Martial Razakatiana

Christophe Kolski
email: christophe.kolski@uphf.fr

René Mandiau
email: rene.mandiau@uphf.fr

Thomas Mahatody
email: tsmahatody@gmail.com

Game Theory-based Human-Assistant Agent Interaction Model: Feasibility Study for a Complex Task

Keywords: Human-computer interaction (HCI), Interaction design, Collaborative and social computing Human-Computer Interaction, assistant agent, game theory, control task, context sensitivity, road traffic

As road traffic is becoming increasingly dense, new needs in terms of intelligent human-machine interaction are emerging for their control by human operators. One avenue of research consists in assisting them in their control task by an assistant agent. This paper presents a feasibility study in this field, involving interactions between humans and an assistant agent. For this purpose, a game theory-based model is proposed in order to be able to model a context-sensitive system for the cooperative realization of complex tasks. In this case, the participants of the game are human operators and an assisting agent interacting within the framework of the realization of a control task. Thus, each participants can choose an action between two possible ones (to cooperate or not). Then, the proposed utility functions allow to build the context-sensitive payoff matrix at each observation cycle of the human-machine interaction. To validate our model, we have implemented a simulated control situation; it concerns the regulation of traffic through intersections; this involves two human operators and an assistant agent. Thus, the assistant agent uses the game payoff matrix for its decision-making in using Nash equilibrium. This paper describes a feasibility study, focusing on an analysis of the results obtained during the execution of the simulation. Different research perspectives arise from this study in order to improve and generalize the proposed model.

Introduction

Since the progressive exploitation of artificial intelligence in human-machine interaction since the late seventies [START_REF] Edmonds | Adaptive Man-Computer Interfaces[END_REF][START_REF] Grudin | AI and HCI: two fields divided by a common focus[END_REF][START_REF] Kolski | Cross-fertilisation between human-computer interaction and artificial intelligence[END_REF], research on the so-called adaptive or intelligent human-machine interaction now induces a sub-domain dealing with humanagent interaction. In particular, intelligent agents are taking an increasingly important place in the design of intelligent interactive, recommender or assistance systems. In more and more cases of practical uses, it becomes possible for these agents to take initiatives [START_REF] Michael Wooldrige | Intelligent agents: theory and practice[END_REF].

In the case of cooperation with human beings, in carrying out complex tasks, several interaction models and assistant agents have already been proposed. For example, Levin and colleagues propose a stochastic interaction model for assistant level decision making [START_REF] Levin | A Stochastic Model of Human-Machine Interaction for Learning Dialog Strategies[END_REF]. In [START_REF] Pynadath | A Markovian Method for Predicting Trust Behavior in Human-Agent Interaction[END_REF], the authors use a markovian model for predicting human trust behaviour in the framework of human-agent interaction. An interaction model is proposed in [START_REF] Barange | Pedagogical Agents as Team Members: Impact of Proactive and Pedagogical Behavior on the User[END_REF], with the objective of describing intelligent agents that can teach human operators working in groups. In an assistance framework, Azaria et al. propose a so-called social agent which can give advice to a human being in case the decision making seems complicated [START_REF] Azaria | Strategic advice provision in repeated human-agent interactions[END_REF]. To our knowledge, among the approaches of artificial intelligence using game theory [START_REF] Von Neumann | Theory of games and economic behavior[END_REF] and the notion of Nash equilibrium [START_REF] Nash | Equilibrium points in n-person games[END_REF], there is not yet an approach directly involving several human beings (our first work focusing on a single human [START_REF] Razakatiana | Human-agent Interaction based on Game Theory: Case of a road traffic supervision task[END_REF]) and one or more assistant agents, as players, with a view of intelligent human-machine interaction. Thus, the participation of human beings in the game, in the sense of game theory, is not yet formally expressed in the literature for this purpose of interaction.

This paper focuses on a human-agent interaction model based on the definition of a repeated (or iterated) multi-player game. This paper is structured as follows. The second section first sketches a state of the art on human-agent interaction models. Then it addresses the problem of applications using agents for road traffic control; indeed such domain is considered in the feasibility study. In the third section, an interaction model based on game theory is proposed, explaining the formulation of the Nash equilibrium search. The fourth section presents a traffic simulator involving two human operators and an assistant agent, who collaborate for the purpose of traffic light control. This is implemented thanks to the possibility of participatory simulation in Netlogo. The analysis of the simulation results is presented in the fifth section; it illustrates the effectiveness of the proposed interaction model. The paper concludes with a discussion, a conclusion and research perspectives.

State of the art

This state of the art focuses primarily on human-agent interaction in a complex task setting. It then focuses on the exploitation of agents in traffic simulation; indeed this application field is used for the instantiation and validation of the interaction model proposed in this paper.

Human-agent interaction models

2.1.1 Agent-based approaches. When performing a complex or sometimes dangerous task, collaboration and cooperation between artificial agent or software agent and human operators proves to be a promising solution. For example, in the case of industrial process supervision, Le Strugeon et al. [START_REF] Le Strugeon | Intelligent agents' concepts for computer-supported collaborative work systems in complex industrial systems[END_REF] propose an intelligent agent model to assist a human operator in a collaborative way to optimize industrial process control. In addition, Badeig et al. [START_REF] Badeig | Analyzing multi-agent approaches for the design of advanced interactive and collaborative systems[END_REF] describe the construction and design of a collaborative system using a multi-agent model. To improve the efficiency of assistance, Mandiau et al. [START_REF] Mandiau | A new approach for the cooperation between human(s) and assistance system(s): a system based on intentional states[END_REF] propose an assistance approach based on the agent concepts of beliefs and intentions. In [START_REF] Holzinger | Interactive machine learning: experimental evidence for the human in the algorithmic loop: A case study on Ant Colony Optimization[END_REF], the authors propose a model of human-agent interaction with the objective of improving artificial intelligence with the support of human business expertise. For this goal, they use the iML interactive learning machine technique based on a principle of ant colony optimization (with possible human influence on constitutive agents). Finally, a recent study proposes a formal definition of explicability in the context of human-agent interaction using Machine Learning techniques. This approach takes into account information from the context, in particular the user's workload level and performance [START_REF] Rosenfeld | Explainability in human-agent systems[END_REF].

2.1.2 Game Theory Approaches. In our previous work, we proposed a first interaction model between a human and an assistant agent [START_REF] Razakatiana | Human-agent Interaction based on Game Theory: Case of a road traffic supervision task[END_REF]. Moreover, in [START_REF] Kraus | Human-agent decision-making: Combining theory and practice[END_REF], the authors use the game theory approach to study a strategic behavioural interaction between a human and an autonomous agent. In fact, this work focuses on the agent's behaviour using two principles: negotiation and deliberation. Regarding human-robot interaction, in [START_REF] Li | Differential game theory for versatile physical humanrobot interaction[END_REF], the authors exploit the game theory model to develop an interactive robot controller capable of physically interacting with one human being. In addition, game-theoretical approaches are proposed in various application domains, such as the Internet of Things (IoT) and Bitcoin technology, in the case of resource and task sharing (see for example [START_REF] Liang | A Survey on Game Theoretical Methods in Human-Machine Networks[END_REF]). However, human participation and context awareness are most often not explicitly addressed.

