Abstract
In this paper we propose an agent architecture with learning capabilities and its application to a transportation problem. The agent consists of the several modules (control, execution, communication, task evaluation, planning and social) and knowledge bases to store information and learned knowledge. The proposed solution is tested on the PDPTW. Agents using supervised and reinforcement learning algorithms generate knowledge to evaluate arriving requests. Experimental results show that learning increases agent performance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Airiau, S., Padham, L., Sardina, S., Sen, S.: Incorporating learning in bdi agents. In: Proceedings of the ALAMAS+ALAg Workshop (May 2008)
Bachem, A., Hochstättler, W., Malich, M.: The simulated trading heuristic for solving vehicle routing problems. Discrete Appl. Math. 65, 47–72 (1996)
Cohen, W.: Fast effective rule induction. In: Machine Learning: Proc. of the Twelfth International Conference. Morgan Kaufmann, Lake Tahoe (1995)
Comité, F.D.: A java platform for reinforcement learning experiments (2005)
Fischer, K., Muller, J., Pischel, M.: Cooperative Transportation Scheduling: an Application Domain for DAI. Applied Artificial Intelligence, 1–33 (1996)
Gehrke, J.D., Wojtusiak, J.: Traffic prediction for agent route planning. In: Bubak, M., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2008, Part III. LNCS, vol. 5103, pp. 692–701. Springer, Heidelberg (2008)
Koźlak, J.: Learning in cooperating agents environment as a method of solving transport problems and limiting the effects of crisis situations. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2007. LNCS, vol. 4488, pp. 872–879. Springer, Heidelberg (2007)
Koźlak, J., Créput, J.C., Hilaire, V., Koukam, A.: Multi-agent approach to dynamic pick-up and delivery problem with uncertain knowledge about future transport demands. Fundam. Inform.: Annales Societatis Mathematicae Polonae 71(1) (2006)
Meignan, D., Creeput, J.C., Koukam, A.: A coalition-based metaheuristic for the vehicle routing problem. In: Proceedings of IEEE Congress on Evolutionary Computation, Hongkong, pp. 1176–1182 (2008)
Mitrowic-Minic, S.: Pickup and Delivery Problem with Time Windows: A Survey. Tech. Rep. 1998-12, SFU CMPT TR (1998)
Nowaczyk, S., Malec, J.: Learning to evaluate conditional partial plans. In: ICMLA 2007: Proceedings of the Sixth International Conference on Machine Learning and Applications, pp. 235–240. IEEE Computer Society, Washington, DC, USA (2007)
Panait, L., Luke, S.: Cooperative multi-agent learning: The state of the art. Autonomous Agents and Multi-Agent Systems 11 (2005)
Quinlan, J.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
Smith, R.G.: The contract net protocol: high-level communication and control in a distributed problem solver. IEEE Transactions on Computer (December 1980)
Śnieżyński, B., Dajda, J., Mlostek, M., Pulchny, M.: Farmer-pest problem: A multidimensional problem domain for comparison of agent learning methods. In: Proc. of the ICCS 2011. Elsevier, Amsterdam (2011)
Śnieżyński, B., Wojcik, W., Gehrke, J.D., Wojtusiak, J.: Combining rule induction and reinforcement learning: An agent-based vehicle routing. In: Proc. of the ICMLA 2010, Washington D.C, pp. 851–856 (2010)
Sugawara, T., Lesser, V.: On-line learning of coordination plans. In: Proceedings of the 12th International Workshop on Distributed Artificial Intelligence (1993)
Sutton, R., Barto, A.: Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning). The MIT Press, Cambridge (March 1998)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Śnieżyński, B., Koźlak, J. (2011). Agent-Based System with Learning Capabilities for Transport Problems. In: Jędrzejowicz, P., Nguyen, N.T., Hoang, K. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2011. Lecture Notes in Computer Science(), vol 6923. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23938-0_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-23938-0_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23937-3
Online ISBN: 978-3-642-23938-0
eBook Packages: Computer ScienceComputer Science (R0)