not distinguish between atelicity and nonboundedness but we are aware that there are good reasons to do so in general (see, for instance, Cappelle & Declerck and the references therein).

NP [ =i]

Peter Following the outline sketched in section . , the meaning of the adverbial for one hour is represented as follows:

@ i person ∧〈 〉Peter S VP [ = l 2 , = ] V swam NP [ = ] l 1 : E , l 2 : swimming ∧〈 〉 , ¡ * l 2 VP PP for one hour VP * [ = , = ] l 4 :↓ e.nonbounded ∧ 〈 〉one-hour ∧ A (〈segment-of 〉e → ), ¡ * l 4
( ) ↓ e.nonbounded ∧ 〈 〉 one-hour ∧ A (〈segment-of 〉e → P)
More precisely, ( ) is paired with an elementary tree, as depicted in the right of figure , and P stands for a hole (in this case, ), which will be filled by the formula associated with the modified VP, here l 2 . We may assume that events of type progression have a sufficently rich subeventual structure that is closed under sum formation. For the present purpose, we only need the property that every progression is an event segment of itself:

( ) A (↓ e.

progression → 〈segment-of 〉e)

As pointed out by an anonymous reviewer, formula ( ) could be expressed equivalently in the following, more compact form by employing the universal modality and the inverse of the relation segment-of :

nonbounded ∧ 〈 〉 one-hour ∧ [segment-of -1 ] P
However, we do not introduce the inversion operator to our logic in this paper. Moreover, this transformation cannot be systematized as it would, for instance, break the compositonality for sentences with multiple quantifiers (Kallmeyer et al. ).

By means of ( ), it follows that in the example under discussion, the whole one-hour event is of type swimming. We will see in the next section that this is different for iterations.

The substitution and adjunction in figure trigger the unifications = , = i, = l 2 on the interface features. As a result, when applying these and collecting the formulas, we obtain the following underspecified semantic formulas:

( ) @ i person ∧ 〈 〉Peter, l 1 : E , l 2 : swimming ∧ 〈 〉i, l 4 :↓ e.nonbounded ∧ 〈 〉one-hour ∧ A (〈segment-of 〉e → l 2 ), ¡ * l 4 , ¡ * l 2
The only possible disambiguation mapping is → l 4 , which yields, with an additional conjunctive interpretation of the set, the formula ( ):

( ) @ i person ∧ 〈 〉Peter ∧ E ↓ e.(nonbounded ∧ 〈 〉one-hour ∧ A (〈segment-of 〉e → swimming ∧ 〈 〉i))
Furthermore, given ( ), swimming ∧ 〈 〉i also holds at e.

Punctual Events and for-Adverbials

Now we consider cases where a for-adverbial combines with a punctual event description. In this case, the event is reinterpreted as an iteration.

( ) Peter knocked at the door for ten minutes.

The meaning of ( ) is that we have an iteration of knocking events, each of them involving Peter as an agent and the same door as a patient, and that the entire iteration goes on for ten minutes:

( ) E(↓ e.iteration ∧ 〈 〉ten-minutes ∧ 〈 〉i ∧ 〈 〉 j ∧ A (〈segment-of 〉e → knocking ∧ 〈 〉i ∧ 〈 〉 j)) ∧ @ i (person ∧ 〈 〉Peter) ∧ @ j door NP [ =i]
Peter

@ i person ∧〈 〉Peter S VP [ = l 2 , = ] PP [ = ] V knocked NP [ = ] l 1 : E , l 2 : knocking ∧〈 〉 ∧〈 〉 , ¡ * l 2 PP [ = j]
at the door Formula ( ), however, leaves several aspects of iterations implicit. Firstly, we need to exclude the possibility that an iteration has no or only one segment. For this reason, following Dölling (

), we assume that iterations consist of at least two segments:

( ) A (↓ e.iteration → E(↓ e 1 .〈segment-of 〉e ∧ E(↓ e 2 .〈segment-of 〉e ∧ ¬@ e 1 e 2 )))
Besides this, the single segments must be distributed over the entire iteration in some regular way. We assume that the specification of what, for a specific type of iteration, "on a regular basis" means, is contextually given. We will not spell this out in this paper. Note that we do not require the segments of an iteration to be adjacent (in contrast to Dölling

). Typically, there are temporal gaps between the segments of an iteration. In particular, events of type progression and iteration are subject to different constraints on how their segments are related to each other.

