Extending the BDI Model with Q-learning in Uncertain Environment

The BDI model has solved the problem of reasoning and decision-making of agents in a particular environment by procedure reasoning. But in uncertain environment which the context is unknown the BDI model is not applicable, because in BDI model the context must be matched in plan library. To address this issue, in this paper we propose a method extending the BDI model with Q-learning which is one algorithm of reinforcement learning, and make an improvement to the decision-making mechanism on the ASL as a implement model of BDI. Finally we completed the simulation of maze on Jason simulation platform to verify the feasibility of the method.