Causality Reconstruction by an Autonomous Agent
| Autor: | Xue, Jianyong, GEORGEON, Olivier L., Guillermin, Mathieu |
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| Přispěvatelé: | Systèmes Cognitifs et Systèmes Multi-Agents (SyCoSMA), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Université Catholique de Lyon (UCLy) (UCLy), A. Samsonovich |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: | |
| Zdroj: | Advances in Intelligent Systems and Computing International Conference on Biologically Inspired Cognitive Architectures International Conference on Biologically Inspired Cognitive Architectures, Aug 2018, Prague, Czech Republic. pp.347-354 |
| Popis: | International audience; Most AI algorithms consider input data as "percepts" that the agent receives from the environment. Constructivist epistemology, however, suggests an alternative approach that considers the algorithm's input data as feedback resulting from the agent's actions. This paper introduces a constructivist algorithm to let an agent learn regularities of actions and feedback. The agent organizes its behaviors to fulfill a form of intentionality defined independently of a specific task. The experiment shows that this algorithm constructs a Petri net whose nodes represent hypothetical stable states afforded by the agent/environment coupling, and arcs represent transitions between such states. Since this Petri net allows the algorithm to predict the consequences of the agent's actions, we argue that it constitutes a rudimentary causal model of the "world" (agent+environment) learned by the agent through experience of interaction. This work opens the way to studying how an autonomous agent car learn more complex causal models of more complex worlds, in particular by explaining regularities of interaction through the presence of objects in the agent's surrounding space. |
| Databáze: | OpenAIRE |
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