2.2 Agents in our case study: road traffic control 2.2.1 Agent-based approaches. Several works deal with the study and simulation of road traffic in terms of agent-based modelling. For example, in [START_REF] Doniec | Controlling no-normative behaviors by anticipation for autonomous agents[END_REF], the authors exploit the advantage of using the anticipatory behaviours of agents in multi-agent coordination for the simulation of traffic at intersections. In [START_REF] Guériau | A constructivist approach for a self-adaptive decisionmaking system: application to road traffic control[END_REF], a constructivist learning control model is proposed to facilitate traffic regulation. For traffic flow optimization, Ksontini et al. [START_REF] Ksontini | Representation of the Agent Environment for Traffic Behavioral Simulation[END_REF] propose a model based on an egocentric representation of the traffic environment that exploits the heterogeneity of vehicles and the drivers' behaviours. In [START_REF] Benhamza | Agent-based modeling for traffic simulation[END_REF], the authors propose a reactive agent model for traffic microscopic simulation. Vith a view of organization of traffic light control, [START_REF] Kumar | Self-Organizing Traffic at a Malfunctioning Intersection[END_REF] describe a selforganizing control in case of intersection signals's failure. In order to improve traffic control, [START_REF] Juan | History-based self-organizing traffic lights[END_REF] introduce a control model based on traffic history. With the same objective, an agent-based modeling is also proposed by [START_REF] Hirankitti | An agent approach for intelligent traffic-light control[END_REF], using inference engines taking in input values related to temporal traffic situations. Finally, [START_REF] Almejalli | An Intelligent Multi-agent Approach for Road Traffic Management Systems[END_REF] propose a division of the global traffic network into a set of sub-networks in order to facilitate the control task by implementing a multi-agent system. To conclude, a brief (but not exhaustive) state of the art of multi-agent road traffic models has been presented. But the involvement of human operators in these agent-based or multi-agent approaches has not yet been explicitly formalized in the literature.

2.2.2 Approaches using game theory. Some work focuses on the application of game theory for the organisation and simulation of road traffic. This is the case in ARCHISIM platform [START_REF] Mandiau | Behaviour based on decision matrices for a coordination between agents in a urban traffic simulation[END_REF]: game theory has been proposed for resolving conflicts between vehicles at intersections. A two-player game model has been proposed in [START_REF] Thanh Thrinh | Towplayers Game-theory-based Analysis of Motorcycle Driver's Behavior at a Signalized Intersection[END_REF] in the case of conflict resolution between two motorcycle drivers. However, these approaches consider only vehicle or vehicle-driver participants. No game-theoretical approach explicitly involves one or more human operators and assistant agents interacting in a traffic control task.

3 Proposal of an interaction model exploiting the principle of game theory for the purpose of assistance

The game theory-based interaction model can be started with the case where an assistant and a human being (i.e. a human operator) interact. However, it is possible to propose a generalization of the model towards an interaction between several agents and several human beings (users). In this paper, we illustrate the proposed approach with one human and one agent, then two humans and one agent. Let us recall that to be able to define a game [START_REF] Von Neumann | Theory of games and economic behavior[END_REF], it is necessary to specify at least the following elements: (i) the participants in the game who are called players, (ii) the rules of the game, (iii) the possible actions for each of the players, (iv) the utility functions allowing to calculate the gains of all the participants for the creation of the game payoff matrix. For a given game, a combination of the possible actions of all players is called a strategic profile. Thus, game theory modeling consists in finding the solutions of the game. The most commonly used approaches are the search for the best answers and the search for equilibrium. For this feasibility study, we will use the Nash equilibria [START_REF] Nash | Equilibrium points in n-person games[END_REF].

Formalization of multi-player game with two possible actions for each player

For formalization, we can consider the case where several human beings (users) interact with one or several assistant agents. In the following, we choose to use the C & D usual notation (coming from the prisoner's dilemma, stated in 1950 by the mathematician Albert W. Tucker) for the actions of the different entities: C for Cooperate, D for Defect (knowing that in our case, D actually means for an agent that it chooses not to cooperate, cf. below).

For human beings, we choose the following notations:

1. The human being asks the assistant for help : C. 2. He or she does not require any help: D.

Concerning the assistant, it can respond to the request of one of the human beings (if there is at least one request) or inform/warn human beings of important information if there is no request: C.

The assistant can also let human beings work autonomously [which is also in line with the principle of learning by doing [START_REF] Dufour | Learning by Doing: A Handbook for Professional Learning Communities at Work[END_REF]:

D.
It is important to note that, in practice, there are other possible actions than: to cooperate and not to cooperate. These two actions have been chosen to facilitate the search for equilibrium (using Nash equilibrium) and adaptation with a view to a generic assistance system.

Thus, Figure 1 shows an example of a graphical representation of possible cases. Hi is one of the human beings involved and A1 designates an assistant. Thus, the outcomes of the game are the paths: CCC, CCD, CDD, DCD, etc. For example, CCC means that the two human beings H1 and H2 simultaneously ask for help and the assistant intervenes. In the decision-making process, the assistant has one of the following strategies at its disposal:

1. To act or not to act randomly (without intelligency) 2. To act or not, while trying to maximize its gain for each move in the game (the assistant is selfish [START_REF] Eshel | Altruists, Egoists, and Hooligans in a Local Interaction Model[END_REF]).

3. To act or not to act, while considering the maximum gain for human beings (it is more altruistic [START_REF] Brainov | Altruistic Cooperation between Self-interested Agents[END_REF]) with human beings). 4. To act or not to act according to Nash equilibrium [START_REF] Nash | Equilibrium points in n-person games[END_REF] considering the optimal gain for everyone.

Payoff of human beings and assistant agent

3.2.1 The Human beings's payoff. If a human being asks for help, his or her gain depends on the responses of the assistant agent and the choices of other humans: his or her gain decreases with his or her workload level. This gain also decreases with his or her profile because the higher experience level and the lower workload, the less the human being should ask for help from the assistant agent (who should then be able to take care of other tasks for example). Thus, the gain of each human also depends on the other human beings: if the human asks for help, his or her gain increases with the sum of the workloads of all humans, and with the criticality level. But the gain decreases with his or her experience level. If the human being is autonomous, his or her gain decreases with the intervention of the assistant and especially if he or she does not have a high level of workload. But in fact, the criticality level can significantly influence the choice of the human being, to be autonomous or not.

