Reinforcement Learning-Based Intelligent Agents for Improved Productivity in Container Vessel Berthing Applications

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Author(s): Prasanna Lokuge (Monash University, Australia)and Damminda Alahakoon (Monash University, Australia)
Copyright: 2006
Pages: 30
Source title: Business Applications and Computational Intelligence
Source Author(s)/Editor(s): Kevin Voges (University of Canterbury, New Zealand)and Nigel Pope (Griffith University, Australia)
DOI: 10.4018/978-1-59140-702-7.ch009

Keywords: Business & Management / Business Information Systems / Business Intelligence / Business Science Reference

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Abstract

This chapter introduces the use of hybrid intelligent agents in a vessel berthing application. Vessel berthing in container terminals is regarded as a very complex, dynamic application, which requires autonomous decision-making capabilities to improve the productivity of the berths. In this chapter, the dynamic nature of the container vessel berthing system has been simulated with reinforcement learning theory, which essentially learns what to do by interaction with the environment. Other techniques, such as Belief-Desire-Intention (BDI) agent systems have also been implemented in many business applications. The chapter proposes a new hybrid agent model using an Adaptive Neuro Fuzzy Inference System (ANFIS), neural networks, and reinforcement learning methods to improve the reactive, proactive and intelligent behavior of generic BDI agents in a shipping application.

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