Iterations, like progressions, are conceived of as nonbounded events and, hence, they satisfy the selectional restrictions of for-adverbials; recall ( ). Furthermore, the following constraints make sure that every event of type nonbounded is either an iteration or a progression and that it cannot be both at the same time:

@ i (person ∧ 〈 〉Peter), @ j door, ¡ * l 2 , ¡ * l 4
The only possible mapping is → l 4 , which leads, with a conjunctive interpretation of the resulting set, to ( ).

( ) E(↓ e.nonbounded ∧ 〈 〉ten-minutes ∧ A (〈segment-of 〉e → knocking ∧ 〈 〉i ∧ 〈 〉 j)) ∧ @ i (person ∧ 〈 〉Peter) ∧ @ j door
We further adopt additional constraints on iterations and progressions concerning the possible types of their segments:

( ) A (〈segment-of 〉iteration → bounded) A (punctual → bounded) A (〈segment-of 〉progression → nonbounded) A (nonbounded → ¬bounded)
Moreover, we have A (knocking → punctual). With these constraints, e in ( ) is necessarily of type iteration since its segments are of type knocking.

The given analysis does not make use of an explicit iteration operator, which is in line with Dölling and Deo & Piñango but in contrast to Champollion (see section ). In the derivation shown in figure , the nonbounded event introduced by the for-adverbial is identified as being of type iteration based on the event type of the modified VP and the constraints listed in ( ) and ( ). Events of type iteration are subject to specific constraints on their inner structure, among which is the constraint stated in ( ).

Bounded Events and for-Adverbials

More interesting though similar cases of bounded events that are iterated are, for example, ( ).

( )

John biked to the office for three months.

Processing such examples seems to be more difficult than processing sentences as ( ). As for the way the for-adverbial combines with the John biked to the office event, we keep the analysis from section . : John biked to the office is a bounded event and, when embedded under the for-adverbial, it is extended to an iteration. The crucial difference from knock in ( ) is that the verb bike itself does not describe a bounded event. Bike without any additional goal specification is an event of type progression. The event boundary in ( ) comes from the additional information provided by the PP to the office. This PP specifies the end of the path of the described movement and thereby delimits the event.

We now no longer want the type progression to be automatically inferred for all motion events of type swimming or biking. Instead, such motion events can become bounded if a goal is added, as formalized by the following constraints:

( ) A (biking → motion) A (motion ∧ 〈 〉 → bounded) A (motion ∧ 〈 〉 → directed-motion) A (directed-motion ∧ ¬〈 〉〈 〉 → nonbounded) A (directed-motion ∧ nonbounded → progression)
The analysis of ( ) in figure is similar to the directed motion analyses proposed in Kallmeyer & Osswald . The elementary tree used for biked in this analysis is the specific tree for the directed motion construction where a directional PP contributes the goal of the movement. In addition to contributing the goal, the PP also specifies some properties of the path, namely that its endpoint lies in the -of the office. Given ( ), the event of type biking in ( ) is also of type bounded and consequently, the application of the for-adverbial triggers the creation of a node of type The underspecified semantic representation we obtain with the derivation in Fig. is given in ( ):

NP [ =i] John @ i person ∧〈 〉John S VP [ = l 2 , = ] PP [ = , = ] V biked NP [ = ] l 1 : E , l 2 : biking ∧ 〈 〉 ∧ 〈 〉 ∧ 〈 〉 , ¡ * l 2 PP [ = j, =l 3 ] to the office @ j office, l 3 : 〈 〉(↓ y.@ j 〈 - 〉
( ) @ i person ∧ 〈 〉John, @ j office,

l 1 : E , l 2 : biking ∧ 〈 〉i ∧ 〈 〉 j ∧ 〈 〉l 3 , l 3 : 〈 〉(↓ y.@ j 〈 - 〉 y) l 4 :↓ e.nonbounded ∧ 〈 〉 -months ∧ A (〈segment-of 〉e → l 2 ), ¡ * l 2 , ¡ * l 4
The only possible disambiguation is → l 4 , which yields, under a conjunctive interpretation, to ( ):

( ) @ i person ∧ 〈 〉John ∧ @ j office, ∧ E ↓ e.nonbounded ∧ 〈 〉 -months ∧ A (〈segment-of 〉e → biking ∧ 〈 〉i ∧ 〈 〉 j ∧ 〈 〉〈 〉(↓ y.@ j 〈 - 〉 y))
Due to the existence of the and the , we can infer that the biking events are in this case bounded directed-motion events. Consequently the entire event has to be an iteration.

