By Scott A. DeLoach (auth.), Robin Cohen, Bruce Spencer (eds.)
The AI convention sequence is the foremost occasion backed by way of the Canadian - ciety for the Computational experiences of Intelligence / Soci´et´e canadienne pour l’´etude d’intelligence par ordinateur. Attendees take pleasure in our generally Canadian - mosphere –hospitable and stimulating. The Canadian AI convention showcases the superb examine paintings performed through Canadians, their overseas colleagues, and others determining to hitch us every one spring. foreign participation is usually excessive; this yr nearly forty% of the submitted papers have been from non-Canadian - searchers. We accredited 24 papers and eight poster papers from fifty two full-length papers submitted. We additionally authorized 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 this planet. (ECCAI’s predecessor, AISB, held conferences in 1974, yet those have when you consider that turn into international.) meetings were held biennially seeing that 1976, and every year when you consider that 2000. AI 2002 back joined its sister Canadian machine technological know-how meetings, imaginative and prescient Interface and pictures Interface, enri- ing the event for all individuals. The joint assembly permits us to stick - shaped approximately different parts, 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 lots of individuals took the chance to travel within sight Ban? and the magni?cent Rocky Mountains.
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Additional info 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
J. O. Kephart. Economic Incentives for Information Agents. In M. Klusch and L. Kerschberg, editors, Cooperative Information Agents IV, Lecture Notes in Artiﬁcial Intelligence, Vol. 1860, pages 72-82. Springer-Verlag, Berlin, 2000. 38 5. M. L. Littman. Markov Games As Framework for Multi-Agent Reinforcement Learning.
2 The Proposed Learning Algorithm In this section we propose a reinforcement learning and reputation based algorithm for buyers and sellers, respectively. The algorithm is aimed at maximizing the expected values of goods for buyers, and maximizing the expected proﬁts for sellers. Note that it is quite possible for both a seller s and a buyer b to be “winning” in a business transaction. This happens when seller s could choose a price p to sell good g to buyer b that maximized its expected proﬁt, and buyer b decided that purchasing good g at price p from seller s would maximize its ex- A Learning Algorithm for Buying and Selling Agents 33 pected value of goods.
In addition, we have examined some of the ways in which AERO can be of particular assistance, given that it may be servicing multiple systems simultaneously. Since exceptions are in fact a real possibility in many agent systems, there is a need to develop methods for addressing these exceptions, in order to maintain the eﬃciency of these systems and to enable these systems to be better trusted by their users. The development of AERO can therefore be seen as an important part of the overall eﬀort to design reliable agent systems.