Modeling Reinforcement Learning using an Extended BDI logic TOMATOes

Abstract

TOMATOes is an extension of BDI logic, which introduced probabilistic state transitions and fix-point operators. Using TOMATOes, we can strictly describe and infer various properties of rational agents with those extended notions. In this paper, we give a detailed explanation of modeling of reinforcement learning with the Kripke structure used in TOMATOes, called BDI structure, and the description of transaction graph with policy using TOMATOes. In addition, we give some issues on rational agents for practical reasoning with the description of transaction graph using TOMATOes.