By Geoffrey Holmes, Mark Hall, Eibe Prank (auth.), Norman Foo (eds.)
The twelfth Australian Joint convention on synthetic Intelligence (AI'QQ) held in Sydney, Australia, 6-10 December 1999, is the newest in a sequence of annual re gional conferences at which advances in synthetic intelligence are said. This sequence now draws many overseas papers, and certainly the structure of this system committee displays this geographical range. in addition to the standard tutorials and workshops, this yr the convention incorporated a better half sympo sium at which papers on business appUcations have been offered. The symposium papers were released in a separate quantity edited by means of Eric Tsui. Ar99 is geared up by means of the collage of latest South Wales, and subsidized by means of the Aus tralian machine Society, the Commonwealth medical and commercial learn organization (CSIRO), computing device Sciences company, the KRRU workforce at Griffith college, the Australian man made Intelligence Institute, and Neuron- Works Ltd. Ar99 bought over one hundred twenty convention paper submissions, of which approximately o- 3rd have been from outdoors Australia. promenade those, 39 have been accredited for normal presentation, and a different 15 for poster show. those complaints include the total typical papers and prolonged summaries of the poster papers. All papers have been refereed, generally by way of or 3 reviewers chosen by means of individuals of this system committee, and an inventory of those reviewers seems to be later. The technical software comprised days of workshops and tutorials, fol lowed via 3 days of convention and symposium plenary and paper sessions.
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Epstein, The intelligent novice — learning to play better, in: D. N. L. Levy and D. F. ). Heuristic Programming in Artificial Intelligence: The First Computer Olympiad, EUis Horwood, Chichester, 1989. 5. S. L. Epstein, J. Gelfand and J. Lesniak, Pattern-based learning and spatieilly oriented concept formation in a multi-agent, decision-making expert, Computational Intelligence 12 (1996) 198-221. 6. R. A. Levinson, Exploiting the physics of state-space search, in: S. Epstein and R. ). Games: Planning & Learning, AAA!
These matters invite further exploration. We have concentrated on learning advisor weights. It would be interesting to learn values for other parameters which influence play. g. for possible promotion, or control of a square) decUne with distance . For another example, consider the search strategy. Cron  introduced search advisors to control search in Metagame, and studied the effect of such advisors' weights on play. Unsupervised Learning in Metagame 35 Acknowledgements We t h a n k Barney Pell and David Dowe for their helpful comments a n d suggestions.
Video indexing using motion vectors. In Conference Proceedings of SPIE Visual Communication and Image Processing 1992, pages 1522-1530, 1992. 2. Douglas H. Fisher. Knowledge aquisition via incremental conceptual clustering. In Jude W. Shavhk and Thomas G. Diettrich, editors. Readings in Machine Learning, pages 267-284. Morgan Kauffman, 1990. 3. John H. Gennari, Pat Langley, and Doug Fisher. Models of incremental concept formation. Artificial Intelligence, 40:11-61, 1989. 4. K. Hemna J. R. Bergen, P.