3.2.2 The assistant's payoff. The assistant(s)'s payoff depends on the choices of the human beings. In addition, the numerical values of the payoff take into account the information relevant to the user context, i.e. the human's workload and the experience level. The assistant is an intelligent agent capable of intervening if human beings need help. In terms of game theory, the agent and the human beings who need help, share the same payoff value, because if the assistant intervenes, it justifies by its payoff that its intervention is necessary; this payoff also increases with the criticality level of the situation. In case of non intervention, the values can be chosen according to two cases: (1) When human beings do not need help, the assistant agent must not react (i.e. respond to their request) at the risk of unnecessarily wasting time (which could be spent on other tasks); (2) When humans have asked for help (and the assistant does not intervene), the assistant's payoff may increase because it has much more time to deal with other tasks, but it will not be very useful from the human point of view [START_REF] Maes | Agents that Reduce work and information Overload[END_REF], especially if the human really needs the help because of a high level of workload, for example.

Proposal of utility functions to build payoff matrix at each iteration during the task

According to the principles related to payoff, stated above, we can propose the following functions in the context of the creation of the game payoff matrix for the case of an assistant agent and m human beings: 3.3.1 Calculation for Assistant agent gain. If the assistant has reacted (i.e. it plays C):

11 ( , ) mm A j h i i h ii u C x c wl usrl d crt       (1)
with:

crt designates the criticality level of the situation, according to [START_REF] Purucker | Comparing the Perception of Critical Longitudinal Distances between Dynamic Driving Simulation, Test Track and Vehicle in the Loop[END_REF] ch means the number of human beings requesting assistance.

dh means the number of human beings who are self-sufficient: no need for assistance.

wli refers to the workload level of a human being of index i [START_REF] Kirwan | Human factor in ATM system design life cycle[END_REF].

usrli is the experience level of the user of index i [START_REF] Hall-Ellis | The Dreyfus Model of Skill Acquisition: A Career Development Framework for Succession Planning and Management in Academic Libraries[END_REF].

m is the total number of human operators.

In this formula, the assistant's payoff increases with the number of humans requesting assistance, and with their workload level. This shows the importance of the assistant's intervention. However, this gain decreases with the sum of their experience's levels because if all humans are experts, the assistant does not need to intervene.

If the assistant leaves it up to the human operators to work autonomously (D):

11 ( , ) mm A j h i i ii u D x c usrl wl    (2)
Here, the assistant's gain increases only with the sum of the values of the human experience levels. But the assistant's payoff decreases with their workload level, and especially with the criticality level (because non-assistance can lead to a loss).

with: ca the number of assistant agents which choose act, da designates the number of assistant agents which choose not to intervene (i.e ca , da {0,1} in our case); k x are the actions for other players (the assistant and non-humans of index k).

In this formula, the human being's payoff asking for help increases with the sum of the workloads of all the human beings. But it decreases with this human being's experience level (here indexed by k).

If the k-index human being is autonomous (D):

1 ( , ) ( ) k m H k a i k k i u D x c wl usrl crt wl        (4)
with: Hk refers to the human of index k. and usrlk the experience level of k-index human being.

In this case, the gain decreases with the sum of the workloads of all human beings. And since the human being (of index k) is autonomous, it is only his or her experience level that will have a positive impact on the value of his or her payoff. However, with a high criticality level and a high workload level, his or her payoff should decrease accordingly.

If the agent acts, the payoff of humans asking for help are greater than the payoff of those who are self-reliant. If the agent does not respond, the payoff of humans asking for help are less than the payoff of autonomous humans. If the human is autonomous, the payoff of the agent in case of intervention is lower than in case of non-intervention. If the human asks for help, the intervening agent's payoff is higher than that in case of non-intervention.

Total payoff calculation over N iterations

As we have considered a repeated game of several players, we propose to calculate each player's payoff by the following formulas:

For human beings:

1 1 ( , ) kk N H H k k i i G u x x N    with: k H u
designates the utility function of each human operator. For the assistant: These formulas make it possible to evaluate each player's payoff by studying the Nash equilibrium of each iteration. For the assistant, we have assumed that it takes charge of other tasks apart from assisting the human operator(s).

1 1 ( , ) N A A k k i i G u x x N    with: ( , )

Equilibrium search

After building the utility functions for the players, we will now proceed to the resolution of the game. The Nash equilibrium in pure strategy corresponds to a situation in which none of the players has an interest in unilaterally changing the strategy [START_REF] Nash | Equilibrium points in n-person games[END_REF]. However, this equilibrium does not always exist. Alternatively, there is also another type of equilibrium called mixed-strategy equilibrium which uses a probability distribution over the set of possible actions for each of the players to determine the equilibrium.

Our formulation leads to a non-zero sum game, i.e. the sum of the payoffs of the different players is not equal to 0. In our case, the following conditions must be met for a strategic profile to be a Nash equilibrium in pure strategy. Let a strategic profile rated s* such that:

    * * * * * * * * * 1 2 1
, , , , , , *, ,

k k m A k k A s s s s s s s s s s       So, s* is a Nash equilibrium in pure strategy if [1, ] im          * * * * * * 21 * * * * * * 2 1 1 1 1 , ,..., , , , , , , , , , , , ii H
H i m A k ii A kA m A u s u s s s s s s s s u s s s s s s u           
with: si* designates an action performed by the human of index i at the equilibrium, si for any action of the latter. Thus, sA* an action performed by the assistant at the equilibrium, and sA designates any action of the assistant.

It would be possible to choose other methods. But Nash equilibrium has been chosen particularly as we are considering an assistant agent (considered rational and cooperative) which can take over other occupations: it can become an assistant for one or more other tasks. Thus, the Nash equilibrium allows to obtain an optimal gain for each participant (humans and agent). For Nash equilibrium search, we use the algorithm described in [START_REF] Shoham | Multiagent Systems: Algorithmic, Game Theoretic and Logicial Foundations[END_REF]. If the algorithm does not find a Nash equilibrium, we have taken by default the action D for the assistant agent.

Feasibility study: road traffic control task involving one and then two human operators assisted by an assistant agent

Traffic control is one of the complex tasks that can involve human beings, who have to deal with regulation at every intersection. In this feasibility study, we use Netlogo to simulate the road traffic control environment. Netlogo is an open source simulation plateform created by Ury Wilensky [START_REF] Wilensky | NetLogo HubNet Gridlock Alternate HubNet[END_REF][START_REF] Wilensky | An introduction to agent-based modeling: Modeling natural, social, and engineered complex systems with NetLogo[END_REF]. It is easy to use in modelling and simulation of complex systems, such as natural, social or engineering systems. Netlogo uses an agentbased approach.

The assistant agent is intended to assist the human being(s) in carrying out these control tasks with two actions: to take special care of an intersection or to leave the task to the human being(s) for the control of the traffic lights located at each intersection.

To this end, certain rules of expertise could be evoked to justify the importance of the intervention or not of the assistant agent through each of the intersections:

1. It can search for an intersection that has the maximum number of vehicles waiting to enter it (for example, vehicles stopped by the red traffic lights in front of them). In this case, it can change the signal control (i.e. colours of the lights) in this intersection.

