Diane S Lazard

H J Lee

Michael Gaebler

Christian A Kell

Eric Truy

Anne-Lise Giraud

Phonological processing in post-lingual deafness and cochlear

HAL is

Table 1 :

 1 Clinical data of the 8 profound deaf candidates for cochlear implantation

	Patient no.	Sex	Age at experiment (year)	Bilateral HL duration (year)	Deafness duration (month)	WRS pre-CI* (%, 60 dB)	WRS post-CI (%, 60 dB)
	1	F	67.7	46	36	0	48
	2	M	45	26	4	0	41
	3	F	32	10	12	0	69
	4	F	56.2	23	36	0	24
	5	F	56	22	48	0	48
	6	F	73	16	8	0	86
	7	M	31.7	18	216	36	X
	8	F	54.7	30	360	50	28
	HL = hearing loss. WRS = word recognition scores

There was no statistical correlation between age and deafness duration. Patient 7 was not implanted for personal reasons. * With optimally fitted hearing aids, Lafon test

Table 2 :

 2 Whole brain correlation with post-CI word recognition

	Correlation L/R	Region	BA	MNI	Cluster	Z
					coordinates	size	score
	Positive	L	Premotor cortex	6	-48 8 34	12	2.79
		L	Premotor cortex	6	-28 -8 60	12	4.00
		L	Superior parietal	7	-10 -72 62	51	3.12
			lobule
		L	Middle occipital gyrus 18	-10 -104 18	109	12.98
		R	Middle occipital gyrus 18	30 -84 4	35	3.71
		L Middle temporal gyrus 37	-54 -54 0	20	2.91
	Negative	L	Temporal pole	22	-54 4 0	76	17.49
		L	Temporal pole	22	-46 8 -16	108	6.33
		L	Inferior frontal gyrus	47	-36 28 -20	30	3.18
		R	Inferior frontal gyrus	47	36 24 -20	186	3.49
		R	Inferior frontal gyrus	47	44 36 -10	73	3.40
		L Middle temporal gyrus 21	-48 -36 -6	77	3.35
		L	Middle occipital gyrus 19	-46 -74 6	35	3.70
		R	Supramarginal gyrus	40	56 -34 26	11	3.51

Table 3 :

 3 Whole brain correlation with hearing loss duration and direct group comparison

	Patients > Controls
	Correlation	L/R	Region	BA	MNI	Cluster	Z
					coordinates	size	score
	Negative	L	Inferior frontal gyrus 44	-52 10 26	41	4.30
	with HL
		L	Middle occipital gyrus 18	-26 -94 0	34	3.62
	Positive with	L	Inferior frontal gyrus 47	-44 28 -8	67	4.02
	HL
	Patients >	R	Surpamarginal gyrus 40	60 -34 22	73	4.43
	Controls
	Controls >	-	-		-	-	-
	Patients
	HL = hearing loss duration
	-= no significant result.


Emna Mezghani

Riadh Ben Halima
email: riadh.benhalima@enis.rnu.tn

Ismael Bouassida Rodriguez

Khalil Drira
email: khalil.drira@laas.fr

A Model Driven Approach for Automated Design of Context-Aware Autonomic Architectures

In this paper, we propose a model driven approach automating the design of autonomic systems. We handle non-functional properties with a focus on managing QoS degradation for cooperative M2M applications such as health care. Nowadays, service delivery becomes close to end-users such as M2M applications, which are being incorporated into the existing infrastructure. The Remote Health Care System and specialized sensors for in-home patient monitoring are at the current forefront of new technologies. While there are benefits from technologies such as reducing costs and medical errors, associated architecture and communication infrastructure have to ensure care continuity and quality of service (QoS). In this paper, we propose a model driven approach which enables the generation of autonomic architecture from high level functional and non-functional requirements. Our work instantiates the Model Driven Architecture (MDA) approach. We elaborate formal rules using graph grammars to transform a high level functional requirements to an autonomic architecture implemented under GMTE, a Graph Matching tool 1 . We generate a Service Component Architecture (SCA) at the MDA low level (PSM) to implement the architecture in different technologies such as EJB, JMS, SOAP, etc. The Remote Health Care System shows the feasibility and the efficiency of our approach.

Introduction

The complexity of M2M applications, the dynamism of the environments in which they are operated, and the need for adaptation to changes are growing rapidly. For that reason, designing autonomic architectures that adapt their services to dynamically context changes is crucial. In general, such architectures have to take into account the management of software both at design and at runtime. These software are deployed in M2M environments including mobile devices, desktop computers, sensors etc.

To design such architectures, designers have to specify functional and non-functional requirements, and should consider the execution context as environment constraints at runtime. Indeed, if a system is able to correctly monitor context, detects changes and reacts to them in a smart way, it will be able to automatically generate a new architecture that meets requirements.

Several research activities handle the dynamic reconfiguration and show different ways to adapt system to the context changes and user requirements. The variety of the solutions in the literature underlines the need to provide tools, models and techniques to build adaptation for cooperative M2M applications. Design approaches have evolved to take into consideration important context changes that increase the design complexity.

In order to tackle the complexity of designing autonomic architectures and the related adaptation issues, we propose a model driven approach that minimizes designer's tasks by auto generating autonomic architecture starting from an initial model describing functional requirements. Our model driven approach automates the generation of 1 autonomic architectures through several transformation and refinement rules between different MDA levels. These rules extend the initial given architecture by the autonomic capabilities such as: monitoring, analysis, planning and execution [START_REF] Ben-Halima | A QoS-oriented reconfigurable middleware for self-healing Web services[END_REF]. This leads to obtain significant gains on software scheduling and cost, especially for complex systems.To illustrate the proposed models and their transformations, we consider the Remote Health Care System for in-home patient monitoring as a case study. In one hand, it allows to reducing costs and medical errors. In the other hand, it has to be monitored and managed at runtime in order to ensure care continuity with better QoS. For that reason, we propose to apply our approach to automatically generate an autonomic architecture for this system.

This paper is organized as follows. Section 2 gives a survey on the related work and presents issues and challenges related to autonomic computing management. Section 3, we present the Remote Health Care System use case. Section 4 details our model driven approach for autonomic computing which is based mainly on IBM MAPE-K loop [START_REF] Kephart | The vision of autonomic computing[END_REF], MDA approach and Graph grammar. This approach is approved with the Remote Health Care System by detailing the different auto generated models. Finally, conclusions to this work are presented and research perspectives are identified.

Related work

To design autonomic system, IBM has proposed the autonomic computing paradigm. Several research activities have adapted this approach to build their autonomic systems in different domain. In this section, we detail different approaches implementing this paradigm, then we present challenges confronted when managing the autonomic computing.

Autonomic Computing

An autonomic computing paradigm has a mechanism that can trigger changes in the computing system behaviors and structures in order to satisfy user requirements. Furthermore, it is a collection of autonomic mechanisms that manage software systems with minimum human intervention and given high-level objectives fixed by the administrator [START_REF] Kephart | The vision of autonomic computing[END_REF].

Autonomic system is extended by the concept of adaptability that characterizes system's capability to change itself in order to improve its performance and to continue its role in different environments. This is based on context-awareness features that refer to the ability of the system to know its execution environment through monitoring and be able to react to its changes through reconfiguration actions [START_REF] Parashar | Autonomic computing : An overview[END_REF]. According to Dey and Abowd [START_REF] Abowd | Towards a better understanding of context and context-awareness[END_REF], a system is sensitive to the context if it uses it to provide relevant information and services for the user, where the relevance depends on the task required by the user.

Several approaches have been developed to tackle the adaptation in different levels and domains to maintain systems functionalities. In [START_REF] Xiao | Qos-aware service composition and adaptation in autonomic communication[END_REF], the authors present autonomic mechanisms for QoS-aware service composition and adaptation of end-to-end network service. They introduce a service provisioning framework based on the autonomic principles, covering a number of essential functions, like domain discovery, composition, domain-wide monitoring, adaptation etc. Through domain graph abstraction, they reduce the domain composition and adaptation problem to the classic multi-constrained optimal path problem. As the network condition changes over time, they develop a set of adaptation path algorithms which ensures QoS requirements while minimizing the overall cost. Authors of [START_REF] Sun | Cme: a middleware architecture for network-aware adaptive applications[END_REF] propose CME, a middleware architecture for service adaptation based on network awareness. CME is structured in the form of a software platform which provides network awareness to applications, and manages network resources in an adaptive fashion. It uses a policy mechanism to facilitate the network management. Policies represent adaptation requirements to ensure at runtime. CME enables monitoring of security privileges by recording which stations communicate with each other.

