Advances in Artificial Intelligence: 15th Conference of the by Scott A. DeLoach (auth.), Robin Cohen, Bruce Spencer (eds.)

By Scott A. DeLoach (auth.), Robin Cohen, Bruce Spencer (eds.)

The AI convention sequence is the optimal occasion subsidized through the Canadian - ciety for the Computational reviews of Intelligence / Soci´et´e canadienne pour l’´etude d’intelligence par ordinateur. Attendees take pleasure in our mostly Canadian - mosphere –hospitable and stimulating. The Canadian AI convention showcases the superb study paintings performed through Canadians, their overseas colleagues, and others identifying to hitch us every one spring. overseas participation is usually excessive; this 12 months nearly forty% of the submitted papers have been from non-Canadian - searchers. We permitted 24 papers and eight poster papers from fifty two full-length papers submitted. We additionally approved 8 of ten abstracts submitted to the Graduate pupil Symposium. All of those approved papers seem during this quantity. The Canadian AI convention is the oldest continuously-held nationwide AI c- ference on the earth. (ECCAI’s predecessor, AISB, held conferences in 1974, yet those have on account that turn into international.) meetings were held biennially considering that 1976, and each year considering that 2000. AI 2002 back joined its sister Canadian machine technological know-how meetings, imaginative and prescient Interface and pix Interface, enri- ing the event for all contributors. The joint assembly permits us to stick - shaped approximately different components, to make new contacts, and maybe to enquire cross-disciplinary study. This yr the meetings used to be hung on the gorgeous campus of the college of Calgary, and plenty of contributors took the chance to journey within reach Ban? and the magni?cent Rocky Mountains.

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Extra resources for Advances in Artificial Intelligence: 15th Conference of the Canadian Society for Computational Studies of Intelligence, AI 2002 Calgary, Canada, May 27–29, 2002 Proceedings

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We believe that modeling an agent environment as a marketplace, where agents are motivated by economic incentives to provide goods and services to each other, is a practical and feasible approach. It is possible in the marketplace that some sellers may try to “cheat” by delivering some good with reasonable quality, followed by low quality goods. Buyers in our approach use reputation as a means to protect themselves from those dishonest sellers: They each dynamically maintain a set of reputable sellers and consider choosing suitable sellers from this set first.

As discussed in detail later, we believe that the proposed algorithm will result in improved performance for buyers, better satisfaction for both buyers and sellers, reduced communication load, and more robust systems. The paper is organized as follows: The next section, section 2, introduces our proposed learning algorithm for buyers and sellers, respectively. Section 3 discusses the possible advantages of the proposed algorithm. Section 4 remarks on related work. Section 5 provides some future research directions.

Figure 1 presents a diagram of the specialists (S) migrating to the remote host system from AERO as a result of the consultant (C) soliciting services from these specialists. Fig. 1. AERO agents working together 1 One way of dispatching a specialist is to clone the original specialist agent and then send the clone to the remote system. Another way may be to explicitly request the specialist to create a functionally-equivalent agent. 1 David Chen and Robin Cohen Host to AERO Processing Algorithm The steps below demonstrate a host system calling AERO and enlisting the exception handling service.

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