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Higher-Order Logic Description of MDPs to Support Meta-cognition in Artificial Agents

An artificial agent acting in natural environments needs metacognition to reconcile dynamically the goal requirements and its internal conditions, and re-use the same strategy directly when engaged in two instances of the same task and to recognize similar classes of tasks. In this work the authors start from their previous research on meta-cognitive architectures based on Markov Decision Processes (MDPs), and propose a formalism to represent factored MDPs in higher-order logic to achieve the objective stated above. The two main representation of an MDP are the numerical, and the propositional logic one. In this work we propose a mixed representation that combines both numerical and propositional formalism using first-, secondand third-order logic. In this way, the MDP description and the planning processes can be managed in a more abstract manner. The presented formalism allows manipulating structures, which describe entire MDP classes rather than a specific process.