Adaptive and Learning Agents: Second Workshop, ALA 2009, by Marc Ponsen, Matthew E. Taylor, Karl Tuyls (auth.), Matthew PDF
By Marc Ponsen, Matthew E. Taylor, Karl Tuyls (auth.), Matthew E. Taylor, Karl Tuyls (eds.)
ThisbookpresentsselectedandrevisedpapersoftheSecondWorkshoponAd- tive and studying brokers 2009 (ALA-09), held on the AAMAS 2009 convention in Budapest, Hungary, could 12. The goalof ALA is to supply an interdisciplinaryforum for scientists from quite a few ?elds resembling machine technology, biology, online game conception and economics. This year’s variation of ALA was once the second one after the merger of the previous wo- outlets ALAMAS and ALAg. In 2008 this joint workshop used to be prepared for the ?rst time below the ?ag of either occasions. ALAMAS used to be a every year returning ecu- pean workshop on adaptive and studying brokers and multi-agent platforms (held 8 times). ALAg was once the overseas workshop on adaptive and studying brokers, which was once frequently held at AAMAS. to extend the power, visibility and caliber of the workshop it used to be made up our minds to merge either workshops lower than the ?ag of ALA and to establish a guidance Committee as an organizational spine. This ebook includes six papers provided throughout the workshop, that have been conscientiously chosen after an extra evaluate around in the summertime of 2009. We consequently desire to explicitly thank the individuals of this system Committee for the standard and sincerity in their e?orts and repair. in addition we want to thank the entire contributors of the senior steerage Committee for making this workshop attainable and aiding it with sound recommendation. We additionally thank the AAMAS convention for supplying us a platform for containing this occasion. eventually we additionally desire to thank all authors who replied to our call-for-papers with attention-grabbing contributions.
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Additional info for Adaptive and Learning Agents: Second Workshop, ALA 2009, Held as Part of the AAMAS 2009 Conference in Budapest, Hungary, May 12, 2009. Revised Selected Papers
8, pp. 1017–1023. MIT Press, Cambridge (1996) 10. : Learning relational options for inductive transfer in relational reinforcement learning. , Tadepalli, P. ) ILP 2007. LNCS (LNAI), vol. 4894, pp. 88–97. Springer, Heidelberg (2008) 11. : Model minimization in Markov decision processes. In: Proceedings of the Thirteenth National Conference on Artificial Intelligence, pp. 106–111 (1997) 12. : An overview of MAXQ hierarchical reinforcement learning. , Walsh, T. ) SARA 2000. LNCS (LNAI), vol. 1864, pp.
The spatial dilemmas of evolution. Int. Journal of Bifurcation and Chaos 3, 35–78 (1993) 14. : Evolving cooperation in the non-iterated prisoner’s dilemma: the importance of spatial organisation. In: Proceedings of Artiﬁcial Life IV (1994) 15. : Stable cooperation in the n-player prisoner’s dilemma: The importance of community structure. , Kudenko, D. ) ALAMAS 2005, ALAMAS 2006, and ALAMAS 2007. LNCS (LNAI), vol. 4865, pp. 157–168. Springer, Heidelberg (2008) 16. : The eﬀects and evolution of tag-mediated selection of partners in populations playing the iterated prisoner’s dilemma.
Lazaric et al.  also focuses on transferring very low-level information by using source task instances in a target task. After learning one or more source tasks, some experience is gathered in the target task, which may have a different state space or transition function, but the state variables and actions must remain unchanged. Saved instances (that is, observed s, a, r, s tuples) are compared to recorded instances in the target task. Source task instances that are very similar, as judged by their distance and alignment with target task data, are transferred.
Adaptive and Learning Agents: Second Workshop, ALA 2009, Held as Part of the AAMAS 2009 Conference in Budapest, Hungary, May 12, 2009. Revised Selected Papers by Marc Ponsen, Matthew E. Taylor, Karl Tuyls (auth.), Matthew E. Taylor, Karl Tuyls (eds.)