Other frameworks are proposed to provide adaptability at the middleware level. In [START_REF] Nasser | Adaptive bandwidth framework for provisioning connection-level qos for next-generation wireless cellular networks[END_REF], an adaptive framework for supporting multiple classes of multimedia services with different QoS requirements in wireless cellular networks is proposed. In [START_REF] Liu | Accord: a programming framework for autonomic applications[END_REF], the authors present the Accord programming framework that extends existing programming models/frameworks to support the development of autonomic applications in wide area distributed environments. The framework was built on the separation of the composition aspects (e.g., organization, interaction, and coordination) of elements from their computational behaviors and extends them to enable their management at runtime while using high level rules. The operation of the proposed framework is illustrated using a forest fire management application.

Becker and Giese [START_REF] Becker | Modeling of correct self-adaptive systems: a graph transformation system based approach[END_REF] present an approach based on graph transformation techniques coupled with UML stereotypes in order to model autonomic systems. Reconfiguration, which is performed at runtime, is decomposed into three levels: goal management, change management and component control.

In [START_REF] Gauvrit | Safdis: A framework to bring self-adaptability to service-based distributed applications[END_REF], the authors present the SAFDIS framework. This framework enables the dynamic evolution of servicebased architectures by providing all the functionalities of the MAPE model. They focused mainly on the analysis and the planning phases while taking into account different constraints. In the execution phase, SAFDIS considers the migration of services as reconfiguration action. The implementation is built for the OSGi platform, using iPOJO to manage the life-cycle.

In [START_REF] Maurel | Ceylon: A service-oriented framework for building autonomic managers[END_REF], the authors detailed their CEYLON framework that focuses on building autonomic managers based on service-oriented components. Ceylon architecture is based on two-layers: the resources management layer that contains a set of management components managed by the Task Manager; and the manager adaptation layer that contains different manager Strategies and implements the MAPE by supervising the Task Manager and develops a runtime model for adaptation.

Current issues and challenges

In this section, we highlight challenges in the autonomic computing management for communicating systems.

Design vs. Runtime

Two different views to enforce autonomic computing features may be distinguished: the design time [START_REF] Dimka Karastoyanova | Extending web service flow models to provide for adaptability[END_REF][START_REF] Fahmy | Using graph rewriting to specify software architectural transformations[END_REF][START_REF] Ermel | Visual design of software architecture and evolution based on graph transformation[END_REF] and the runtime managements [START_REF] Chang | Automatic configuration and run-time adaptation of distributed applications[END_REF][START_REF] Bade | Carsa -an architecture for the development of context adaptive retrieval systems[END_REF]. For the first view, we can find design support tools which handle the application development cycle and optimize the resources for example. On the other hand, the autonomic management at runtime may relay on several adaptation techniques which use proxy services, change model of interaction and reorganize application structure [START_REF] Friday | Developing adaptive applications: The most experience[END_REF].

Local vs. Distributed

Autonomic computing may have a local or a distributed scope. Following a local approach, the components can be deployed on a single machine where changes are performed locally. Following a distributed approach, the components can be deployed on several distributed machines and synchronization problems between peer autonomic entities have to be managed [START_REF] Bridges | Supporting coordinated adaption in networked systems[END_REF].

Behavioral vs. Structural

The autonomic solutions suggested in the literature distinguish between behavioral and structural reconfigurations. The reconfiguration is behavioral (or algorithmic) when the behavior of the autonomic service can be modified, without modifying its structure. That ′ s way, this reconfiguration is considered as a service state adjustment. It modifies the internal composition of system components in order to correct it. Standard protocols like TCP and specific protocols such as those presented in [START_REF] Wu | Streaming video over the internet: Approaches and directions[END_REF][START_REF] Özgür | Atl: an adaptive transport layer suite for next-generation wireless internet[END_REF] provide behavior-based autonomic mechanisms. Other approaches, like JavaPod [START_REF] Bruneton | Experiments with javapod, a platform designed for the adaptation of non-functional properties[END_REF] manages the adaptation by using object compositions. It performs the reconfiguration action as dynamic extension of methods with new implementations.

The reconfiguration is structural when the service composition can be modified [START_REF]Ieee std 1471-2000, ieee recommended practice for architectural description of software-intensive systems[END_REF][START_REF] Garlan | Introduction to the special issue on software architecture[END_REF][START_REF] Ellis | Toward a recommended practice for architectural description[END_REF] dynamically. The reconfiguration is done by creating and connecting, removing and disconnecting components during execution to meet the execution context involving, for example, variations of communication networks and processing resources or requirements related to mobility of users and cooperation structure modifications.

The Remote Health Care System Case Study

In this section, we motivate the need of autonomic architecture through a case study. First, we present the Remote Health Care System (RHCS) case study. Then, we bring out issues related to the non-use of autonomic features. After that, we propose and formalize a novel model driven approach which adapts systems at runtime. RHCS represents a use case of an M2M application that supports the remote management of patient illnesses. It enables a smart management platform providing comprehensive tools for physicians and nurses to improve the quality of life of patients. This kind of applications is very crucial in the medical domain. RHCS is able to monitor remotely the patient health, control the medical treatments and trigger alarms when critical conditions are detected. The patient may be either at the clinic in case of critical diseases, or at home to reduce the traveling hassles with elderly and overweight patients.

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Figure 1. Remote Health Care System case study

Therefore, to get information about the patient's health, appropriate sensors have to be deployed. Generally, the patient wears one or more sensor devices that record health indicators such as blood pressure, body temperature, heart rate and weight as shown in Figure 1. It is also possible to use sensors located in the environment of the patient. This allows the monitoring of parameters related to the health condition of the patient. The health data are collected by these sensors and reported to servers periodically via M2M gateways. Wearable sensors often use wireless links to a gateway device that has WAN connectivity. A sensor device could communicate with gateways through different interfaces (Bluetooth, WiFi, 3G, etc.). For example, the blood pressure sends its collected data either through WiFi or Bluetooth interface. The device kind and the period of getting health information depend on the treated diseases. RHCS offers the continuous collection of patient data and the forwarding to servers that provide doctors or nurses instant access to these data through mobile phone or the Web.

One of the critical issues is to assure the timely and robust delivery of the life-critical health data. Especially in our case, in the resource-constrained wireless sensor networking environment. The RHCS must ensure care continuity to avoid vital signs information loss whatever the traffics even during patient mobility. Such system usually involves the usage of a variety of sensors that requires managing the QoS to maintain the availability to health data instantly. Different scenarios can occur and lead to degrade the performance of RHCS:

• Scenario1: The communication between servers and the gateway can take too long. This degradation is expressed by an increase in the response time of the gateway. When a large number of requests from physicians and nurses are sent via servers to get patient health data, the gateway may be overloaded and needs substitution by another equivalent (connected to the same devices "GatewayEq" as shown in Figure 1)

• Scenario2: At the level of the gateway, the interface managing the WiFi can be overloaded. This fact provokes an additional delay to the data delivery time. Balancing the trafic using the other available interfaces (for example interface managing Bluetooth) is a solution to avoid additional delay.

For each scenario, the RHCS has to be able to manage available resources to overcome degradations. We need specific components which monitor provided QoS and manage it at runtime through reconfiguration actions: substitution and load balancing.

A model Driven Approach for Autonomic Architectures

In this section, we describe our model driven approach to design autonomic architecture. It starts from a high level requirements and ends to an SCA architecture that manage context changes.

