Programming Multi-Agent Systems: 8th International Workshop, by Michal Pěchouček, Michal Jakob, Peter Novák (auth.), Rem

By Michal Pěchouček, Michal Jakob, Peter Novák (auth.), Rem Collier, Jürgen Dix, Peter Novák (eds.)

This booklet constitutes the complaints of the eighth overseas Workshop on Programming Multi-Agent structures held in Toronto, Canada, in may well 2010 together with AAMAS 2010, the ninth overseas Joint convention on self reliant brokers and Multiagent platforms. The 7 revised complete papers provided including 1 invited paper have been rigorously reviewed and chosen for inclusion within the ebook. The papers conceal a extensive variety of typically functional themes like selection portion of agent platforms; sensible examples of programming languages; interplay with the surroundings, and are hence equipped in topical sections on reasoning, programming languages, and environments.

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Additional resources for Programming Multi-Agent Systems: 8th International Workshop, ProMAS 2010, Toronto, ON, Canada, May 11, 2010. Revised Selected Papers

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The atomic components of thought. Lawrence Erlbaum, Mahwah (1998) 3. : A robust and fast action selection mechanism for planning. In: Proceedings of AAAI 1997, pp. 714–719 (1997) 4. : Relational reinforcement learning. Machine Learning 43(1), 7–52 (2001) 5. : Programming Rational Agents in GOAL. In: Multi-Agent Programming: Languages, Tools and Applications, ch. 4, pp. 119–157. Springer, Heidelberg (2009) 6. : Strategies for ´ Affect-Controlled Action-Selection in Soar-RL. R. ) IWINAC 2007. LNCS, vol.

The agent program lists two rules. Three setups were in the logistics domain in which the agent has to deliver two orders each consisting of two different packages to two clients at different locations. In total there are three locations, with all packages at the starting Reinforcement Learning as Heuristic for Action-Rule Preferences 35 location and each client at a different location. A location can be reached directly in one action. The agent can load and unload a package as well as goto a different location.

Finally, the goals of an agent represent what the agent wants the environment to be like. For example, the agent of Listing 1 wants to realise a state where block a is on top of block b. Goals are to be interpreted as achievement goals, that is as a goal the agent wants to achieve at some future moment in time and does not believe to be the case yet. This requirement is implemented by imposing a rationality constraint such that any goal in the goal base must not believed to be the case. Upon achieving the complete goal, an agent will drop the goal.

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