2.

It can select an intersection that has the minimum number of vehicles waiting to enter the intersection (stopped by the red traffic lights).

Description of the participative traffic simulation application under Netlogo

The hubnet server allows a so-called participative simulation [START_REF] Wilensky | Learning through Participatory Simulations: Network-Based Design for Systems Learning in Classrooms[END_REF]. In this simulation, clients (human operators) can participate in a cooperative way in the management of traffic lights in order to obtain maximum fluidity.

The three participants (later referred to as P1, P2 and P3) in this first feasibility study are undergraduate students in physics or economics (average age: 25 years; two men and one woman).

The main selection criteria for choosing participants are: Have good eyesight, be able to use a computer, be able to work in a team, not be subject to dizziness (which can be caused by vehicle movements on a screen). All the people solicited in the framework of this first feasibility study agreed to take part in it, but the health situation related to Covid-19 did not make it possible to have more participants (no access to the buildings).

In addition, an external observer (evaluator) is appointed for the initial configuration of the traffic model by setting the speed limiter, number of vehicles, etc. The observer also sets the duration of an iteration, starts and stops the simulation. For the implementation, the protocol in Figure 2 is followed.

Figure 2. Protocol followed by participants

In terms of functionalities, the use case diagram visible in Figure 3 shows the different types of actors: human operator, simulating a road traffic controller (one, then two human operators); assistant agent; external observer (evaluator).

Hubnet client description

On the client's user interface, the human being connected to the simulator server has the choice to take the initiative on an intersection he or she can choose: he or she can change the corresponding traffic lights or wait for the help of the assistant agent set up to help them control the lights of the whole traffic. Thus, each human operator can also consult information about the workload level of the other operators and their experience levels; this information may help his or her decision-making. For example, it can be seen that the workload level of the other human is equal to 1 (very low workload) and his or her experience level is equal to 5 (very high level).

In addition, humans can control the maximum vehicles's speed limit. They can choose the intersection concerning the lights they want to intervene on.

5 Results of the feasibility study with 1 agent and 1 human then 2 humans

An initial feasibility study has yielded a set of promising results. These make it possible to highlight the impact of decisions taken by players on the overall traffic behaviour. This impact is in terms of average traffic speed, number of vehicles waiting, evolution of the average waiting time of vehicles.

The proposed simulator has been used in situations involving 200 vehicles to give meaning to the cooperation between human beings and the assistant in the context of traffic control using traffic lights.

Each participant used the Netlogo simulator for about 1 hour and 30 minutes. This period included a series of simulator runs, with short breaks in between (to approximate working conditions in a control room [START_REF] Ivergard | Handbook of Control Room Design and Ergonomics: A Perspective for the Future[END_REF]). For this feasibility study, an execution lasts 180s for a number of iterations equal to 60, to obtain the following results: Average speed, number of vehicles waiting and vehicle waiting time for both cases: (1) one human being and one assistant, (2) two human beings and one assistant. The curves below show usage sequences of 60 cycles only (for good readability of the curves presented for representative purposes, in relation to this feasibility study). These sequences occur after approximately one hour of use; each participant had the opportunity to master the commands of the user interface.

Numerical Results

Figure 5 shows the average vehicle speeds calculated for each cycle in the simulation. At the beginning of the simulation, all the vehicles move with a random initial speed, and a peak is obtained for the average speed. Afterwards, due to the traffic density, a decrease in the average speed will be observed. Indeed some vehicles have to stop or slow down depending on the situation of the vehicle ahead: presence of a red traffic light or waiting for a vehicle that crosses the intersection first. In Figure 5, v_moy_1_1 refers to the evolution of the average speed of vehicles in the case of one human being (P1) and one assistant agent; v_moy_1_2 refers to the case of two human beings (P2 and P3) and one assistant. For the two simulation cases mentioned above, Figure 6 shows the evolution of the total number of vehicles stopped during each of the cycles. After the start of the simulation, almost all the vehicles are running at initial speeds, which justifies the low value of this number at the start. In Figure 6, vehicule_stop_1_1 refers to the number of vehicles stopped in the case of one human and one assisting agent; vehicule_stop_1_2 refers to the case involving two humans and one assisting agent.

Figure 7 shows the cumulative waiting time of vehicles during the 60 cycles. Wait_time_1_1 shows the evolution of the waiting time for vehicles at each iteration (case involving one human being and one assistant agent); wait_time_1_2 refers to the case involving two human beings and one assistant.

Qualitative results

The answers of the 3 participants to the questionnaire after the performance of the control task (during 60 iterations) assisted by the agent, are visible in Table 1. For evaluation purposes, each of the participants answers the question on the usability of the proposed simulator, the effectiveness of the agent assistant in assisting and the opinion on the mode of assistance. Each response is measured using the 5-value Likert scale [START_REF] Likert | A Technique for the Measurement of Attitudes[END_REF]. The values are 1 (Strongly Disagree), 2: (Disagree), 3 (Neither Disagree nor Agree), 4 (Agree), 5 (Strongly Agree). The first results regarding participants' opinions are provided in Table 1.

They show a first positive trend, which is promising for this first feasibility study. This will have to be confirmed in the context of other studies involving more participants. Several first impressions of the participants can be seen below:

« I think it's a good traffic simulator. In terms of performance, I've seen that the assistant offers effective help in controlling and detecting intersections with a lot of vehicles waiting to enter them. It really is a cooperative traffic management assistant » (Participant P1).

«I found the simulator easy to operate. About assistance, I found that the assistant acts efficiently on the most overloaded intersections when necessary. I have the impression that it can quickly determine the number of vehicles stopped at the entrance of the intersections to be prioritized and act on the traffic lights » (Participant P2).

« I think the simulator gives you some ideas about road traffic. I have the impression that the assistant acts appropriately when my workload is high. However, there is a limit in some intermediate cases where it does not intervene, even when my workload is high and the other person is not» (Participant P3).

Discussion

The discussion is focused on two points.

Equilibria considering only the intervention of the assistant

The Nash equilibrium proposes an intervention for the assistant in each iteration. In the case where the assistant agent takes over the control of all the junctions, the obtained results show a certain efficiency in the management of the junctions, in particular in terms of average traffic speed, number of vehicles stopped. At each iteration, the assistant is, therefore, obliged to act because the equilibrium allows it to intervene. To do this, the agent will consider successively during the same cycle, in its area of occupation, the number of vehicles stopped for each intersection in descending order: the assistant must first take care of the intersection or intersections with the maximum number of stopped vehicles. Thus, the number of interventions coincides with the number of iterations (remember that we have considered a number of iterations equal to 60).