Overview of the proposed model

We present a generic approach which allows an automated generation of autonomic architecture starting from an abstract specification of the functional requirements and ending to an SCA architecture while following MDA. This approach is shown in Figure 2. In the left branch of the model (Design branch), the designer focuses on specifying the functional requirements. The designer creates an instance of the system architecture according to the style. In addition, the designer specifies the non-functional requirements. While the right branch, as shown in Figure 2, is composed of three levels as described by MDA: CIM (Computation Independent Model), PIM (Platform Independent Model) and PSM (Platform Specific Model). In each level, we have elaborated a set of automated treatments using patterns and rules able to refine and transform -intermediate-input models. In the bottom level, we provide an SCA based architecture, as final output (see Figure 7).

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In our approach, the final architecture is obtained by applying automatically successive Graph grammar productions the initial CIM. We use the graph grammar also to model the CIM and PIM levels as graph model while we use the SCA to model the PSM level.

CIM level Description

It is the most abstract model in MDA. It represents the system structure and hides the complexity. In our approach, we distinguish two sub-levels: the InitCIM (Initial CIM) and ReCIM (Reconfiguration enabled CIM) as

InitCIM Description

The InitCIM models the functional requirements which are described as communications between system's entities (sources and destinations as described in Figure 3), using the graph model. In the case of the Remote Health Care System, communications between entities are as following:

• M2MServers communicate with gateways. Therefore in the InitCIM, M2MServers are represented as "Source" and gateways are represented as "Destination".

• Gateways communicate with devices. These communications are based on different wireless technologies (Bluetooth, Wifi, 3G) that connect gateways to sensors, as shown in Figure 3. In the InitCIM, we consider the gateway communication interfaces as destinations.The equivalent destination is provided by the designer.

Initially, the M2MServers -acting as sources-are connected to the gateway "G". An equivalent gateway (GEq in Figure 3) is available as an equivalent destination since it is connected to the same devices and offers the same service as the already connected one, in our case the getaway "G", ( "connect to device").

As output of this step, we obtained the graph describing the InitCIM as shown in Figure 4. We distinguish two types of nodes: one has an id and a type corresponding to "Source" or "ConcreteDestination", the other type has an id, a type equals to "EqDestination", and a reference to the equivalent destination. The source and destination nodes are linked via the "communicate" edge that models the functional communication.

Transformation InitCIM into ReCIM

The first model auto-generated by our approach is the ReCIM. It is obtained by applying the graph grammar GGInitCIM-→ReCIM on the InitCIM. This step focuses on plug in the MAPE-K loop [START_REF] Ibm | An architectural blueprint for autonomic computing[END_REF] -as a black box-on the InitCIM model to enable autonomic features on the designed system. The MAPE-K loop is plugged between each destination and its sources. In our case study, the MAPE-K loop is plugged between M2MServers and Gateways and also at the communicating interfaces in the gateways as shown in Figure 5. Despite the transformation made to the InitCIM, the ReCIM always keeps an abstract view. It will be detailed using next rules and patterns.

In the ReCIM graph model, there is only one type of node which has two attributes, an id and a type that can take as value "Source", "ConcreteDestination", "Monitor", "Analysis", "Plan" and "Execution". And all communications are considered as "relatedTo". A is "relatedTo" B, while A and B are two nodes means that there is a communication between A and B, which could be explained as data flows or invocations. The relation "relatedTo" keeps the abstract view of the CIM level. For example, in Figure 5, we have plugged the MAPE

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. The InitCIM graph of RHCS loop (idM1,idA1,IP1,idE1) between S1, S2 and the two gateways. Then, the link between the loop components is "relatedTo". That means that the monitor node having the identifier idM1, can communicate only with the analysis and execution nodes having the identifiers idA1 and idE1. According to our case study (Figure 1) only (iddG, ConcreteDestination) is connected to (idS1, Source) and (idS2, Source) because initially only the "Gateway" is used in RHCS. At this step, we note that the type "EqDestination" has been replaced by "ConcreteDestination" to be used by the (idE1, Execution) for the reconfiguration. This is due to a set of graph grammar rules which allowed us to consolidate all equivalent destinations and attached them to the "Execution" node to ensure later the dynamic reconfiguration.

From the CIM to PIM

The PIM includes more details than the CIM. Our starting point in this step is the ReCIM. In our approach, we distinguish two levels in the PIM. First, the RePIM-mape-step1 (Reconfiguration enabled PIM-mape-step1) and second the RePIM-mape-step2 (Reconfiguration enabled PIM-mape-step2). This step takes the ReCIM graph model as input and provides the RePIM-mape-step2 as a final model which describes the PIM level. This is performed through a set of refinements rules. The first refinement, which uses the graph grammar GGReCIM-→RePIM-mape-step1, details the Execution component (Figure 6) by dividing it into sub-components. In our reconfigurable architecture, the Execution component contains three components: the "VirtualDestination" which offers the same services as the "ConcreteDestination" with an empty body (a dummy component), the "Desti-nationManagement" which is responsible of performing the planned reconfiguration and the "DynamicConnector", which is the main component in the Execution level. It binds "Source" requests to the "ConcreteDestination" according to the planned reconfiguration actions (substitution, load balancing. . .). This refinement leads to get the RePIM-mape-step1. The second refinement leads to obtain the RePIM-mape-step2, which details the Monitor component (Figure 6). However, this refinement takes input from non-functional requirements. In our reconfigurable architecture, there is three ways to detail the monitoring model:

• Source side: For each source, we associate a monitoring component "SSM".

• Destination side: For all providers, we associate only one monitoring component "DSM".

• Both Source and Destination sides: it represents a combination of the first and second cases.

In the RHCS, we propose to integrate autonomic architecture to manage the non-functional properties. Different combinations are possible: we propose to deal with the response time as a QoS parameter between the M2MServers and the gateways, while the capacity, which is defined as the number of requests that could be treated without degradation during a period of time, as QoS parameter at the gateway level. So, the monitoring component used between the M2MServers and gateways must monitor both source and destination sides which explains its composition into sub-components "SSM", for each source, and "DSM" for all "ConcreteDestination". While, monitoring the overload of the software components requires only monitoring the destinations side in order to measure the capacity of each interface so that we integrate in the gateway only the "DSM" component. As result of the PIM level, we obtain the RePIM-mape-step2 of the RHCS as shown in Figure 6.

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Compared to the ReCIM, the RePIM-mape-step2 is more detailed. First, it added other nodes already described and it distinguished between nodes interactions. The "relatedTo" edge is transformed into three types of edges as presented in Figure 6 :

• has-a: A has-a B, that means the node A is a composite of nodes and B represents its child.

• call: A call B, that means the node A calls or invokes the node B.

• monitors: A monitors B that means, the node A observes node B flows. In Figure 7, we show that we monitor both source and destination sides between servers and gateways. Therefore, the monitoring node (idM1,Monitor) has two"SSM", (idSSM1,SourceSideMonitor) and (idSSM2,SourceSideMonitor), and a "DSM" (idDSM1,DestinationSideMonitor). In the gateways, we present that we monitor only provider side, in this case, the monitoring nodes (idM2,Monitor)and (idM3,Monitor) have -each one-only a "DSM" ((idDSM2,DestinationSideMonitor) and (idDSM2,DestinationSideMonitor)).

From the PIM to PSM

This phase is considered as the most important and complex part of our approach. The PIM, modeled as a graph model, is transformed to the PSM. We choose to benefit from the advantages of the SCA for modeling the PSM.

Using the SCA didn't limit the developer to implement with one platform, since SCA exposes its functionality as services independently from the implementation. The mapping from the PIM to the PSM, which is made by applying a "Transformation Algorithm", is partial. In this phase, a set of the component (such as "Log", "Analysis" and "Plan") are presented in SCA component while the "Monitoring" and the "Execution" are hybrids a combination of SCA and non-SCA components. For example, in the monitoring module, the "SSM" (respectively "DSM") is composed of an SCA component implementing the monitoring treatment and a non SCA component "SourceSideInterceptor" (respectively the "DestinationSideInterceptor") which intercepts the incoming inflow/outflow to the "Source" (respectively the "Destination") and invokes the monitoring service to extract QoS parameter from the message. This composition is also applied in the "DynamicConnector" which intercepts messages destined to the "VirtualDestination", which is modeled as a non-SCA component, using the non-SCA component namely "DynamicConnectorInterceptor" that redirects them to the "ConcreteDestination" through "DynamicConnectorTreatment" implemented in an SCA Component. The reconfiguration actions are modeled as SCA component which facilitates the plug-in of new actions. The technique of interception is fixed by the developer. The Figure 7 depicted the final model obtained by our approach in the case of the Remote Health Care System.

