Reinforcement learning in neuro BDI agents for achieving agent´s intentions in vessel berthing applications

Complex business application systems that involve non trivial decision making can have highly unpredictable situations. In such situation adaptive and intelligent behaviors would able to mitigate the risk in business. Vessel berthing application in container terminals is regarded as a very complex dynamic application, which requires autonomous decision making capabilities to improve the productivity of the berths. On the other hand, BDI agent systems have been implemented in many applications and found some limitations in learning. We propose a new enhanced hybrid BDI model with ANFIS and reinforcement learning methods to over come the above limitation. Our paper discusses how the commitment strategy of agent´s desire, intentions and plans could be enhanced with intelligent learning capabilities. A new motivation based distance calculation method supported with ANFIS and reinforcement learning is proposed in the paper, which improve the reactive, proactive and intelligent behaviors of generic BDI agents in complex applications.