Equilibria with or without the intervention of the assistant

The feasibility study shows us the interaction between an assistant agent and one and then two human operators in traffic control. Here, the assistant agent uses the Nash equilibrium principle [START_REF] Nash | Equilibrium points in n-person games[END_REF] for decision making on all intersections (including those occupied by human operators). Simulation sequences over 60 cycles have been carried out, several of which have been illustrated to show the feasibility of the approach. Such a sequence duration allows to illustrate different interventions of the assistant agent, the human(s) involved, and the consequences of the interventions on traffic.

Thus, in the sequences selected and illustrated above, the assistant intervenes from iteration 10 to 35 (for the case: one human and one assistant), and 25 to 50 (for the case: two humans and one assistant) (Figure 9). There is stability in the average traffic speed (Figure 5). Between iterations 35 and 40 (for the case: one human and one assistant) and 50 to 60 (for the case: two humans and one assistant) (Figure 9) during which the assistant has calculated the Nash equilibrium: the human beings have a lower workload level and with a high of expertise, the assistant does not act (given the equilibrium) hoping that the human beings have the possibility to act. In this period, the assistant does not intervene and there is a decrease in the average speed, despite the effort of the human beings (Figure 8).

Always considering Nash equilibrium, an optimization of traffic is also observed if the human beings have a high level of workload with a slightly lower expertise level: hence the importance of the assistant's intervention.

Finally, it can be noted that the traffic results do not depend on the number of iterations. We can go up to several hundreds or even thousands of iterations. Effectiveness depends essentially on the interventions of the human beings and the assistant throughout the execution, while taking into account the initial characteristics of the traffic.

Conclusion

In this paper, we proposed a game-theoretical model of humancomputer interaction, involving an assistant agent and several human beings (two in the described study) who interact in a given interactive environment. This model supports decision making from the perspective of assisting, or not, based on information coming from the context, of human operators. The feasibility study was carried out on two cases: one human and one assistant agent; two humans and one assistant agent. These two cases concerned a complex traffic control task. They show the feasibility of the approach, and also give an insight into the effectiveness of the assistant agent. If the assistant does not intervene, a degradation of the traffic can be observed.

A first research perspective concerns the setting up of two user studies: the first with about ten participants for the case involving a single human; the second with about ten groups of participants for the case involving two humans and an agent. In these studies involving more participants, it will be possible to analyse their behaviour in detail. Another perspective concerns the explanation of the efficiency of the agents linked to a measure of satisfaction of human beings in terms of assistance. This measure can also be related to the principle of context awareness in the context of assistance. Finally, the comparison with other adaptation approaches, as well as with other agentbased approaches for traffic management, will be also envisaged.

Figure 1 :

 1 Figure 1: Graph showing the outcome of the game (case of two humans and one assistant agent)

3. 3 . 2

 32 Calculation for Human (Hk) gain. If the human of index k (called Hk) asks for help (C):

  in the i-th iteration and A u the utility function of the assistant agent.

Figure 3 :

 3 Figure 3: Use Case Diagram Figure 4 shows a screenshot used by participant P2 (case of two humans P2 and P3 involved in the traffic control task). Each participant is connected via a Hubnet client. Each client displays a user interface with a set of commands to intervene on road traffic.

Figure 4 :

 4 Figure 4: Hubnet Client interface (participant P2)

Figure 5 :

 5 Figure 5: Evolution of the average speed of the vehicles

Figure 6 :

 6 Figure 6: Evolution of number of stopped vehicles

Figure 7 :

 7 Figure 7: Evolution of average vehicle waiting time over iterations Concerning the number of interventions (expressed, for example, by the number of colour changes at traffic lights), Figure 8 shows the evolution of the number of interventions by human beings (in both cases considered) while Figure 9 shows the evolution of the number of interventions by assistant agents.

Figure 8 :

 8 Figure 8: Evolution of the human's intervention numbers In Figure 8, interv_hum_1_1 refers to the evolution of interventions of human beings in the case of one human being (P1) and one assistant agent; interv_hum_1_2 refers to the case of two human beings (P2 and P3) and one

Figure 9 :

 9 Figure 9: Evolution of the intervention of the assistant agent depending on the Nash equilibria In Figure 9, interv_ass_1_1 refers to the intervention of the assistant agent in the case of one human being (P1) and one assistant agent; interv_ass_1_2 refers to the case of two human beings (P2 and P3) and one assistant. The description of the interventions of the assistant and the human beings during the 60 cycles is as follows: 35 interventions of the assistant and 12 for the human beings (case: 1 assistant, 2 human beings); 45 interventions of the assistant and 4 for the human being (case: 1 assistant, 1 human being). This can be explained by the fact that with only one human being, the assistant has to increase the frequency of the interventions in order to contribute to the fluidity of the traffic.

Table 1 :

 1 Opinions of the three participants involved (Case 1-1: one human being (P1) and one assistant agent; Case 1-2: two human beings (P2 and P3) and one assistant).

ACKNOWLEDGMENTS

The authors thank the participants in the feasibility study. They thank also the anonymous reviewers for their numerous constructive remarks.


E Le Strugeon
email: strugeon@univ-valenciennes.fr

C Kolski
email: kolski@univ-valenciennes.fr

R Mandiau
email: mandiau@univ-valenciennes.fr

M Tendjaoui
email: tendjaoui@univ-valenciennes.fr

INTELLIGENT AGENTS CONCEPTS FOR COMPUTER-SUPPORTED COLLABORATIVE WORK SYSTEMS IN COMPLEX INDUSTRIAL SYSTEMS -RESEARCH DIRECTIONS

Keywords: Distributed Artificial Intelligence, Computer Supported Collaborative Work, Interaction, Communication and Information sharing in complex industrial systems

Cet article s'intéresse à la contribution de concepts d'agents intelligents pour la mise en oeuvre de collecticiels dans les systèmes industriels complexes (nucléaire, espace...) où des agents humains peuvent coopérer avec différents agents logiciels, "intelligents" ou non, dans des situations très différentes. Une caractérisation globale des systèmes hommesmachines industriels complexes est d'abord donnée en insistant sur le contexte coopératif.

Puis, plusieurs directions de recherche sont proposées concernant la notion de modélisation d'organisation en vue de la mise en oeuvre de CSCW dans les systèmes industriels complexes, sous l'angle de l'Intelligence Artificielle Distribuée.

Introduction

In this article, we are interested in developing CSCW systems for control rooms to supervise and control complex industrial systems (chemical, nuclear power, energy nets, transports, …). This domain offers a large study field in reason of the complexity of the tasks that are entrusted to the humans, the diversity of criteria required to achieve these tasks (linked to the production, economy, quality…), and the variety of possible computer-supported assistances that can be given to human agents. Few studies have been made until now in this perspective.

In that context, this article is made of three parts. In the first part, we characterize globally the complex industrial systems in stressing on the cooperative context. The second part gives some research directions towards a global model of the human-machine system in control room with the help of multiagent concepts. Finally, the last part gives research lines related to the notion of organisation modelling to realize CSCW systems in complex industrial systems.