As shown in Figure 7, we maintain Substitution and LoadBalancing components between the servers and gateways and even under gateways in order to be used if requirements evolve over time. Different combinations of the reconfiguration actions may be presented to manage the QoS. For example according to the scenario 1 (cf Section 3), the SCA Substitution component is invoked as reconfiguration action between the servers and gateways. Thus, the degraded gateway will be replaced by the equivalent one that offers a better QoS by decreasing response time. In scenario2 (cf Section 3), for each gateway, the SCA LoadBalancing component is invoked in order to balance physicians/ nurses requests over different interfaces. As a consequence, the connected gateway to servers take into consideration the QoS by controlling the interfaces overload.

Summary

Our approach aims to obtain significant gains on software scheduling and cost, especially for complex systems. The only effort considered from the architect side was to generate the initial CIM, in otherwise, mapping the requirements from a set of specifications to a graph model. In this stage, our approach supports the transition from abstract level to detailed levels and transforms at the same time this application into an autonomic one while managing the non-functional requirements. Therefore, our approach hides application complexity and minimizes both design and time costs.

To illustrate the use of our model driven approach to design context-aware autonomic architectures, we provide the initial CIM of the Remote Health Care System as an input. After processing this input, we obtain a SCA architecture (Figure 7 ) supporting different technologies and ready to be deployed.

Acknowledgement

This research is supported by the Itea2's A2NETS (Autonomic Services in M2M Networks) project2 .

Conclusion

In this paper, we proposed a model driven approach to automate the design of autonomic architectures following an MDA-based approach. We generate autonomic architectures from a high level functional requirements implemented by graph grammars rules. Our approach extends an initial architecture with MAPE components through transformation and refinement implemented using GMTE a Graph Matching and Transformation Engine. Then, we map the resulting architecture to different implementation technologies following the SCA standard such as EJB, JMS and SOAP. The Remote Health Care System represents our case study application. The generated architecture manages automatically the QoS through two reconfiguration actions : load balancing and substitution.

Our approach supports the synchronous communication based-applications. We are currently working on the refinement of our approach in order to manage the one way (asynchronous) communication . At the same time, we are implementing a GUI in order to make easy the capture of the high level function requirements.

Figure 2 .

 2 Figure 2. Model Driven Approach for Context-Aware Autonomic Architectures Design

Figure 3 .

 3 Figure 3. Remote Health Care System software components

Figure 5 .

 5 Figure 5. The ReCIM graph of RHCS

Figure 6 .

 6 Figure 6. The RePIM graph of RHCS

Figure 7 .

 7 Figure 7. SCA architecture model

https://a2nets.erve.vtt.fi/


Sami Lini

Pierre-Alexandre Favier

Xavier Servantie

Bruno Vallespir

Sylvain Hourlier

The plan as a cognitive-resource-saving tool 1 The plan as a cognitive-resource-saving tool: planning and anticipation, examples in aeronautics

Keywords: anticipation, planning, cognitive resource management, plan, literature review, aeronautical psychology

In their efforts to break with aircraft assistance, Thales Avionics and the ENSC (Ecole Nationale Supérieure de Cognitique)through the work carried out within their Human Factors for Aerospace Laboratory (HEAL)have been investigating HMIs assisting pilots in the anticipation process. The approach is user-centred: the understanding of the cognitive mechanisms underlying the anticipation process allows a context-dependent anticipation model to be devised, which aims at designing an ecological assistance tool for pilots. A possible approach to the anticipation process consists of investigating how cognitive plans are developed. To start with, the concepts of anticipation and planning are reviewed. By comparing their definitions, both concepts can be clearly differentiated and the essential function of a plan can be emphasised. The question of the plan as a means of saving cognitive resources is then addressed. Examples in aeronautics are given. Several relevant surrounding psychological concepts are introduced, pointing out the main features of the plan: hierarchy and deficiency. To conclude, based on the conditions of implementation of the plan, the mechanisms of anticipation are discussed, which are close to the principle of reafference.

The plan as a cognitive-resource-saving tool: planning and anticipation, examples in aeronautics

In an effort to break with aircraft assistance, a study is conducted on HMIs assisting pilots in the anticipation process. The approach is user-centred: the understanding of the cognitive mechanisms underlying the anticipation process allows a context-dependent anticipation model to be devised, which aims at assisting pilots through an ecological HMI. A possible approach to the anticipation process consists of investigating how cognitive plans are developed.

From definitions of the concepts of anticipation and planning, this literature review will address the question of the plan as a means of saving cognitive resources, from its preparation to its implementation. This review will rely on works in the field of cognitive psychology, mainly those by Amalberti and Hoc, and will insist on their contribution to the field of aeronautical psychology. It will be illustrated with examples taken from both civil and military aviation, in order to point out various kinds of constraints, in particular time constraints.

In this effort, the anticipation process will be specifically contemplated and scrutinised. From the activation to validation of a plan, the literature referenced will highlight a closed-loop mechanism, close to the principle of reafference of von [START_REF] Von Holst | Das reafferenzprinzip[END_REF].

Definitions: planning or anticipation?

Even though the term 'anticipate' is widely used, it is nonetheless difficult to define it precisely. Its Latin etymology 'anticipare' means 'to take action beforehand, to take the initiative, to take the lead'. It is often confused with the terms 'predict', 'foresee' and 'plan ahead'. One idea is common to these concepts: the process is both in the present and in the future.

In psychology, [START_REF] Sutter | L'anticipation[END_REF] defined anticipation as a 'movement by which man carries his entire being beyond the present into a future, near or far, that is essentially his future' (p. 5). Even though this definition does not exclusively consider taking action, it elicits the idea of thinking ahead: to anticipate is to represent ourselves and our environment in a process of evolution and adaptation.

In cognitive psychology, [START_REF] Cellier | Exigences et gestion temporelle dans les environnements dynamiques[END_REF] gave the following definition: 'An activity consisting of evaluating the future state of a dynamic process, determining the type and timing of actions to undertake on the basis of a representation of the process in the future, and, finally, mentally evaluating the possibilities of these actions. It is dependent on the general purpose assigned to an operator in a dynamic context, which is to keep the process, physical or otherwise, within acceptable limits, and therefore avoid the propagation of disturbances. It is also governed by a logic aimed at reducing the complexity of a given situation. Finally, it is a way of managing individual resources. ' (p. 35) Several ideas are included in this definition: assessment of the development of the situation, mental simulation, and anchoring in both temporality and action. Two important aspects should be stressed: the teleonomic aspect, as anticipation only makes sense in light of a general purpose; and the cognitive aspect, as the process is also governed by the need to reduce the load and complexity of the environment. It is therefore possible to anticipate without planning.

By contrast, planning cannot be detached from anticipation. [START_REF] Hoc | Psychologie cognitive de la planification[END_REF] defined planning as 'the development and/or implementation of plans ' (p. 68). In other words, planning consists of building a simplified schematic representation of a task, breaking it down into sub-goals, the aim of this being both to save cognitive resources and to be action-oriented: planning is teleonomic, i.e. it is directly linked to the achievement of a predefined end. As a result, according to [START_REF] Amalberti | Anticipation[END_REF], if the purpose of planning is to develop an executable plan which is compatible with the operator's skills, the purpose of anticipating is precisely to test the validity of such a plan. After pointing out the central position of the concept of plan, this study will focus on the means of preparing and implementing plans.

