By David Wolfe (auth.), Jonathan Schaeffer, Martin Müller, Yngvi Björnsson (eds.)
The pcs and video games (CG) sequence begun in 1998 with the target of showcasing new advancements in arti?cial intelligence (AI) learn that used video games because the experimental test-bed. The ?rst CG meetings have been held at Hamamatsu,Japan(1998,2000).ComputersandGames2002(CG2002)wasthe 3rd occasion during this biennial sequence. The convention used to be held on the college of Alberta(Edmonton,Alberta,Canada),July25–27,2002.Theprogramconsisted of the most convention that includes refereed papers and keynote audio system, in addition to a number of part occasions together with the video games Informatics Workshop, the brokers in laptop video games Workshop, the buying and selling brokers festival, and the North American laptop pass Championship. CG 2002 attracted one hundred ten individuals from over a dozen nations. a part of the successoftheconferencewasthatitwasco-locatedwiththeNationalConference of the yank organization for Arti?cial Intelligence (AAAI), which started in Edmonton simply as CG 2002 ended. The CG 2002 software had 27 refereed paper shows. The papers ranged over a large choice of AI-related issues together with seek, wisdom, studying, making plans, and combinatorial video game conception. learn test-beds integrated one-player video games (blackjack, sliding-tile puzzles, Sokoban), two-player video games (Amazons, awari, chess, chinese language chess, clobber, cross, Hex, traces of motion, O- ello, shogi), multi-player video games (Chinese checkers, cribbage, international relations, hearts, spades), advertisement video games (role-playing video games, real-time procedure games), and novel functions (Post’s Correspondence Problem).
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Additional info for Computers and Games: Third International Conference, CG 2002, Edmonton, Canada, July 25-27, 2002. Revised Papers
Fig. 5. Discard strategy using co-evolution (same hands dealt to both players). The results for this experiment are comparable to the strategy based around Fig. 65. There is an upward trend with peaks and troughs corresponding to the element of luck introduced by the cut card. 33 after the training period. In cribbage, the cards you discard are not just based on maximizing your own hand score but some attention must be paid to the crib. If it is your crib you also want to maximize that score but if it is your opponent’s crib you will want to minimize the crib score which may involve reducing the score in your own hand.
This is still not good enough for expert play. We decided to take a deeper look at the learning procedure and to see where the problems are and how to overcome them. There are mainly two problems: noisy estimations and generalization. When using reinforcement learning together with function approximation this is one of the key issues. Changing the parameters at one state aﬀects also predictions for other states. One solution for improving generalization in the game of Othello has been proposed by Buro.
All the other hands, when dealt against this hand, will be forced to evolve, no matter Investigation of an Adaptive Cribbage Player 37 how well they are played. Therefore, it seems sensible to deal both players the same hand (even though this is sometimes not normally possible if playing with a physical deck of cards, for example when a player is dealt three nines) and let the player that plays it worse evolve. However, to stop the players converging to the same values and then playing the same way, but maybe sub-optimally, one of the players decides which discards to make based on the values stored in its array and the other player plays randomly.
Computers and Games: Third International Conference, CG 2002, Edmonton, Canada, July 25-27, 2002. Revised Papers by David Wolfe (auth.), Jonathan Schaeffer, Martin Müller, Yngvi Björnsson (eds.)