Machine Autonomy : Definition, Approaches, Challenges and Research Gaps

Postprin

Causality Reconstruction by an Autonomous Agent

International audienceMost 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

Causality Reconstruction by an Autonomous Agent

International audienceMost 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

Trace Based System in TEL Systems: Theory and Practice

Part 3: Machine LearningInternational audienceWe present in this paper an easier way to manage activity traces and to compute human learning indicators activities in Technology Enhanced Learning TEL systems. We review our research work related to Trace based system TBS and we explain how we use TBS to develop new and generic model to represent the indicator life cycle from its creation to its reuse. This paper presents the underlying theory and how this theory is implemented to compute human learning indicators activities available for use with any other learning platform, provided the TBS can access the learning platform traces