About the concept of plan

Developing plans [START_REF] Hoc | Psychologie cognitive de la planification[END_REF] defined the concept of plan as 'a schematic and/or hierarchical representation which is likely to guide the activity'. The terms 'schematic' and 'hierarchical' are of particular importance: they mean that the representation is defined at a low level of detail and that a strategy of successive refinements is needed. If, in Rasmussen's decomposition hierarchy (Rasmussen, 1986), the plan is defined at a higher level of detail than the implementation of the activity, then it can be hierarchical as well: this will bring to light different levels in the plan's structure, and the relationship existing between such levels. This may prove useful when the implementing rules of a given procedure are linked to those of a sub-procedure to be performed later. Thus, planning also consists of sequencing operations before performing them. [START_REF] Amalberti | La maîtrise des situations dynamiques[END_REF] highlighted a fundamental limitation in dynamic contexts: the processes of planning and refining a representation are to last until no credible solutions are found or as long as the cost of implementing the representation is unacceptably high.

O 'Hara & Payne (1998[START_REF] Denecker | Les composantes symboliques et subsymboliques de l'anticipation dans la gestion des situations dynamiques[END_REF] brought up the 'stopping condition': the most important point consists of defining a meta-plan relying on adequacy criteria (minimum acceptable requirements, objectives, costs) and defining the difficulties that need to be avoided or addressed. It is not necessary to refine the current representation indefinitely with unsteady elements or if the defined solution is strong enough with regard to the acceptable requirements.

In the context of photographic reconnaissance in single-seater fighter aircraft, [START_REF] Amalberti | La conduite de systèmes à risques[END_REF] distinguished between expert and novice approaches: expert pilots use prospective procedural planning, whereas novices use a mix of prospective strategiesthe ones they are taughtand retrospective strategies in order to manage realistic implementations considering their lack of expertise. As a result, experts are faster at developing plans for they possess metaknowledge which allows them to use already memorised parts of plans. This study will then focus on the structure and main features of the plan.

The plan and related concepts

The first main feature of the plan has been outlined above: it is fundamentally schematic. Its second main feature is that of being oriented towards anticipation. Several cognitive psychology concepts related to the plan deal with the latter feature. The notion of 'scheme' introduced by [START_REF] Piaget | Naissance de l'intelligence chez l'enfant[END_REF] goes further in this direction: any actions taken in the real world would follow a predetermined scheme adapted to the current situation.

A direct consequence of this postulate is that the notion of expectation about the development of the situation must be added to our view of the plan. [START_REF] Vergnaud | Concepts et schèmes dans la théorie opératoire de la représentation[END_REF] insisted on the relationship between the scheme and the real world: he put the stress on the necessary isomorphism between the plan and the real world, which is validated by the performance of the plan. [START_REF] Denecker | Les composantes symboliques et subsymboliques de l'anticipation dans la gestion des situations dynamiques[END_REF] postulated that the scheme organises the subject's behaviour by cutting the real world down into objects. The resulting representation may be the subject of operations, inferences, rules of action, or even predictions and expectations. Regarding Bartlett's concept of 'schema' [START_REF] Bartlett | Remembering: A Study in Experimental and Social Psychology[END_REF], [START_REF] Denecker | Les composantes symboliques et subsymboliques de l'anticipation dans la gestion des situations dynamiques[END_REF] raised several issues relevant to the debate:

 it is an unconscious mental structure produced from past experience;  it stores, in the long-term memory, a set of active knowledge structures instead of passive images;

 it is a reconstruction, not a copy, of the past. [START_REF] Bobrow | Some Principles of Memory Schemata[END_REF] hypothesised that the schema is not a definite structure, and that it links the variables and the constraints over them. Such variables are singled out during the performance of the plan thanks to environmental clues which shape the implementation of the plan.

In short, as pointed out earlier, the plan is hierarchical and it is also fundamentally deficient. Thanks to its structure, operations can be performed on the plan itself, and this feature is a way of saving resources. Next, the accuracy of this representation needs to be reviewed. [START_REF] Amalberti | La conduite de systèmes à risques[END_REF] pointed out the role of time constraints in the search for a compromise between the cost of plan development and the degree of accuracy in guidance, i.e. the tolerance to variations linked to possible hazards. In less dynamic systems, the action plan is usually maintained at a low level of detail and its interpretation and the ultimate choices of implementation are made at the very moment of performance of the plan (de Keyser & van Daele, 1986); besides, the preparation of solutions is not given much attention. In more dynamic (i.e. fast) processes, such as fighter aircraft, the pilot needs to arm himself against the lack of time and resources in order to develop new plans or solve unforeseen problems. The solution lies in developing more detailed and mode guiding plans, and preparing responses to a limited number of likely incidents. It is a bet, weighted by the pilot's experience and the context of action.

Plans, time constraints and implementation

A commercial-transport-aircraft flight is a temporally intermediate situation.

Preparing to incidents is no longer part of the initial briefing but is contemplated on a sequence-by-sequence basis during the entire flight. [START_REF] Wickens | Situation Awareness and Workload in Aviation[END_REF] defined four kinds of tasks to be undertaken while flying an aircraft: aviating, navigating, communication, and systems management. All of these tasks need to be performed, and this is the reason why pilots, however expert they may be, rely on checklists. Anticipating can therefore consist of preparing responses to a list of likely events. This example is in line with Rasmussen's SRK model [START_REF] Rasmussen | Skills, rules, and knowledge; signals, signs, and symbols, and other distinctions in human performance models[END_REF]: when facing a complex situation requiring implementation of knowledge to construct a response, anticipation makes it possible to develop a routine that is ready to use when appropriate. [START_REF] Amalberti | La conduite de systèmes à risques[END_REF] developed the idea further: the operator avoids, as much as possible, situations which prove complex with regard to his own expertise, and he gets ready for unavoidable situations by organising his responses beforehand.

Nevertheless, a few important limitations should be noted: checklists can by no means assist the pilot in performing multiple tasks simultaneously, nor do they address by definitionthe unexpected. Moreover, it is often not possible to draw up a list of tasks related to procedural knowledge, however vital this knowledge may be for the appropriate performance of the tasks [START_REF] Wickens | Situation Awareness and Workload in Aviation[END_REF].

As discussed earlier, anticipation is the most important tool for implementing plans, the details and objectives of which depend on expertise. Deviations from plans are a perfect example of this [START_REF] Amalberti | La conduite de systèmes à risques[END_REF]. For expert pilots, deviations are systematic and repetitive but limited in their value. Though accurate, the framework provided by plans allows sufficient latitude for interpretation during implementation.

Experts therefore prefer procedural, action-oriented plans in order to save cognitive resources. For novice pilots, deviations are exceptional but of greater magnitude: they are not the result of procedural interpretation but of a will to protect the initial plan and apply the responses prepared during planning. An example is the case of young pilots who delay their actions intentionally in order to anticipate a subsequent situation to which they may have to react by increasing their speed.

Whereas planning consists of developing plans, anticipation works toward their validation. After analysing the structure of plans, the mechanisms of anticipation will now be reviewed.

Anticipation

In the context of driving a car, [START_REF] Tanida | A hierarchical model of operational anticipation windows in driving an automobile[END_REF] and [START_REF] Mundutéguy | Perception et anticipation du comportement d'autrui en situation simulée de conduite automobile[END_REF] offered a generalisation of the principle of reafference proposed by von [START_REF] Von Holst | Das reafferenzprinzip[END_REF] to explain how anticipation works: a plan of the situation is activated from a pattern of sensory environmental clues. The expectation component of the plan was described above. An efferent copy of this component is produced, which can be compared with the real world. The plan is, in itself, an active means of recognition: it guides and directs the search for information in order to validate itself [START_REF] Amalberti | La conduite de systèmes à risques[END_REF]. A plan is validated when its entire content can be particularised to the situation; but some mechanisms make it possible to fill any gaps with acceptable default values.

The teleonomic component of the plan was described above: it is action-oriented and directed towards a specific purpose. This component of the plan is subject to two forms of monitoring [START_REF] Amalberti | La conduite de systèmes à risques[END_REF]: external monitoringinvolving the physical process and the situation -, and internal monitoringinvolving the cognitive actor of the process. In case of problem (negative outcome in self-assessment of the performance), thanks to internal monitoring, the degree of cognitive investment can be increased in order to adjust the selected mental model: enrichment, adjustment, or even construction of a new solution. This metacognitive monitoring is also in charge of arbitrating the following processes: intensity, priority, stopping.