Interaction with the Scope of Indefinites

As mentioned in section . , indefinites usually do not take narrow scope with respect to a for-adverbial in the way they can have different scope with respect to other adverbials or quantifiers. In the examples in ( ) (from Kratzer ), the indefinite always scopes over the adverbial.

( ) a. John pushed a cart for an hour. b. I dialed a wrong phone number for five minutes. c. She bounced a ball for minutes.

The following example (taken from Zucchi & White ) shows that in cases where a narrow scope reading would be preferred for plausibility reasons, it is nevertheless not possible if no clue is available from context or world knowledge of how to partition the interval: ( ) ??John found a flea on his dog for a month.

Before discussing our analysis, let us have a look at the proposal in Champollion .

( ) John dialed a wrong phone number for five minutes. The existential ∃x is taken to be part of the P predicate in the semantics of the for-adverbial. Since one part of the for-semantics requires P to hold at the entire interval I, one correctly obtains that there is a single phone number number I that has been dialed repeatedly over the interval I. However, in the predicates that apply to the smaller time intervals J, there is also an existential quantification ∃ y over phone numbers and the dialing here applies to y, not to x. In other words, in every shorter interval J, there has been a repeated dialing of some number number J that is possibly different from number I . This is at least unnecessary and goes against what the meaning of for-adverbials is supposed to capture, namely that the same number has been dialed in each of these smaller time intervals. But without any additional postulate, ( ) does not prevent there to be intervals among the J during which there was no dialing of number I .

NP [ =i] Peter @ i person ∧ 〈 〉Peter S VP [ = l 2 , = ] NP [ = , =l 1 ] V bounced NP [ = , =l 1 ] l 1 : E , l 2 : bouncing ∧ 〈 〉 ∧ 〈 〉 , ¡ * l 2 NP [ =x, = ] NP * [ = ] Det a E(↓ x. ∧ ), ¡ * , ¡ * NP [ = l
Our analysis avoids the second existential quantification in the scope of the universal quantification coming from the for-adverbial. Therefore, each subevent involves the same x as the entire event. Let us explain our analysis with the example ( ).

( )

Peter bounced a ball for ten minutes.

In the analysis of quantifiers in figure , the quantifier gets its minimal scope from some interface feature . According to figure , the value of this feature is the label of the E formula associated with the verbal predicate. If this is adapted to ( ), the prediction is that indefinites have scope over the for-adverbial.

The derivation of ( ) is given in figure . The label l 1 of the E formula introducing the event node is passed to the quantifier as its minimal scope via the interface feature . Due to the unification of interface variables during substitution and adjunction and due to the final top-bottom unification, we obtain the result that (the minimal scope of the indefinite) gets identified with l 1 while the for-adverbial gets embedded under , which is the scope of the E-formula labeled l 1 . In other words, the predicate bounce contributes two scope windows: a scope window for quantifiers with a lower limit given by the feature and a lower scope window inside the event structure, delimited by the feature and the value. For-adverbials target this lower scope window since they modify the internal structure of the event.

As a result, we obtain the underspecified HL formula in ( ):

( ) @ i person ∧ 〈 〉Peter, This analysis correctly predicts that a quantifier can have narrow scope with respect to a second quantifier since both target the same scope window. However, they both have to scope over a for-adverbial.

( ) Every boy bounced a ball for ten minutes.

For ( ), in our analysis, we obtain the underspecified formula in ( ).

( ) l 1 : E , l 2 : bouncing ∧ 〈 〉x ∧ 〈 〉 y, A (↓ x. → ), l 5 : boy, E(↓ y. ∧ ), l 6 : ball, l 4 :↓ e.nonbounded ∧ 〈 〉ten-minutes ∧ A (〈segment-of 〉e → l 2 ), ¡ * l 4 , ¡ * l 5 , ¡ * l 1 , ¡ * l 6 , ¡ * l 1 , ¡ * l 2

The dominance constraints from ( ) are depicted in figure . Here, we can see clearly that the scope window for the two quantifiers where the scope order of the universal and the existential is underspecified is higher than the universal quantification coming from the for-adverbial.

Conclusion

The frame-semantic perspective supports a fine-grained and structured characterization of semantic components. By using Hybrid Logic as a description language, we added quantification to frame semantics while preserving the original object-centered view. We applied this formalism to the analysis of for-adverbials and their interaction with the aspectual properties of the modified verb phrases. Moreover, by allowing underspecified formulas, we integrated our analysis into a fully compositional model of the syntax-semantics interface within the LTAG framework.

