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By David Wolfe (auth.), Jonathan Schaeffer, Martin Müller, Yngvi Björnsson (eds.)
The desktops and video games (CG) sequence begun in 1998 with the target of showcasing new advancements in arti?cial intelligence (AI) examine 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 collage 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 aspect occasions together with the video games Informatics Workshop, the brokers in machine video games Workshop, the buying and selling brokers festival, and the North American laptop cross Championship. CG 2002 attracted one hundred ten contributors from over a dozen international locations. 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 application 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 thought. learn test-beds incorporated one-player video games (blackjack, sliding-tile puzzles, Sokoban), two-player video games (Amazons, awari, chess, chinese language chess, clobber, move, Hex, strains 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 process games), and novel functions (Post’s Correspondence Problem).
Read Online or Download Computers and Games: Third International Conference, CG 2002, Edmonton, Canada, July 25-27, 2002. Revised Papers PDF
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Additional info for Computers and Games: Third International Conference, CG 2002, Edmonton, Canada, July 25-27, 2002. Revised Papers
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Schaeffer et al. ): CG 2002, LNCS 2883, pp. 42–60, 2003. c Springer-Verlag Berlin Heidelberg 2003 Learning a Game Strategy Using Pattern-Weights and Self-play 43 Games were played utilizing a database of knowledge that was consulted for decision-making on every turn. Partial state information was stored in a structure called a pattern-weight pair [2]. For each move, the automated player searched the database for partial state information that best matched the current state, then calculated the best move from that state.
If p is too high, then even good discards may be altered, if the cut card is an adverse one. Therefore, if p is set too high, the statistical array is too volatile. This means that a “final” discard pattern is never reached, as the player constantly evolves, trying to find a non-existent discard pattern that satisfies the over-stringent evaluation function. In order to try and find a good quality value for p, a series of experiments were run that varied p and recorded the average score. The same set of training hands were used for each value of p, 250,000 iterations were executed.
2 Evolutionary Strategies Evolutionary strategies (ES) are closely related to genetic algorithms and evolutionary programming. Originally they used only mutation, only used a population of size one, and were used to optimize real-valued variables. More recently, ES’s have used a population size greater then one, they have used crossover and have also been applied to discrete variables [23, 24]. However, their main use is still in finding values for real variables by a process of mutation, rather than crossover.