Depending on the available timeframe, this monitoring offers different strategies: in the short term, the operator first and foremost tends to deal with ongoing tasks, using heuristics and various measurements (predictability, time constraints, etc.) to be able to perform multiple tasks at the same time while minimising risks; in the long term, the Cognitive Architecture of Dynamic Control model described by [START_REF] Hoc | Diagnostic et prise de décision dans les situations dynamiques[END_REF] illustrates the possibility, for the operator, to open several cognitive loops simultaneously, which project into different levels of temporal depth.

The levels of performance, models and anticipations are continually selfassessed. Because of his cognitive limitations, the operator has to achieve a compromise with regard to the possible corrections; this compromise is based on the resources available and on the various requirements related to the task. Three loops take place in three different temporalitiesi.e. three distinct ambitions of correctionsfrom automatic control to the complete reconsideration of the current representation. As set out earlier, the attentional supervisor is responsible for the possible corrections and for any local changes in the level of action control.

At this stage, it appears that a major way of assisting pilots in the anticipation process consists of helping them choosing their plans. This may be considered as a further argument in favour of our approach: the operator remains at the centre of the decision loop. Any additional information that may be provided to the operator would help him construct or complete his representation but would not, under any circumstances, intend to replace it.

Conclusion

As shown throughout this study, the plan plays a central role in both the cognitive-resource-saving process and the anticipation process. In order to design a tool assisting operators in the anticipation process, it is relevant to tackle process modelling starting from the plan itself. This literature review pointed out the hierarchical and fundamentally deficient structure of the plan, as well as its activation through the detection of a pattern of environmental clues. Defining an activation framework can be a way of designing such a tool.

A last important aspect is about the distance between the initial plan and the plan actually implemented. Two subsidiary questions emerge:  How to measure the cognitive cost of challenging an established plan, or even wholly or partially rebuilding it, when faced to contradictory environmental elements (unfulfilled expectations)?

 How to handle tolerance to deviations from the initial plan (are errors controlled or not)? These questions will be the subject of more detailed investigations in future works.


Sami Lini
email: sami.lini@ensc.fr

; Sylvain Hourlier

Xavier Servantie

Bruno Vallespir

Pierre-Alexandre Favier

Helping Pilots Better Anticipate: a User-Centered HMI Concept for Flight Displays

Anticipating and planning are two major components of the human ability to manage one's own cognitive load. Helping pilots enhance their anticipation potential in the cockpit environment becomes an important challenge to address. From an in-situ task analysis and a cognitive model of the anticipation we conceptualized an HMI aiming at supporting the anticipation and planning ability of pilots.

Introduction

Anticipation and planning are two major components of the human ability to manage one's own cognitive load. Helping pilots enhance their anticipation potential in the cockpit environment becomes an important challenge to address. Implementing a user-centered methodology for designing such a tool makes sense. It is then mandatory to understand the anticipation cognitive process. In the first part we will present a model of the anticipation from the cognitive psychology literature. We establish that one way to help pilots better anticipate can consist into presenting in advance the tasks they will have to perform. In order to do so, we implemented a knowledge management methodology to accurately define the task flow. This will be presented in the second part. At last we present the concept HMI resulting from our preliminary work.

Anticipation Model

The cognitive psychology field has been addressing the question of anticipation, mostly in static situations. A cockpit is a dynamic environment [START_REF] Cellier | Exigences et gestion temporelle dans les environnements dynamiques[END_REF] which is highly demanding in a cognitive way. The area of application of our study is civil aviation. In this environment, we talk about long-term symbolic anticipation [START_REF] Hoc | Supervision et contrôle de processus: la cognition en situation dynamique[END_REF]. In [START_REF] Lini | Developing ASAP (Anticipation Support for Aeronautical Planning)[END_REF], we underlined the fact that anticipation can truly be considered as a metacognitive process lying on cognitive resources management, situation awareness. From a set of sensory cues, the subject makes an assumption about the evolution of the situation and its cognitive demand and time management, using abstract representation.

Situation awareness process operates in three steps [START_REF] Endsley | Toward a theory of situation awareness in dynamic systems[END_REF]: perception of sensory cues of the environment, comprehension of the situation, projection of its evolution. Rasmussen's SRK model [START_REF] Rasmussen | Skills, rules, and knowledge; signals, signs, and symbols, and other distinctions in human performance models[END_REF] defines three levels of control of the situation: skills, rules, knowledge can alternately be used to deal with a problem, depending on both the available time and the subject's expertise, knowledge being highly resources demanding. In cognitive psychology, time is considered a high level process. This means that there are cognitive tools to manage it. Valax in [START_REF] Valax | Cadre temporel et planification des tâches quotidiennes: étude de la structure des plans journaliers chez les agriculteurs[END_REF] established what these tools are: pivotal tasks, time-based goals, tasks' built-in flexibility and open tasks, i.e. tasks that are not temporally constrained. All of these tools require an assessment of the kind of task the subject deals with. This theoretical background allowed us to distinguish two kinds of anticipation [START_REF] Lini | Developing ASAP (Anticipation Support for Aeronautical Planning)[END_REF]: preparing a solution in advance, Figure 1, and acting in advance. At that level of development of our concept we will keep to the former, which does not necessitate any kind of automation. Figure 2 explains more deeply how anticipation operates: it activates a mental representation of the situation -a schemafrom a set of environmental sensory cues. This abstract representation is incomplete and the subject will tend to fill the gaps. While doing so, this representation is compared to the reality. At the end of the loop, a decision is made: if the difference is small, the subject can go on; otherwise, he has to challenge anticipation and find another solution to his problem. Therefore an anticipation support should allow the subject to choose an appropriate representation, refining this representation and projecting the evolution of the situation. It should also be able to make the subject aware of a wrong choice of schema and make challenging it easier.

Task Analysis

Helping pilots better anticipate can consist in presenting the whole set of tasks they will have to perform. Modeling in the knowledge management field permits to get a simple representation of a complex system in order to better understand the activity of an expert.

Using the MASK knowledge management methodology [START_REF] Matta | Knowledge Capitalization with a knowledge engineering approach: the MASK method[END_REF] we made a task analysis aiming at building a knowledge base. The tasks diagram, Figure 3, is one of the models contained in the MASK methodology.

Figure 3 : Tasks diagram

At first, we capitalized the knowledge of an expert pilot on a defined flight phase: the descent and approach phases of a commercial flight are known to be representative of the troubles pilots have to face. This capitalization consists in a recorded semi-directed interview where the subject is asked to describe as accurately as possible his tasks and activity. Questions are asked from time to time to disambiguate grey areas. A camera video recording of the cockpit was also made during an actual flight phase with the expert. These two sources of information form the material we have been using for building the task diagram. We stressed the inputs and outputs of every single task and their links to anticipation. This permits to build a representation of the progress of the pilots' activity over time. A second model, the activity diagram, gives a higher level of details about it: processes and used resources are detailed. We interviewed a second expert to double check the modeling.

At the end of this phase we get a knowledge base about the activity of pilots in a commercial flight during the descent and approach phases

HMI Concept

We use the previous analysis to fuel our HMI with data. One main idea is to present a task flow. This only makes sense with regard to an adequate time representation. We chose to mix distance with time information: pilots work with both and they mainly need time to distance information. Absolute time is not relevant for them because the main information for projecting the status of the plane is its speed. Before taking-off pilots well know the three dimensional trajectory they will be following. Using the flight management system, it is possible to assess the speed at any moment of the flight. It is then possible to match time and distance: depending on the speed, the graphical representation of time will be either expanded or contracted.

Figure 4: HMI Concept

According to the anticipation model, the task flow allows the subject to perceive and comprehend the situation: tasks are presented in advance and makes the subject able to refresh his representation at any time. Links between tasks are shown and help him better understand the evolution of the situation.

The time to distance representation makes easier for him to project the status of the situation and allows him to save resources from mentally representing the time to distance relationship. The time management process is made easier and the subject is made able to assess the temporal constraints for each task and the cognitive investment needed to deal with each phases. This way, he is able to prepare himself to the problems he might have to face.