I. Complex industrial systems characterization

WOODS (especially in [Woods 88]) has underlined the difficulties of a global definition of a complex industrial system. Indeed, he has shown the difficulty of modeling the capacities of assistance tools, as well as the capacities and resources of human agents. We can consider the development of tools able to plan, and even to adapt themselves, to dynamic functionings, even if numerous research works show that planning is still a crucial and difficult problem. One can notice, however, that the applicability of these tools (which are mostly AI tools) is a new challenge in the study of the humanmachine "couple's behavior". That challenge, for researchers from different fields (cognitive ergonomists, psychologists, sociologists, computer scientists, automatists...), concerns the study of cooperation among intelligent entities. Such a study leads naturally to determine the cooperation actions that allow to reach optimal performances of the whole system.

In this scope, cooperation implies two essential elements, that are (1) the specific cooperative context of the application, (2) the cooperative structures gathering the participants.

I.1. The cooperative context

Following the technological evolution, the human agents in control room have to achieve more and more complex problem solving tasks, with the means of graphical tools, and this in dynamic and specific demanding situations [Van Daele 92] [De Keyser 92]. They work in a complete physical disconnection from the devices. Especially, they solve, alone or collectively, complex problems including human-computer or computer-computer negotiation and cooperation needs [Reinartz & Reinartz 89] [Brehmer 91] [Schmidt 91] [Heath & Luff 92]. The means put at the teams' disposal play a basic role in the efficiency and reliability of the human-machine system. Demanding a high knowledge level, the human tasks in complex systems have been classified by ROUSE [Rouse 83] in four main categories:

• The transition tasks: they are linked to the system's rate changes (stop, start, changes of functioning points). The human-machine interface must then allow the members of the team to do pre-established procedures to appreciate their effects on the system and to supervise the evolution of it at every time.

• The control and supervising tasks: the human-machine interface must make the system state supervision easier to the team, in the aim to detect and anticipate abnormal event outbreaks and to optimize the production by sharp adjustments.

• The default detection and diagnosis tasks: by alarms settings and/or the possibility to observe some variable of the system's abnormal evolution, the members of the team must be able to detect the defaults and to make his diagnosis accordingly. Then, the human-machine interface must necessarily enable an easy underline of the causes/effects relations among the system variables.

• Compensation or correction tasks: to restore the system normal functioning state, the interface must help the members of the team to decide the actions to achieve and to display their effects on the system.

To achieve these complex tasks, some including thousands of variables, the human agents have at their disposal mostly common human-machine interfaces, that display an information set in a graphic way. Such interfaces can be linked to assistance modules, whose reasoning is sometimes called "intelligent" when based on artificial intelligence techniques. These assistance modules can be specialized in diagnosis, prediction of defaults, alarms filters, etc [Kolski 93]. The human-machine interfaces have to synthesize the process state and assist the members of the teams during their activities. Thus, the human agents do not have a direct view on the system and on the actions results, they must build their own mental representation of the remote system functioning state and of the variables that compose it. Then, they work through the mental representation they have of the system, the representation they get especially by the human-machine interface [Norman 86] [Rasmussen 86] [Tanni et al. 94].

I.2. Cooperative structures in an industrial context

Despite many works about the human-machine cooperation notion (Cf. for example [Mandiau et al. 91], [Vanderhaegen et al. 94], [Karsenty & Brezillon 95], [Mandiau & Le Strugeon 95], [Millot & Mandiau 95]), it is still difficult to characterize sharply the notion of "cooperation" between a single operator and an "intelligent" tool (called AI tool in the following paragraphs). Of course, the difficulty is bigger when studying the cooperation aspects among several operators and AI tools. We propose to develop the cooperation notion seen from the Distributed Artificial Intelligence angle, starting with the works of SCHMIDT [Schmidt 89], who distinguishes the notions of (1) cooperation form and (2) cooperation mode.

The cooperation form puts in light the reasons why the different entities of the system cooperate.

These reasons are linked to the fact that each (human or AI) entity has bounded cognitive capacities.

Moreover, some applications require cooperation because the tasks can not be individually achieved (in reason of time, security or efficiency constraints). That is why SCHMIDT has proposed three cooperation forms, summed up in the table 1.

These cooperation forms correspond to different ways to use each entity's capacities in different aims. The integrating cooperation enables to obtain a global result from entities that have distinct and specialised tasks. It is above all a coordination problem. The debate cooperation allows to use different points of view, knowledge or strategies on a given problem. In the argument cooperation, the aim is to pool every entities' abilities to perform a task more efficiently than what could be done by each of them.

Location dimension

Proximate cooperation: the cooperating entities are at the same location Remote cooperation: the cooperating entities are in different places, implying bounds in their interactions

Time dimension

Synchronous cooperation: the tasks are simultaneously performed Asynchronous cooperation: the inter-dependent tasks must be coordinated

Organization dimension

Collective cooperation: the entities cooperate overtly and consciously to a common goal Distributed cooperation: the entities can modify their behavior and are not necessarily informed of the others' activities

Communication dimension

Direct cooperation: the entities interact by the means of verbal (speech acts) or non-verbal (moves, visual) exchanges Mediated cooperation: the entities cooperate via a shared information space (wall screens, calculators)

More recently, RODDEN and BLAIR [Rodden & Blair 91] have proposed a classification of CSCW systems along two dimensions: the cooperation form (synchronous, asynchronous, mixed) and the geographical nature of it (co-located, virtually co-located, locally remote, remote). In the case of an industrial system supervision, the cooperation form which is used is mainly of the "asynchronous" type since the actions are made in real-time on the supervised process; the cooperation acts among the members of the team are made in real-time either. Regarding to the geographical nature of the system, the supervision team may be either grouped together in a single room, or be dispatched among several rooms. Thus, it is the case of the "virtually co-located" system defined by [Rodden & Blair 91].

Computer-supported collaborative work systems have been mainly studied in office automation contexts. For this type of applications, KARSENTY [Karsenty 94] has classified the groupware systems by the functions they propose: (1) electronic mail, (2) shared editor, (3) computer-supported conferencing and meeting, (4) decision making assistance system and ( 5) coordinator aiming at facilitating the work in the group. These two last categories can be seen as really potentially including the main functions that are useful in an industrial process supervision context. Indeed, these systems' complexity comes, for the most part, from management of more than one action at the same moment on the same dynamic piece of data (or the same set of dynamic data) problems and from consistency management among the distributed handled data.

We think it is necessary to tend to a more global approach, in which the human and AI agents are situated in an organization, with strong interconnections through human-computer, computer-computer and human-human interfaces. This aspect is developped in the following part.

II. Global modeling of a work organization in a control room -research directions from the DAI point of view

Using a multiagent view in the context of a control room, we could see the team made of human operators and artificial intelligent assistants as a system of agents working jointly with a shared goal.

An agent is defined here as either a human or an artificial tool with intelligent capacities that allow it to act on its environment so as to achieve its goals.

