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 most recent in a chain of annual re gional conferences at which advances in man made intelligence are suggested. This sequence now draws many foreign papers, and certainly the structure of this system committee displays this geographical range. along with the standard tutorials and workshops, this 12 months the convention incorporated a spouse sympo sium at which papers on business appUcations have been awarded. The symposium papers were released in a separate quantity edited by way of Eric Tsui. Ar99 is geared up by means of the college of latest South Wales, and subsidized through the Aus tralian computing device Society, the Commonwealth medical and commercial learn business enterprise (CSIRO), desktop Sciences company, the KRRU workforce at Griffith college, the Australian man made Intelligence Institute, and Neuron- Works Ltd. Ar99 bought over a hundred and twenty convention paper submissions, of which approximately o- 3rd have been from outdoors Australia. promenade those, 39 have been authorized for normal presentation, and one other 15 for poster exhibit. those court cases include the entire commonplace papers and prolonged summaries of the poster papers. All papers have been refereed, generally by way of or 3 reviewers chosen by way of participants of this system committee, and a listing of those reviewers seems later. The technical software comprised days of workshops and tutorials, fol lowed by means of 3 days of convention and symposium plenary and paper sessions.
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Additional resources for Advanced Topics in Artificial Intelligence: 12th Australian Joint Conference on Artificial Intelligence, AI’99 Sydney, Australia, December 6–10, 1999 Proceedings
Use linear regression to find the least squares solution to this system of equations for A. ) 3. Update weight vector according to how much of an improvement it is over the previous weight vector: A('=):=(l-pfe_i)A+pfc_iA(*-i) (Remark: this was found to give better results than just putting A^*') := ^(fe-i). The latter gave erratic performance, frequently throwing away good weight vectors in favour of inferior ones. ) 4. Next Learning Iteration: k :— k + l. 6. Combining weight vectors: A final weight vector A* is chosen from all the A^''' found at each of the successive learning iterations.
Farr and David R. Powell These results show that learning produced a significantly better player for Game 4, Game 5, Lose-chess, and Draughts. For Chess, significant improvement was obtained with the third weight-combining technique, although the average performance over all three techniques is not signifcantly better. In Games 1, 2 and 3, the Learning Player's performance did not differ significantly from the baseline. Of the three weight-combining techniques considered, the third seemed to be the best, in that it always produced improved play (over baseline) whenever either of the other two did.
As in , then, one way to improve a set of weights is to adjust them so that the resulting evaluation function more nearly approximates the backed up value obtained from using the current weights at the leaves. , assembUng equations ^'o(A('=),P) = t;d(A(*-^),P) Unsupervised Leeirning in Metagame 27 for a suitably large number of positions P and finding the least squares solution of this over-constrained system for A^''^. This process may converge on a new weight vector A. We thus need a large number of positions P.
Advanced Topics in Artificial Intelligence: 12th Australian Joint Conference on Artificial Intelligence, AI’99 Sydney, Australia, December 6–10, 1999 Proceedings by Geoffrey Holmes, Mark Hall, Eibe Prank (auth.), Norman Foo (eds.)