A step forward is to deal with the second kind of anticipation: acting in advance. Further works are undertaken to offer the user the ability to automatize tasks. The challenge to be addressed here is to maintain him at the center of the decision loop in order not to decrease his situation awareness. This question will be the subject of future publications.

Acknowledgement

We would like to thank Nicolas Renoir, engineering student of ENSC, for his work on the task analysis part.

Figure 1 :

 1 Figure 1: Anticipation: preparing a solution in advance Situation awareness makes the subject perceive, understand and project the situation. Time management allows him to assess the temporal constraints of the problem. In this case, it is a timebased goal, which cannot be performed before the due date. Cognitive resources management allows him to assess the cognitive demand. It consequently allows him to climb into his control levels -depending on his expertise-and use his knowledge -or less if not necessary -to create a ready-to-use solution.

Figure 2 :

 2 Figure 2: Anticipation model from [7] and [8]


Sami Lini

Pierre-Alexandre Favier

Xavier Servantie

Bruno Vallespir

Sylvain Hourlier

DEVELOPING ASAP (ANTICIPATION SUPPORT FOR AERONAUTICAL PLANNING)

The 2009 A320 ditching on the Hudson River revealed to the public that critical decision-making ability was a key asset for pilots. Situation awareness and workload management are two key elements as well as the ability to anticipate how the situation will evolve. Thales Avionics is funding research through its HF research Lab (HEAL) on HMI facilitating pilot anticipation. This presentation covers a literature review on anticipation so as to develop a model of anticipation in a realistic flying task. Analyzing the concept of anticipation leads us to consider it as a metacognitive process relying on cognitive resource management, situation awareness and time management, using abstract representations. After reviewing these concepts, we focus on time management and anticipation. It reveals a closed loop mechanism inspired from the reafference principle. This will be the basis for a HMI based on a model of anticipation in flight.

The feat of ditching on the Hudson in January 2009 has revealed the criticality of certain decisions under high pressure. Metaknowledge and anticipation are the cornerstones of human ability to manage these types of situations: helping pilots to better anticipate situations is therefore a major challenge that the ASAP project (Anticipation Support for Aeronautical Planning) intends to address. This presentation is an anticipation-oriented literature review, with the intention to develop a model of anticipation in the context of a simulated flight task. This work will enable the design of a user-centered HMI prototype dedicated to assisting in-flight anticipation.

First, we will compare different definitions of the "anticipation" concept in diverse disciplinary fields. This will lead us to consider anticipation as a metacognitive process based on cognitive resource management, situation awareness and time management, using abstract representation. These themes are discussed with regard to their contribution to the process of anticipation. Second, we concentrate on time management within the scope of cognitive psychology. Emphasis is placed on the relationship with time and the environment: workload as a function of the perceived distance to the goal is specifically addressed. Finally, work on anticipation is presented, highlighting a closed-loop mechanism similar to the reafference principle.

Anticipation as a metacognitive process

Definition

Even though the term anticipate is commonly used, it is nevertheless still difficult to define precisely. We confuse it and often use predict, foresee and plan ahead instead. There is one idea common to these concepts; it is a process that is both in the present and the future, as suggested in its Latin etymology: "anticipare" means "take in advance, take the initiative, take the lead".

In psychology, [START_REF] Sutter | L'anticipation: Presses Universitaires de France[END_REF] defines anticipation as a "movement by which man carries his entire being beyond the present into a future, near or far, that is essentially his future". Even though this definition does not exclusively consider taking action, it elicits the idea of thinking ahead: to anticipate is to represent ourselves and our environment in a process of evolution and adaptation, it is a "transplant of the future in the present" [START_REF] Minkowski | Le temps vécu: Delachaux et Niestlé[END_REF]). A new idea emerges from the definition of an anticipatory system in computer science provided by [START_REF] Rosen | Anticipatory systems: philosophical, mathematical, and methodological foundations[END_REF]: an anticipatory system is "a system containing a predictive model of itself and/or of its environment, which allows it to state at an instant in accord with the model's predictions pertaining to a later instant". In computer science, to anticipate entails two phases: a prediction phase and a phase of using the prediction. It is therefore a system that has a kind of "future memory", a database enabling it to infer the evolution of the situation as a function of similar situations already encountered.

In cognitive psychology, [START_REF] Cellier | Exigences et gestion temporelle dans les environnements dynamiques[END_REF] gives the following definition: an "activity consisting of evaluating the future state of a dynamic process, determining the type and timing of actions to undertake on the basis of a representation of the process in the future, and, finally, mentally evaluating the possibilities of these actions. It is dependent on the overall goal assigned to an operator in a dynamic situation, which is to keep the process, physical or otherwise, within acceptable limits, and therefore avoid the propagation of disturbances. It is also governed by a logic aimed at reducing the complexity of a situation. Finally, it is a way of managing individual resources". All of the preceding ideas are included in this definition: assessment of the evolution of the situation, mental simulation and encoding in both temporality and the action. However, a few additional points are presented: the teleonomic aspect, anticipation only makes sense in light of an overall goal; from a cognitive point of view, the process also meets the requirement to reduce the load and the complexity of the environment.

For this bibliographic work, we will therefore focus on anticipation as a metacognitive process enshrined in a dynamic temporality: a process aimed at conserving cognitive resources, requiring awareness of oneself and of the situation, as well as time management ability.

Anticipation as a means of cognitive economy: impact of cognitive resource management

In the management of the high-pressure situation presented earlier, it is essential for the pilot to avoid any cognitive overload. Anticipation is presented as a process that makes it possible to overcome the limits placed on his resources.

One strategy consists of spreading cognitive processing out through time: [START_REF] Amalberti | Anticipation[END_REF] gives the example of preparing a response in advance to a probable combination of events. This example is itself a continuation of the SRK model [START_REF] Rasmussen | Skills, rules, and knowledge; signals, signs, and symbols, and other distinctions in human performance models[END_REF]): in the face of a complex situation requiring the use of knowledge to develop a response, anticipation enables the construction of a routine that is ready to be used when needed. [START_REF] Amalberti | La conduite de systèmes à risques[END_REF] further clarifies this idea: the operator avoids complex situations as much as possible in line with his expertise, and prepares for those situations that he cannot avoid by preparing his responses in advance.

3 Within a complex system, such as a cockpit, the operator may be faced with a situation where several distinct temporal dynamics coexist (inertia of the airplane, transmitting information via the radio, etc.), [START_REF] Leplat | Ergonomie du contrôle de processus en marche simultanée : un cas dans l'industrie biochimique[END_REF] emphasize that, in these cases, anticipation exploits the temporal tolerance induced by these dynamics. We will discuss this later in more detail.

With regards to an activated plan, anticipation also consists of mentally simulating the evolution of a situation: this is the last level of Endsley's situation awareness model [START_REF] Endsley | Toward a theory of situation awareness in dynamic systems[END_REF]), which we will also discuss later. When the task requirements increase, anticipation allows assumptions to be made on the evolution of the situation as well as to test them [START_REF] Amalberti | La conduite de systèmes à risques[END_REF]). The operator imagines the consequences of these situations and consequently changes his conduct by adapting his plan of action if necessary. This attitude allows him to keep the situation within controllable limits while managing any deviations to the plan. To illustrate this, in aviation education, it is said that "the pilot must learn to be in front of his plane" and should be encouraged to "make permanent assumptions about the future situation in order to actively adapt to this situation and not wait for it to occur". Thus, according to [START_REF] Amalberti | La maîtrise des situations dynamiques[END_REF], anticipation enables the subject to integrate his own ability in order to impact one's environment and protect oneself from feared events by adjusting the plan.

Anticipation and projection of the evolution of the situation: impact of situation awareness

Even though we emphasized the importance of the representation of the situation during a crisis, it is still correlated to having a good awareness of the situation. The idea of anticipation is included in the definition given by [START_REF] Endsley | SAGAT: A methodology for the measurement of situation awareness[END_REF], "situation awareness is the perception of the elements in the environment within a volume of time and space, the comprehension of their meaning, and the projection of their status in the near future." The last part of the definition corresponds to level 3 of this model: awareness of the status and dynamic of the elements is made possible by the first two levels of the situation analysis and allow the evolution to be assessed. [START_REF] Baumann | A Comprehension Based Cognitive Model of Situation Awareness[END_REF] add the following to this analysis: the information collected activates the associated knowledge stored in long-term memory. From this knowledge network, a coherent representation is constructed by following a constraint-satisfaction process: the compatible elements are activated, the incompatible elements deleted. The subject's expertise allows him to add expectations regarding the evolution of the situation to this representation. These expectations are related to schemas that are activated when certain environmental patterns are encountered; more details will be provided for this later.