In the proposed model, the semantic representation of for-adverbials selects for nonbounded events and comes with a universal quantification over event components. Based on the event type of the modified VP and general semantic constraints on the types of events and their event components, the correct type of the overall phrase (i.e., iteration vs. progression) can be inferred without assuming an additional iteration operator or the like. Finally, we have shown how our model can cope with the specific scopal behavior that for-adverbials show with respective to indefinites.

Figure 1

 1 Figure1Frame compatible with the sense of The man walked to the house (adapted fromKallmeyer & Osswald )

Figure 2

 2 Figure 2 Sample LTAG derivation

Figure 4

 4 Figure 4 HL-based syntax-semantics interface

Figure 6

 6 Figure 6 Derivation for ( )

Figure 7

 7 Figure 7 Derivation for ( )

  of 〉e → l 2 ),

Figure 8

 8 Figure 8 Derivation for ( )

  For ( ), Champollion proposes the representation in ( ). ( ) λI[∃e∃x[number(x) ∧ * dial(e, john, x) ∧ I = τ(e) ∧ minutes(I) = 5 ∧ ∀J[J ∈ short(I) I → ∃e ∃ y[number( y) ∧ * dial(e, john, y) ∧ J = τ(e )]]]]

Figure 9

 9 Figure 9 Derivation for ( )

l 1 :

 1 E , l 2 : bouncing ∧ 〈 〉i ∧ 〈 〉x, E(↓ x. ∧ ), l 5 : ball, l 4 :↓ e.nonbounded ∧ 〈 〉ten-minutes ∧ A (〈segment-of 〉e → l 2 ), ¡ * l 4 , ¡ * l 5 , ¡ * l 1 , ¡ * l 2The only possible disambiguation, → l 5 , → l 1 , → l 4 , yields ( ):( ) @ i person ∧ 〈 〉Peter ∧ E(↓ x.ball ∧ E ↓ e.nonbounded ∧

Figure 10

 10 Figure 10 Dominance constraints from ( )

)

  Let Rel = Func ∪ PropRel be a set of functional and non-functional relation symbols, Type a set of type symbols, Nom a set of nominals (node names), and Nvar a set of node variables, with Node = Nom ∪ Nvar. Formulas are defined as:

Acknowledgments This work was supported by the INRIA sabbatical program and by the CRC "The Structure of Representations in Language, Cognition, and Science" funded by the German Research Foundation (DFG). We would like to thank Timm Lichte and Christian Wurm for fruitful discussions, and Christopher Piñón and an anonymous reviewer for their helpful comments.

, Barcelona, Spain. open archive: hal-01151641. Kallmeyer, Laura & Rainer Osswald. . Syntax-driven semantic frame composition in lexicalized tree adjoining grammars. Journal of Language Modelling ( ).

-. Zucchi, Sandro & Michael White.

. Twigs, sequences and the temporal constitution of predicates. Linguistics and Philosophy ( ).

-.


Amir Aly

Sascha Griffiths

Francesca Stramandinoli

Towards Intelligent Social Robots Current Advances in Cognitive Robotics

Introduction

In 1950, Alan Turing described a now well-known thought experiment, which has now become known as the Turing test [START_REF] Turing | [END_REF]. While this has actually been adopted as a kind of benchmark for systems with some degree of AI, the original idea seems much more to be about the concept of intelligence being what we perceive it to be. If an unbiased human observer cannot tell the difference between human and machine in interaction then that machine can be claimed to posses some degree of intelligence. This puts interaction at the center of artificial intelligence.

However, in the past many approaches have rather taken a disembodied perspective [START_REF] Dautenhahn | Socially intelligent robots: dimensions of human-robot interaction[END_REF]. These had very little to do with interaction. For example, even the early artificial intelligence systems, which followed in close temporal proximity to Turing's work dealt with the problems posed to them by human programmers in isolation. One example was Newell and Simon's "General Problem Solver" [START_REF] Newell | Report on a general problem-solving program[END_REF].

Recent research in cognitive science and its allied disciplines, however, does strongly suggest that human cognition is not only very good at interaction but that interaction is indeed fundamental to human cognition. Culture and sociality may, indeed, create human cognition and intelligence. This finds support by a family of related theories, which puts interaction at the heart of human cognition, such as The Social Brain Hypothesis [START_REF] Dunbar | The social brain: Mind, language, and society in evolutionary perspective[END_REF][START_REF] Shultz | The social brain hypothesis: An evolutionary perspective on the neurobiology of social behaviour, I Know What You Are Thinking: Brain Imaging and Mental Privacy[END_REF] or the Vygotskian Intelligence Hypothesis [START_REF] Moll | Cooperation and human cognition: the Vygotskian intelligence hypothesis[END_REF]. On the other hand, robots become more embedded into the social world that humans live in. Therefore, it seems to be an opportunity to reexamine whether artificial intelligence can actually profit from the social context, in which these new embodied agents will find themselves. Inspired by the cognitive science literature on the link between sociality and cognition, intelligent social robotics, hence, focuses on the social cognition aspect of robotics.

