Enaction-Based Artificial Intelligence: Toward Coevolution with Humans in the Loop

This article deals with the links between the enaction paradigm and artificial intelligence. Enaction is considered a metaphor for artificial intelligence, as a number of the notions which it deals with are deemed incompatible with the phenomenal field of the virtual. After explaining this stance, we shall review previous works regarding this issue in terms of artifical life and robotics. We shall focus on the lack of recognition of co-evolution at the heart of these approaches. We propose to explicitly integrate the evolution of the environment into our approach in order to refine the ontogenesis of the artificial system, and to compare it with the enaction paradigm. The growing complexity of the ontogenetic mechanisms to be activated can therefore be compensated by an interactive guidance system emanating from the environment. This proposition does not however resolve that of the relevance of the meaning created by the machine (sense-making). Such reflections lead us to integrate human interaction into this environment in order to construct relevant meaning in terms of participative artificial intelligence. This raises a number of questions with regards to setting up an enactive interaction. The article concludes by exploring a number of issues, thereby enabling us to associate current approaches with the principles of morphogenesis, guidance, the phenomenology of interactions and the use of minimal enactive interfaces in setting up experiments which will deal with the problem of artificial intelligence in a variety of enaction-based ways

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 Challenge of Believability in Video Games: Definitions, Agents Models and Imitation Learning

In this paper, we address the problem of creating believable agents (virtual characters) in video games. We consider only one meaning of believability, ``giving the feeling of being controlled by a player'', and outline the problem of its evaluation. We present several models for agents in games which can produce believable behaviours, both from industry and research. For high level of believability, learning and especially imitation learning seems to be the way to go. We make a quick overview of different approaches to make video games' agents learn from players. To conclude we propose a two-step method to develop new models for believable agents. First we must find the criteria for believability for our application and define an evaluation method. Then the model and the learning algorithm can be designed

From decision to action : intentionality, a guide for the specification of intelligent agents' behaviour

International audienceThis article introduces a reflexion about behavioural specification for interactive and participative agent-based simulation in virtual reality. Within this context, it is neces sary to reach a high level of expressivness in order to enforce interactions between the designer and the behavioural model during the in-line prototyping. This requires to consider the need of semantic very early in the design process. The Intentional agent model is here exposed as a possible answer. It relies on a mixed imperative and declarative approach which focuses on the link between decision and action. The design of a tool able to simulate virtual environment implying agents based on this model is discus

Learning a Representation of a Believable Virtual Character's Environment with an Imitation Algorithm

In video games, virtual characters' decision systems often use a simplified representation of the world. To increase both their autonomy and believability we want those characters to be able to learn this representation from human players. We propose to use a model called growing neural gas to learn by imitation the topology of the environment. The implementation of the model, the modifications and the parameters we used are detailed. Then, the quality of the learned representations and their evolution during the learning are studied using different measures. Improvements for the growing neural gas to give more information to the character's model are given in the conclusion

Réalité Virtuelle et énaction

Cet article argumente sur la capacité du paradigme de l'énaction, à offrir un cadre épistémologique pour le domaine de la réalité virtuelle

Context-based decision-making for virtual soccer players.

International audienceThis article introduces a decision-making model for virtual agents evolving in dynamic and collaborative situations. Agents and humans have to collaborate in a virtual environment. In order to enhance the collaboration, the agent decision-making model is based on notions close to human ones. Those notions are context and case based reasoning. After an introduction of dynamic and collaborative situations, we present the notion of context and we give a definition adapted to our framework. The next part describes the decision making process. This one relies on the case identification thanks to a graph search algorithm. The last part of this document illustrates our purpose with an example taken from our application

Guiding for Associative Learning : How to Shape Artificial Dynamic Cognition ?

Je n'ai pas de nouvelles de la date de parution de ces actes. La conférence est passée, les organisateurs ont confirmés que les papiers sortiront dans deux numéros de LNAI, en attendant, je souhaitais mettre notre travail en ligne pour qu'il soit diffusé ...International audienceThis paper describes an evolutionary robotics experiment, which aims at showing the possibility of learning by guidance in a dynamic cognition perspective. Our model relies on Continuous Time Recurrent Neural Networks and Hebbian plasticity. The agents have the ability to be guided by stimuli and we study the influence of a guidance on their external behavior and internal dynamic when faced with other stimuli. The article develops the experiment and presents some results on the dynamic of the systems

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

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