Handling Multiple Events in Hybrid BDI Agents with Reinforcement Learning: A Container Application

Vessel berthing in a container port is considered as one of the most important application systems in the shipping industry. The objective of the vessel planning application system is to determine a suitable berth guaranteeing high vessel productivity. This is regarded as a very complex dynamic application, which can vastly benefited from autonomous decision making capabilities. On the other hand, BDI agent systems have been implemented in many business applications and found to have some limitations in observing environmental changes, adaptation and learning. We propose new hybrid BDI architecture with learning capabilities to overcome some of the limitations in the generic BDI model. A new “Knowledge Acquisition Module” (KAM) is proposed to improve the learning ability of the generic BDI model. Further, the generic BDI execution cycle has been extended to capture multiple events for a committed intention in achieving the set desires. This would essentially improve the autonomous behavior of the BDI agents, especially, in the intention reconsideration process. Changes in the environment are captured as events and the reinforcement learning techniques have been used to evaluate the effect of the environmental changes to the committed intentions in the proposed system. Finally, the Adaptive Neuro Fuzzy Inference (ANFIS) system is used to determine the validity of the committed intentions with the environmental changes.