Comprehension of the situation consists not only of giving coherence to the facts but also explaining them and predicting their evolution. This is therefore a continuous mechanism that is based on a dual relationship with temporality: backwards to explain the cause and forwards to predict the future. All comprehension is teleonomic: it therefore involves permanently constructing an adapted representation, at a given moment, depending on the specific purpose.

Time management

The concepts addressed earlier have shown that the idea of anticipation is inconceivable outside of a temporal framework. The issue of cognitive processes allowing the appropriation of this framework cannot be ignored. [START_REF] Michon | The complete time experience time experience[END_REF] defines time as "the conscious experiential product of the processes that allow the (human) organism to adaptively organize itself so that its behavior remains tuned to the sequential (i.e., order) relations in its environment." From this perspective, time is no longer a concept for which attributes need to be defined, nor can we still consider it as an intrinsic property of our world, but as a co-occurrence of processes that allows synchronization with the evolution of the environment. This implies that the cognition of temporality belongs in the field of declarative knowledge; the temporal representations would then be a form of high-level cognition.

The psychology of time

Of all the theoretical concepts dealing with representations of time, we adopt one principal idea based on recurring events. Among farmers-ranchers, [START_REF] Valax | Cadre temporel et planification des tâches quotidiennes: étude de la structure des plans journaliers chez les agriculteurs[END_REF] establishes that a plan is determined around repeated daily pivotal tasks and time-based goals, which are irrelevant both before and after the appointed time. However, two systems provide margins of tolerance: tasks are always planned with some built-in flexibility to make up for a predecessor's possible delay, and open tasks, which are not constrained by a "when to act" and may be inserted in moments of calm. As briefly mentioned earlier, the process of anticipation plays a significant role in the situation described here: by simulating the evolution of his future cognitive load, the operator plays with the margins that time gives him to smooth out and avoid peaks in the cognitive load, i.e. cognitive overload. This means that the operator must be able to assess his cognitive investment based on the distance to the action.

Time and cognitive control

In managing the time available for the action, there is a fundamental difficulty related to the anticipation span. There is a proportional relationship between the distance to an expected event and the uncertainty around it. [START_REF] Hollnagel | Context, cognition and control[END_REF] distinguished four types of control, functions of the perception of time available for the action and the familiarity of the situation: strategic, tactical, reactive and disorganized, which define different degrees of mastery of the situation and behavior types. This is closer to the SRK model [START_REF] Rasmussen | Skills, rules, and knowledge; signals, signs, and symbols, and other distinctions in human performance models[END_REF]), mentioned above. [START_REF] Reynolds | Modeling the Air Traffic Controller's Cognitive Projection Process[END_REF] addresses this question in the aeronautical field. He states that, in the short term, we place ourselves in a persistent area, where the environment will not change very much. A little further in the future, models, such as physical laws, can be applied to get a relatively accurate idea of the evolution of the situation; we have now positioned ourselves in a deterministic area. Beyond a certain distance in time, the rules for assessing the evolution of a situation are subject to a combinatorial explosion linked to the set of variables to be considered. Here, we are in a probabilistic area, in which the slope of the uncertainty curve increases exponentially. This raises the question of the relationship between gain associated with anticipation and cost associated with the uncertainty generated by the distance to the event and thus, the importance of planning.

Planning

Hoc (1987) defined planning as "the development or implementation of plans". He attributes two fundamental properties to the plan: it is both schematic and orientated towards anticipation. Several cognitive psychology concepts related to the plan express this second characteristic. Thus, the notion of scheme introduced by Piaget (1952) continues in this direction: any action in reality would be made with a previously established scheme in mind and then adapted to the current situation. Therefore, this involves a mental representation that contains a component of expectations about the evolution of the situation that is action-orientated with an identified purpose.

The concept of schema [START_REF] Bartlett | Remembering : A Study in Experimental and Social Psychology[END_REF]) provides a second element in this analysis. [START_REF] Bobrow | Some Principles of Memory Schemata[END_REF] hypothesize that this structure, which is not fully specified, represents the relationships between the variables and the constraints on these variables. They will be distinguished during the implementation through environmental cues, which determine its application. Anticipation operates on this idea.

Anticipation

To illustrate how anticipation works, [START_REF] Tanida | A hierarchical model of operational anticipation windows in driving an automobile[END_REF] propose a wider application of the reafference principle proposed by von [START_REF] Von Holst | Das reafferenzprinzip[END_REF] . [START_REF] Mundutéguy | Perception et anticipation du comportement d'autrui en situation simulée de conduite automobile[END_REF] confirm this functioning in the context of driving a car: a schema of the situation is activated from a set of sensory cues detected in the environment. An efferent copy of the expectation component is made in order to be compared with the objective reality; this is called a corollary discharge. In order to validate the chosen representation, an oriented search for cues is carried out. The schema constitutes an active means of recognition in itself: once activated, it guides and orientates the search for information in order to validate itself [START_REF] Amalberti | La conduite de systèmes à risques[END_REF]). The schema is validated when all of its contents in the situation are determined, but the mechanisms provide an opportunity to address any existing gaps by values that are, by default, seemingly realistic.

The teleonomic component of the schema is submitted to two forms of monitoring [START_REF] Amalberti | La conduite de systèmes à risques[END_REF]): an external monitoring, involving the physical process and the situation, and an internal monitoring, involving the cognitive actor of the process. In case of a problem (negative self-evaluation of the performance), the internal supervision makes it possible to increase the cognitive load in order to adjust the selected mental model: enrichment, adjustment or even construction of a new solution. This metacognitive monitoring is also in charge of arbitrating the following processing: intensity, priority, stopping.

On a more global scale, the Cognitive Architecture of Dynamic Control model described by [START_REF] Hoc | Diagnostic et prise de décision dans les situations dynamiques[END_REF] illustrates the possibility for the operator to open several cognitive loops at the same time, which project to different levels of temporal depth: a knowledge base is partially activated through the current representation, and partially mobilized at an unconscious level by an activation lattice controlled by this representation. The performance, models and anticipations are continually self-evaluated. The operator's cognitive limits results in compromises with regard to the possible corrections, function of the available resources and different requirements related to the task. Three loops take place within three different temporalities, three distinct ambitions to make corrections, from automatic control to the complete reconsideration of the current representation. As presented earlier, the attentional supervisor is responsible for both filtering and weighting the sensory inputs as well as a potential local change of the level of control of the action.

In line with the work presented earlier, two alternatives are available to design an efficient tool: help to choose the right plan, or help to validate it. A fundamental paradox appears at this stage: helping the operator to anticipate basically makes sense in the case where a representation of the situation is too terse. The paradox resides in the fact that to automatically provide help would result in giving him precisely this representation. In fact, this is another argument in favor of the direction followed until now: the operator is at the center of the decision loop. The potential additional information that we could give him would help him to construct or complete his representation but is not, in any case, intended to replace it.

Conclusion

In this bibliographical work, we have established that anticipation is a metacognitive process aimed at optimizing cognitive resources, based on situation awareness and time management. Its operation in a closed loop opens opportunities for constructing an HMI aide for anticipation. In the ASAP project, the design method is centered on the final user: the understanding of its cognitive functioning in terms of anticipation makes it possible to envisage the design of the tool while keeping the end-user in mind. This bibliographical review opens up two development directions: first, contextualization in the aviation industry; this will involve breaking down the tasks constituting a defined flight phase based on their relationship with anticipation. This formalization will allow the creation of a knowledge base in order to fuel future HMI prototypes. Second, the definition of the optimal span of anticipation in a flying task: this will involve defining the window of time within which the help information given for anticipation will improve the performance at a locally minimal cognitive price.