Explaining computer predictions with augmented appraisal degrees

An augmented appraisal degree (AAD) has been conceived as a mathematical representation of the connotative meaning in an experience-based evaluation, which depends on a particular experience or knowledge. Aiming to improve the interpretability of computer predictions, we explore the use of AADs to represent evaluations that are per- formed by a machine to predict the class of a particular object. Hence, we propose a novel method whereby predictions made using a support vector machine classification process are augmented through AADs. An illustra- tive example, in which the classes of handwritten digits are predicted, shows how the augmentation of such predictions can favor their interpretability

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

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

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

Nola Green gedenklesing

*Direkte weergawe soos voorgedraBEPLANNING EN BEHUISING: Waar kom ons vandaan. Waar het ons oorspronklik so ’n paar jaar gelede begin met ’n nuwe behuis- ingsbeleid. Waar staan ons vandag. Wat is die omvang van die huidige voorstelle wat op die tafel is, en die aankondigings wat reeds gedoen is. Waar gaan ons heen in die toekoms. Watter werk is nog nie gedoen nie. Wat moet ons doen en hoe dit eintlik die stads- en streek- beplanners spesifiek sal raak in die toekoms

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

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

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