HANDLING MULTIPLE EVENTS IN HYBRID BDI AGENTS WITH REINFORCEMENT LEARNING: A CONTAINER APPLICATION

Prasanna Lokuge, Damminda Alahakoon

2005

Abstract

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.

References

  1. Kim, K.H and Moon, K.C., 2003. “Berth Scheduling by simulated annealing”, Transportation research part B., V.37, 541-560, Elsevier Science Ltd.
  2. Georgeff, M, B. Pell, M. Pollack, M. Tambe, and M. Wooldridge.,1998. “The Belief-Desire-Intention Model of Agency”, Springer Publishers.
  3. Rao A.S and Georgeff ,1991, ”Modeling Rational agents with a BDI Architecture”, In proceeding of the second international conference on principles of knowledge representation and reasoning, p. 473-484, Morgan Kaufmann.
  4. Wooldridge M, 2000, “Reasoning about Rational Agents”, The MIT Press, London.
  5. Lokuge D.P.S and Alahakoon D, 2004, “BDI Agents with fuzzy associative memory for vessel berthing in container ports”, In proceeding of the Sixth international conference on enterprise information systems, INSTICC press, V2,p. 315-320, Porto, Portugal.
  6. Sutton R.S and Barto, 1988, A.G, “Reinforcement Learning, an Introduction”, The MIT Pres.
  7. Jang, J.S.R., 1993, “Adaptive network based fuzzy inference systems," IEEE Trans. on system, Man and Cybernetics, Vol. 23, No. 3, p. 665-685.
  8. Wooldridge M. and Jennings N.R, 1995. “ Intelligent agents: Theory to practice”, The knowledge engineering review, V. 10, no 12, p. 115-152.
  9. Chia, J.T., Lau, H.C and Lim, A. 1999. “Ant Colony optimization for ship berthing problem”, In proceeding of ASIAN99, 359-370, LNCS 1742.
  10. Schut, M and Wooldridge M. 2001, “Principles of Intention reconsideration “, In proceeding of the AGENTS'01, Montreal, Quebec, Canada, ACM 1- 58113-326-X.
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Paper Citation


in Harvard Style

Lokuge P. and Alahakoon D. (2005). HANDLING MULTIPLE EVENTS IN HYBRID BDI AGENTS WITH REINFORCEMENT LEARNING: A CONTAINER APPLICATION . In Proceedings of the Seventh International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 972-8865-19-8, pages 83-90. DOI: 10.5220/0002518600830090


in Bibtex Style

@conference{iceis05,
author={Prasanna Lokuge and Damminda Alahakoon},
title={HANDLING MULTIPLE EVENTS IN HYBRID BDI AGENTS WITH REINFORCEMENT LEARNING: A CONTAINER APPLICATION},
booktitle={Proceedings of the Seventh International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2005},
pages={83-90},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002518600830090},
isbn={972-8865-19-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Seventh International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - HANDLING MULTIPLE EVENTS IN HYBRID BDI AGENTS WITH REINFORCEMENT LEARNING: A CONTAINER APPLICATION
SN - 972-8865-19-8
AU - Lokuge P.
AU - Alahakoon D.
PY - 2005
SP - 83
EP - 90
DO - 10.5220/0002518600830090