II.1. Environment of a human agent in complex industrial systems

A human agent in a control room is the most often interacting with other human agents, and also with software agents. The figure 1 illustrates, for example, a human agent X cooperating through a human-machine interface (in which an assistance tool is embedded) with others human agents (in/out the control room), and with a responsible human agent (for example, a foreman) coordinating theirs actions. This example which may be a priori simple, underlines the lack of studies about interactions between the human agent and his interface. In fact, considering a single human agent faced to his human-machine interface is not appropriate in this context. In such environment, we have to consider the set of interactions between human(s) and machine(s).

In a complex industrial system (nuclear, space...), the recent interfaces may include one or several aid tools. These systems may be based on concepts and techniques issued from Artificial Intelligence. They may take decisions and act, in some cases, directly on the process. According to BOY [Boy 88] [Boy 95], they may be considered as intelligent "assistant agents" collaborating with the human agent to perform the task. They are more and more studied and considered as software autonomous and personal assistants. For example, [Maes 94] proposes a "metaphor of the personal assistant": the agent learns how he could assist the user and how, in some cases, it could take the place of the human agent in his interaction with the application, and this by observing the human agent and imitating him. (a) A machine-machine interface is required between the software agents (or assistant agents) and the process. It is essentially a filter allowing to translate raw data from the process (data from sensors for instance) towards information at a higher level (using a communication protocol ) for the assistants, or conversely.

(b) The human agents act on the process using a classical human-machine interface.

(c) The human agents interact with software agents by a human-machine interface of another type.

Through this interface, the human agents have another view on the process, the raw data have been filtered compared with those perceived with the interface (a). With this interface, the software agents must either be able to give explanations about their solutions, and also about the 1 See also [Woods et al. 91] for a discussion about the representation of the human-machine interactions.

actions which can be -in some cases-directly performed on the process. It is very important for the human operators to have a feedback on the artificial agents'activities.

Control and supervision team may be considered as composed of interacting human and artificial agents. They work together to solve problems and to intervene on the process. This vision -of the domain-allows to underline different interaction types with associated research field(s).

II.2. The different interaction types and their associated research fields

According to the previous paragraph, the study of the interaction between heterogeneous entities is the object of different research fields, figure 3.

Fig. 3: Research fields concerned by the different types of interaction between human and software agents

Several types of interactions can be distinguished, and particularly the interactions among the human agents. This type of interaction is studied by sociologists and psychologists (see for example [CACM 94]) ; note the promising studies issued from sociologists focusing on organization notion [Mintzberg 86].

Concerning the human(s)-machine(s) interactions: the interactions between (1) a human and a machine (2) a human and an artificial agent are studied for a long time by the human factors specialists. The interaction between an artificial agent and several human agents seems more complex.

In fact, this type of interaction is a support for cooperative work, as it is studied by many researchers in CSCW (Computer Supported Cooperative Work) or HCCW (Human Computer Cooperative Work) (see for example [Jones et al. 94] [Li et al. 94]) fields.

Concerning the interactions between artificial agents: the Distributed Artificial Intelligence (DAI) is the domain in which the interactions between agents are studied. An artificial agent may be composed of two facets, figure 4: an internal aspect (how it is implemented) and an external aspect (its behavior). In an industrial complex system, it is possible to study an agent as a cooperative entity

II.3. Internal aspect of an artificial agent -needs for complex industrial systems.

According to an internal aspect, artificial agents -able to assist human agents and to communicate with one another-must have at least the following abilities [Mandiau et al. 91,Mandiau 93]:

• Reasoning. An intelligent agent must have Knowledge Bases (KB) about itself (current states, goals, capacities…), the other agents (current states, goals, capacities, knowledge, beliefs…) and the process (current states, expertise). It must be able to reason on symbolic (and/or numerical) knowledge. It must also integrate planning capacities (for the generation of plans based on beliefs and intentions) and decision capacities (for the choice of actions sequence that must be elaborated).

If the environment of an artificial agent includes human agents, it must have a specific representation for these specific colleagues. An artificial agent cooperate with its accointances in reasoning from the knowledge it posses about them. It is the same when it cooperates with a human agent: the artificial agent must have a representaton of each human with whom it collaborates. In the case of other artificial agents, it can deduce the reason of their acts using what it knows of them, because they reason in the same as it does. It is obviously far more complex when it interacts with human agents. To introduce pertinent knowledge about human agents in the bases of the artificial ones is one of the problems that must be solve to obtain efficient cooperation in such teams.

• Communication. The agents must be able to exchange (to send and to receive) information, and particularly by messages. The communication must also (1) allow the agents to include new knowledge about their environment, and (2) support the coordination of actions between the agents. Note that the tendency consists in basing the communication on the Speech Acts [Cohen & Levesque 90] [Blanford 93] which are defined as physical actions (i.e. pre-conditions and effects of action in a given situation).

II.4. External aspect of an artificial agent -needs for complex industrial systems.

We have seen above some basic needs concerning organizations modeling in complex systems.

These organizations may give the possibility to define the behavior of a global system, and thus, to lead to identify -for each (normal and abnormal) situation-the optimal behavior for the group. This modeling may be investigated by the external structure of the agent. In fact, this behavior can be perceived by the realization of (1) physical actions and (2) communicative acts. Moreover, this intelligent agent must have a "social behavior" since it is involved in a group. More exactly, its reactions have to depend on different internal parameters: its intentional states (beliefs, desires, intentions and commitments), its knowledge concerning the others agents and the tasks they have to perform, and its own social position in this group. By the social position, the agents may be able to organize themselves in a way to cooperate.

Several organization models among artificial agents has been defined according to human organization models. The characteristics of these different organizations are studied by DAI's researchers, sociologists and economists (a synthesis of these works is given in [Le Strugeon 95]): we can define in a simple manner the organizations as hierarchical, market and communities organizations. These organizations lead to attractive researches for the problematic of complex industrial systems. More explanations about them are given in the following part.

III. Organization models -research directions

III.1 Multiagent organization models

According to [Gasser 92] and [Le Strugeon 95], an appropriate classification of multiagent organizations is based on the type of control used. [Gasser 92] distinguishes four organizations categories:

• The centralized organization is an usual structure in human groups. It is a hierarchic configuration, the links between the agents are of the master-slave type. At each level of the hierarchy, masters centralize the decision and control powers. • The community with behavior rules is a community of specialists. They interact according to defined and known protocols. Multi-expert and multi-knowledge bases systems [Geizes et al. 90] belong to this category. Control and communication among the specialists is often done by blackboard techniques.

As a matter of fact, the "community with behavior rules" (or "society") form is the more realistic one to design or describe complex organizations. It includes the three other categories, which conversely appear as basic organization forms. Thus, it seems more interesting to consider the organization classification including the three categories: hierarchy, market and community.

An organization can be described as being made of functional components and relations among these components:

• A component is described by a type, that allows to distinguish a single component from a group component, and a role, that allows to represent the component's position in the organization.

