AI 2005: Advances in Artificial Intelligence: 18th by Shichao Zhang, Ray Jarvis

By Shichao Zhang, Ray Jarvis

This booklet constitutes the refereed court cases of the 18th Australian Joint convention on synthetic Intelligence, AI 2005, held in Sydney, Australia in December 2005.

The seventy seven revised complete papers and 119 revised brief papers provided including the abstracts of three keynote speeches have been rigorously reviewed and chosen from 535 submissions. The papers are catgorized in 3 vast sections, particularly: AI foundations and applied sciences, computational intelligence, and AI in really expert domain names. specific issues addressed by means of the papers are good judgment and reasoning, laptop studying, video game concept, robot expertise, information mining, neural networks, fuzzy conception and algorithms, evolutionary computing, net intelligence, choice making, trend reputation, agent expertise, and AI functions.

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Extra info for AI 2005: Advances in Artificial Intelligence: 18th Australian Joint Conference on Artificial Intelligence, Sydney, Australia, December 5-9, 2005, Proceedings

Example text

Transitions in beliefalternative worlds correspond only to the execution of actions of the form observe α. Our logical language makes use of a formula do(π) which is true at an execution state iff the program π is the action about to be executed by the agent at that state. e. are the ways of executing π in that state), taking into account the belief alternatives of the states. The semantics of action must distinguish between doing an action and attempting to do an action, as the PRS-like agent cannot guarantee success.

This points to one complicated aspect of event handling in the PRS-like architecture. The third and fourth properties represent the persistence of intentions. 6 Conclusion This paper has provided a formalization of the execution model of a family of BDI agents based on the PRS architecture, and shown how precise notions of belief and intention can be represented in reachability graphs, structures that are suitable for applying model checking algorithms to this class of agents. By clarifying the mental notions underlying this class of agents and by systematically relating these notions to the operational behaviour of the agent interpreter, a more robust methodology underpinning the verification of complex BDI agents is made possible.

It is not possible to execute all the agent’s plans to successful completion, even when no adverse events occur in the world). In such a case, the theory says that the agent does not really “intend” to execute those plans; it only intends to execute the compatible parts of the plans. Thus the notion of intention encoded in the reachability graph is more a notion of commitment (the agent intends those actions it is “committed” to performing), whereas the notion of intention used in the architecture is something weaker again (the agent is not even committed to carrying out its “intentions”).

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