Book ChapterDOI
The Games Computers (and People) Play
Jonathan Schaeffer
- Vol. 52, pp 1179
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TLDR
The past, present, and future of the development of games-playing programs are discussed and some surprising changes of direction occurring that will result in games being more of an experimental testbed for mainstream AI research, with less emphasis on building world-championship-caliber programs.Abstract:
The development of high-performance game-playing programs has been one of the major successes of artificial intelligence research. The results have been outstanding but, with one notable exception (Deep Blue), they have not been widely disseminated. This talk will discuss the past, present, and future of the development of games-playing programs. Case studies for backgammon, bridge, checkers, chess, go, hex, Othello, poker, and Scrabble will be used. The research emphasis of the past has been on high performance (synonymous with brute-force search) for twoplayer perfect-information games. The research emphasis of the present encompasses multi-player imperfect/nondeterministic information games. And what of the future? There are some surprising changes of direction occurring that will result in games being more of an experimental testbed for mainstream AI research, with less emphasis on building world-championship-caliber programs. One of the most profound contributions to mankind’s knowledge has been made by the artificial intelligence (AI) research community: the realization that intelligence is not uniquely human. 1 Using computers, it is possible to achieve human-like behavior in nonhumans. In other words, the illusion of human intelligence can be created in a computer. This idea has been vividly illustrated throughout the history of computer games research. Unlike most of the early work in AI, game researchers were interested in developing high-performance, real-time solutions to challenging problems. This led to an ends-justify-the-means attitude: the result—a strong chess program—was all that mattered, not the means by which it was achieved. In contrast, much of the mainstream AI work used simplified domains, while eschewing real-time performance objectives. This research typically used human intelligence as a model: one only had to emulate the human example to achieve intelligent behavior. The battle (and philosophical) lines were drawn. The difference in philosophy can be easily illustrated. The human brain and the computer are different machines, each with its own sets of strengths and weaknesses. Humans are good at, for example, learning, reasoning by analogy, andread more
Citations
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Journal ArticleDOI
Self-Play and Using an Expert to Learn to Play Backgammon with Temporal Difference Learning
TL;DR: This paper compares three different methods for generating training games using temporal difference methods with neural networks to learn the game of backgammon and examines whether it is helpful to learn directly from the board evaluations given by the expert.
Proceedings ArticleDOI
An investigation, using co-evolution, to evolve an Awari player
J.E. Davis,Graham Kendall +1 more
TL;DR: This work shows how an awari player can be evolved using a co-evolutionary approach where computer players play against one another, with the strongest players surviving and being mutated using an evolutionary strategy (ES).
Book
Learning to play strong poker
TL;DR: This chapter describes and evaluates the implicit and explicit learning in the poker program LOKI, a program capable of playing reasonably strong poker, but there remains considerable research to be done to play at a world-class level.
Dissertation
PSO-based coevolutionary Game Learning
TL;DR: The PSO-based coevolutionary learning technique described and examined in this study shows promise in evolving intelligent evaluators for the aforementioned games, and further study will be conducted to analyse its scalability to larger search spaces and games of varying complexity.
Proceedings ArticleDOI
Behavioral biometrics for verification and recognition of malicious software agents
TL;DR: This work proposes applying statistical behavior modeling techniques developed for recognition of humans to the identification and verification of intelligent and potentially malicious software agents and demonstrates feasibility of such methods for both artificial agent verification and even for recognition purposes.
References
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