Hybrid BDI agents with improved learning capabilities for adaptive planning in a container terminal application

Vessel berthing system in a container terminal is regarded as a very complex dynamic application in today´s business world. We propose a new extended BDI framework with an intelligent module for handling complex situations. Change rate of beliefs (Ψ) and expected cost of reaching the final goal state from different states in the plan hierarchy have been considered by the agent in the proposed architecture. This would enable agents to identify the alternative plans in the intention structure with the change of the environment. Dynamic selection of plans and expected cost of achieving the final goal state from various plan paths are modeled with the use of a supervised neural network. Adaptive neuro fuzzy inference system (ANFIS) has been incorporated in making the final rational decisions in the agent model.