Solving distributed and dynamic constraints using an emotional metaphor: Application to the timetabling problem.

International audienceThis paper presents a method and it's implementation for solving distributed and dynamic constraints satisfaction problem. In order to improve adaptability and perfor- mance, our algorithm is based on agents with autonomous behaviors guided by metaphoric assumptions. Our ap- proach can be distinguished by the following points : The metaphor turns on sociological and emotional criterias without negotiation and memorisation. It tries to copy collective and affective human's behavior during a complex decision making. The agent's model include the notions of affective power, intruder and public mood perception. We have applied this method successfully to the timetabling problem. This paper show formalisation, implementation and first results of this work

The memorization of in-line sensorimotor invariants: toward behavioral ontogeny and enactive agents

International audienceThis paper presents a behavioral ontogeny for artificial agents based on the interactive memorization of sensorimotor invariants. The agents are controlled by continuous timed recurrent neural networks (CTRNNs) which bind their sensors and motors within a dynamic system. The behavioral ontogenesis is based on a phylo- genetic approach: memorization occurs during the agent's lifetime and an evolutionary algorithm discovers CTRNN parameters. This shows that sensorimotor invariants can be durably modified through interaction with a guiding agent. After this phase has finished, agents are able to adopt new sensorimotor invariants relative to the environment with no further guidance. We obtained these kinds of behaviors for CTRNNs with 3-6 units, and this paper examines the functioning of those CTRNNs. For instance, they are able to internally simulate guidance when it is externally absent, in line with theories of simulation in neuroscience and the enactive field of cognitive science

Real-time retrieval for case-based reasoning in interactive multiagent-based simulations

The aim of this paper is to present the principles and results about case-based reasoning adapted to real- time interactive simulations, more precisely concerning retrieval mechanisms. The article begins by introducing the constraints involved in interactive multiagent-based simulations. The second section pre- sents a framework stemming from case-based reasoning by autonomous agents. Each agent uses a case base of local situations and, from this base, it can choose an action in order to interact with other auton- omous agents or users' avatars. We illustrate this framework with an example dedicated to the study of dynamic situations in football. We then go on to address the difficulties of conducting such simulations in real-time and propose a model for case and for case base. Using generic agents and adequate case base structure associated with a dedicated recall algorithm, we improve retrieval performance under time pressure compared to classic CBR techniques. We present some results relating to the performance of this solution. The article concludes by outlining future development of our project

Dynamical Systems to Account for Turn-Taking in Spoken Interactions

International audienceTurn management is considered as essential for an Embodied Conversational Agent (ECA) to increase user’s engagement. This article presents a dynamical model for turn management in dyadic interactions. The model is a system of differential equations that mixes two models from the cognitive sciences, the Drift Diffusion Model, and the Behavioral Dynamics. Decision-making and the control of actions are two coupled processes that modulate continuously the behavior of the interacting agent. This conceptual model accounts for the emer- gence of smooth transitions without using neither prediction nor planning of the agent’s behavior. The objective was not to obtain a fully realistic behavior, but to show how the model could account for the main qualitative properties of turn management, such as interrupting the current speaker, signaling its willingness to go on speaking, or yielding the turn to the next speaker

Mascaret: Pedagogical multi-agents system for virtual environment for training.

International audienceThis study concerns virtual environments for training in operational conditions. The principal developed idea is that these environments are heterogeneous and open multi-agent systems. The MASCARET model is proposed to organize the interactions between agents and to provide them reactive, cognitive and social abilities to simulate the physical and social environment. The physical environment represents, in a realistic way, the phenomena that learners and teachers have to take into account. The social environment is simulated by agents executing collaborative and adaptive tasks. These agents realize, in team, procedures that they have to adapt to the environment. The users participate to the training environment through their avatar. In this article, we explain how we integrated, in MASCARET, models necessary to the creation of Intelligent Tutoring System. We notably incorporate pedagogical strategies and pedagogical actions. We present pedagogical agents. To validate our model, the SÉCURÉVI application for fire-fighters training is developed

PEGASE: A generic and adaptable intelligent system for virtual reality learning environments

International audienceThe context of this research is the creation of human learning environments using virtual reality. We propose the integration of a generic and adaptable intelligent tutoring system (Pegase) into a virtual environment. The aim of this environment is to instruct the learner, and to assist the instructor. The proposed system is created using a multi-agent system. This system emits a set of knowledge (actions carried out by the learner, knowledge about the field, etc.) which Pegase uses to make informed decisions. Our study focuses on the representation of knowledge about the environment, and on the adaptable pedagogical agent providing instructive assistance

MASCARET: multiagent system for virtual environment for training.

International audienceThis study concerns virtual environments for training in operational conditions. The principal developed idea is that these environments are heterogeneous and open multi-agent systems. The MASCARET model is proposed to organize the interactions between agents and to provide them reactive, cognitive and social abilities to simulate the physical and social environment. The physical environment represents, in a realistic way, the phenomena that learners and teachers have to take into account. The social environment is simulated by agents executing collaborative and adaptive tasks. These agents realize, in team, procedures that they have to adapt to the environment. The users participate to the training environment through their avatar. In this article, we show that MASCARET allows the establishment of models necessary to the creation of Intelligent Tutoring System. We interest notably to its use in pedagogical aspects

Collaborative Behaviour Modelling of Virtual Agents using Communication in a Mixed Human-Agent Teamwork

International audience—The coordination is an essential ingredient for the mixed human-agent teamwork. It requires team members to share knowledge to establish common grounding and mutual awareness among them. In this paper, we proposed a collaborative conversational belief-desire-intention (C 2 BDI) behavioural agent architecture that allows to enhance the knowledge sharing using natural language communication between team members. We defined collaborative conversation protocols that provide proactive behaviour to agents for the coordination between team members. Furthermore, to endow the communication capabilities to C 2 BDI agent, we described the information state based approach for the natural language processing of the utterances. We have applied the proposed architecture to a real scenario in a collaborative virtual environment for training. Our solution enables the user to coordinate with other team members

Communicative Capabilities of Agents for the Collaboration in a Human-Agent Team

International audienceThe coordination is an essential ingredient for the human-agent teamwork. It requires team members to share knowledge to establish common grounding and mutual awareness among them. In this paper, we propose a behavioral architecture C 2 BDI that allows to enhance the knowledge sharing using natural language communication between team members. We define collaborative conversation protocols that provide proactive behavior to agents for the coordination between team members. We have applied this architecture to a real scenario in a col-laborative virtual environment for training. Our solution enables users to coordinate with other team members

Accounting for Patterns of Collective Behavior in Crowd Locomotor Dynamics for Realistic Simulations

International audienceDo people in a crowd behave like a set of isolated individuals or like a cohesive group? Studies of crowd modeling usually consider pedestrian behavior either from the point of view of an isolated individual or from that of large swarms. We introduce here a study of small crowds walking towards a common goal and propose to make the link between individual behavior and crowd dynamics. Data show that participants, even though not instructed to behave collectively, do form a cohesive group and do not merely treat one another as obstacles. We present qualitative and quantitative measurements of this collective behavior, and propose a first set of patterns characterizing such behavior. This work is part of a wider effort to test crowd models against observed data