Robots are having an increasing omnipresent role in human social life, this requires them to be able to behave appropriately to the context of interaction and to have an accepted appearance for a human user. The problematic requirements of robot behavior and appearance for creating a successful long term human-robot relationship constitute major challenges in current cognitive science and robotics research. The complementary relationship between cognitive science and robotics defines the aspects of cognitive robotics research, which focuses on endowing robots with advanced cognitive abilities in order to perceive, reason, and behave autonomously in a similar intelligent manner to humans.

The use of robots side by side with cognitive science allows for modeling the hypotheses and theories of cognition so as to provide a clear conceptualization for cognitive functions considering the effect of the environment. This opens the door to several cognitive-social applications that require intelligent embodied robot behaviors, such as: modeling human learning and adaptation cognitive functions, defining the involved mechanisms in language and action development, understanding and generating multimodal affective behaviors, understanding a broad spectrum of cognitive developmental disorders (e.g., autism), and elderly assisted living, in addition to public interactive applications (e.g., receptionist robot, museum tour-guide robot, and tutor robot).

This special issue aims at shedding light on the intersection of cognitive science and robotics from the theoretical and technical aspects, covering the basic research and its applications. The recent advances and the future scope of cognitive robotics including the new methodologies, applied technologies, and robots are principal topics to be addressed in this special issue.

Summary of the special issue

In 1997, Billard and Dautenhahn [START_REF] Dautenhahn | Bringing up robots or-the psychology of socially intelligent robots: From theory to implementation[END_REF] coined the concept of "social robots", which refers to endowing embodied agents with high-level cognitive functions to engage in social interaction with human users. Developing such "intelligent social robots" takes robotics research to the center of artificial intelligence, neuroscience, and cognitive psychology research so as to cross the distance from human intelligence to artificial intelligence, where the fundamental target is to make robots able to perceive, think, and behave humanly and rationally in different contexts of interaction [START_REF] Norvig | Artificial intelligence: A modern approach[END_REF].

During the last decades, researchers in both artificial intelligence and cognitive psychology were involved in developing theories of cognition to serve in building computational models that can make robots able to interact with human users in an intelligent manner, such as: situated and embodied cognition [9,10], perceptual symbol systems [11], and working memory [START_REF] Baddeley | Working memory[END_REF], which could give interesting insights into understanding the way humans think and behave.

The important role that cognitive robotics plays in understanding human cognition lies in providing the ability to examine hypotheses of cognition empirically through mechanistic embodiment for related theories of cognition [START_REF] Mello | Computational modeling/cognitive robotics compliments functional modeling/experimental psychology[END_REF] so as to replicate human-like intelligence. However, to meet this target efficiently, a primary open challenge to address is how to quantify this "human-like intelligence" level. The philosophy of artificial intelligence describes this human-like level of intelligence by the ability of robots to solve the same problems that human users can solve by thinking. However, this raises other open questions, such as how can robots have mental states and consciousness as human users? Yet, a stubbornly elusive challenge for building intelligent social robots is meta-cognition, where robots need to build knowledge about how to employ specific learning strategies according to the situation of interaction and the environment [START_REF] Metcalfe | Metacognition: Knowing about knowing[END_REF]. However, this point is still being considered as a pure artificial intelligence challenge without real applications in robotics. In this special issue, we take a step forward towards bridging between different disciplines of cognitive science and robotics research with the objective of shedding light on current and future challenges in cognitive intelligent robotics.

Tsardoulias et al. [START_REF] Tsardoulias | Towards an integrated robotics architecture for social inclusion -the RAPP paradigm[END_REF] propose the cloud-based RAPP framework that enables robotic devices to deploy robotic applications so as to reduce the computational load on robots. The developed robotic applications according to this paradigm allow social robots to adapt to several situations and scenarios. The study shows the efficacy of the RAPP paradigm in fulfilling the needs of end users as well as for developers.