Three roles are defined: coordination, performance and expertise. The coordinator's responsibility consists in managing the coordination among the components, that means to organize the actions to perform in a chronological order. The underling makes no decision, it performs actions. The expert provides information to the others.

• There are two types of relations between components: authority and communication relations.

The authority links state the relations existing between a component that has a role i with a component with a role j. The communication links state the communication direction that can occur between two specific components. Thus, relations among components are determined by their respective roles in the organization.

This description can be used at the different levels of a complex organization structure. If the component is a group, the same method can be used to describe its own organization: its internal components and the links among them. Thus, an organization including many sub-organizations can be described in a recursive way. The three organization types of our classification are modeled on this pattern:

• The hierarchy model is composed of two components types: coordinators and underlings. The coordinator is superior to the underling in the hierarchy. Coordinator gives orders to underling components, that perform the tasks and send back results to coordinators.

• Initially, in the market organization, all the components are peers. Then, contracts create several couples among them. The invitation to tender is made by a coordinator. When the contract exists, some components that had no role become underling ones.

• The community model includes only one component role. The organization relies mainly on negotiation and partial plans exchanges. Each component takes alternately expert, coordinator or underling roles.

After the explanation of the different organization models, we propose to include them in tools for multi-user cooperation.

III.2 Intelligent agents in multi-user cooperation

It seems that the cooperation between the participants of the supervision team can be improved by the use of intelligent agents. Indeed, some organization models exist in the multiagent systems field, that enable an efficient cooperation in the group. By implementing an intelligent agent in each distributed interface, the communication problem among these sites can be seen as a multiagent cooperation problem.

This cooperation problem is the central point of the research works in the multiagent systems field. Several methods exist, including agents with more or less capacities of reaction to events, mutual knowledge, etc. Generally, an efficient cooperation can be established only when an organization structure, where everyone has a definite role, is created in the group. This way, the actions performed by each one contribute to the common goal and generate as less conflicts as possible, mostly caused by resources sharing. For an application of complex industrial system supervision, the common goal is the well-functioning of the process.

In a context like this, the cooperation in the system composed of human and artificial agents can be described according to the four dimensions exposed in the first part of the article (cf. table 3):

• location dimension: both cooperation types, proximate or remote, can be found. The distinction has no effect if an artificial agent plays a part in the relation, since all the exchanges are made through computing elements (screen, keyboard, etc.).

• time dimension: both cooperation types, synchronous and asynchronous, are conceivable.

• organization dimension: the cooperation is mainly a collective cooperation, in which every entity is conscious of playing a part in the common goal performing. But, it is conceivable that some of the artificial agents do not have all the knowledge required to be "conscious" to take part of the group, so the "distributed" aspect (as SCHMIDT calls it) is present either.

• communication dimension: the cooperation among the human operators can be direct or mediated. But, every cooperation including an exchange between an artificial agent and a human agent is necessarily mediated, because of the distinct nature of them. Among artificial agents, the communication type depends on the way they have been designed: the communication is direct if the agents exchange messages, it is mediated if they use a blackboard. Otherwise, the table 4 exposes a first description, that must be confirmed by further research works, of the organizations according to the cooperation form previously explained by [Schmidt 89].

The table shows that the organizational structures to establish depend on the relations among the agents:

• integrating cooperation: This cooperation form introduces the problem of the tasks allocation, linked to the negotiation and possible conflicts solving. In a master-slave relation, this cooperation requires a strong coordination between the agents to integrate every agent's abilities into the global task performing. This coordination is efficiently used in a hierarchical structure. In a peer relation between agents, performing tasks that require different abilities, generates organizations of the society type.

• debate cooperation: This cooperation allows to deal with tasks from different points of view.

The cooperation enables the results synthesis. That leads naturally to results sharing. In a master-slave relation, this type of cooperation can not really exist, since the slave has the master's point of view. On the opposite and by definition, community organizations, where the agents have egalitarian relations, make the most of it. Results sharing, in these organization types, deals with problems linked to the multi-expertise or to knowledge consistency.

• argument cooperation: In a master-slave relation, this cooperation form fits with a market-like organization, whereas in a peer relation, the organization can be a community or a society.

Conclusion

In this article, we have proposed a set of research directions to design CSCW systems in complex industrial systems, with the help of concepts from the multiagent systems studies.

According to the first trends concerning multi-users architectures (like [Hill 92], [Dewan 92], [Croisy 94], [Ben Atallah et al. 95]), it seems more and more necessary to study the organizations to develop. These organizations require to analyze several problems, such as, for example: the coordination among the agents for a given task, conflicts solving by negotiation, communications using speech acts enabling to explain or to cooperate in a natural manner for the (human or not) agents. This research field is really in a growing period, the complex industrial systems offer an extremely rich context of study.

Fig. 1 :Fig. 2 :

 12 Fig.1: Simplified example of human agent environment in control room.Although the researches attempt to study the intelligent agent notion in a static discrete context (office automation, personal assistant), in this paper, we investigate it for a continuous dynamic process. In this context, the human-machine and machine-machine interfaces are used as acting on the process and as interacting with the assistant agents (figure2)[Le Strugeon et al. 95]:

[Fig. 4 :

 4 Fig. 4: Representation of intelligent agent's principal elements.

•

  The market-like organizations are based on the principle of contract, like the bidding mode of cooperation. The reference example in DAI is the Contract-Net of Smith [Smith 80]. • The pluralistic communities are made of independent agents. They prepare solutions and communicate them to the other members of the community. Each of them verifies, validates or improves the propositions of the others [Kornfeld 81] [Lesser & Corkill 83].

Table 1

 1

	: cooperation forms (synthesized from [Schmidt 89])
	integrating	Performing tasks that integrate the abilities of each
	cooperation	and every entity
	debate	Need for a multi-representation of the points of view
	cooperation	to perform the task
	argument	Increase of the group's capacities
	cooperation
	Similarly, SCHMIDT proposes a classification for the cooperation modes (table 2) that shows
	the way to achieve cooperation among different entities.
	Table 2: cooperation modes (synthesized from [Schmidt 89])

Table 3 :

 3 The cooperation dimensions seen from the nature of the cooperating entities

	entities		human agents
	/	human agents	and	artificial agents
	cooperation		artificial agents
	dimensions
	location	proximate or remote	with no effect	with no effect
	time	synchronous or asynchronous	synchronous or asynchronous	mostly synchronous, but can be asynchronous either
	organization	collective	collective with some distributed component	collective and/or distributed
				depends on the implemented
	communication direct or mediated	mediated	communication type:
				BB => mediated
				messages => direct

Table 4 :

 4 Organization models corresponding to the use of a social links between agents and a cooperation form.

	type of social links
	/	master-slave	peers
	cooperation form
	integrating	hierarchy	society
	debate	X	community (multi-expertise)
	argument	market	community society

Acknowledgements

The authors wish to thank their reviewers for their helpful comments about this paper.