Choi and Kwak [START_REF] Choi | Who is this?: Identity and presence in robot-mediated communication[END_REF] discuss the concept of identity and presence in robotmediated communication with remote humans. The study shows that both the robot identity level and the number of remote senders had an effect on the presence of the remote sender, in addition to both the telepresence and presence of the robot.

Wiltshire et al. [START_REF] Wiltshire | Enabling robotic social intelligence by engineering human social-cognitive mechanisms[END_REF] take a closer look at social cognition. The authors' aim is to provide a set of recommendations with the aim of facilitating robots to become more capable of complex social interactions. For this purpose, they examine possibilities to facilitate the development of the perception, motor control, and an overall cognitive architecture for artificial cognitive systems of the future.

Parisi et al. [START_REF] Parisi | Emergence of multimodal action representations from neural network self-organization[END_REF] investigate how robust multimodal representations can naturally develop in a self-organizing manner from co-occurring multisensory inputs. They propose a hierarchical learning architecture with growing self-organizing neural networks for learning human actions from audiovisual inputs.

Schadenberg et al. [START_REF] Schadenberg | Personalising game difficulty to keep children motivated to play with a social robot: A Bayesian approach[END_REF] discuss the effect of adapting the difficulty of a child-robot game on the motivation of the child to meet the challenge of the game. They propose a Bayesian-based user modeling module that adjusts the game difficulty to the level of the child's skills. The study shows that the Bayesian-rating module was able to measure the level of the child's performance during the game with the social robot.

Gonzalez et al. [START_REF] Gonzalez | A three-layer planning architecture for the autonomous control of rehabilitation therapies based on social robots[END_REF] introduce their NAOTherapist, a cognitive architecture for automated planning to perform rehabilitation sessions for pediatric patients with upper-limb disorders. These patients suffer from cerebral palsy or obstetric brachial plexus palsy and the NAOTherapist uses three levels of automated planning to assist these patients in the rehabilitation sessions. The social interactive robot takes on the role of a therapist in a hands-off rehabilitation, which allows training by imitation.

Looije et al. [START_REF] Looije | Specifying and testing the design rationale of social robots for behavior change in children[END_REF] present an extension of the situated Cognitive Engineering (sCE) methodology: a formal template that describes the relations between support objectives, behavior change theory, design specifications and evaluation outcomes, called situated Design Rationale (sDR) and the method to obtain this. The sDR supports design of functionalities and evaluation before an experiment and reason about the effects and decisions afterwards.

Biswas and Murray [START_REF] Biswas | The effects of cognitive biases and imperfectness in long-term robot-human companionship: Case studies using five biases on humanoid robot[END_REF] present a number of experiments with robot companions, in which they model human-like cognitive biases. They focus on selected biases: misattribution, empathy gap, Dunning-Kruger, a humorous effect and self-serving bias. These biases are modeled and then tested in interaction with human subjects. The result is a discussion of how these effects of biases, which are prominent features of human cognition, can be useful in human-robot interaction.

Maniadakis et al. [START_REF] Maniadakis | Time-informed task planning in multi-agent collaboration[END_REF] present their work on timing, which is a crucial aspect of human-robot interaction. They integrate a planner into a simulated multi-robot scenario, in which one robot takes on the role of a human and one robot takes on the role usually fulfilled by a robot. Timing is then modeled using a representation of time intervals as fuzzy numbers. The au-thors evaluate their model by showing a collaboration between two simulated robots, in which they prepare a salad collaboratively.

Schneider et al. [START_REF] Schneider | A reusable framework for designing socially assistive robot interactions[END_REF] present a framework for interaction with robots in assistive scenarios. The authors focus on reusability in socially assistive robots (SAR). They present an interactive system, which uses motivational instruction patterns, and they evaluate it in a sports scenario. In this scenario, the software design principles interact with social context and the physicality of the robot.

Aly et al. [START_REF] Aly | Metrics and benchmarks in human-robot interaction: Recent advances in cognitive robotics[END_REF] define metrics and benchmarks for different aspects of human-robot interaction, and discuss the outcome of an annual workshop that focuses on recent advances in cognitive robotics. Besides, they provide a summary of an interactive discussion session between the workshop participants and the invited speakers about different issues related to cognitive robotics research.

Acknowledgments

The guest editors would like to thank the authors whose contributions allowed this special issue to succeed. Similarly, the guest editors would like to sincerely thank the reviewers, who kindly provided very helpful reviews for the submitted papers to this special issue. Francesca Stramandinoli's work was supported by the Marie Curie Intra European Fellowship Robo-TAsk (624424) within the 7th European Community Framework Programme (FP7).