A computer program capable of acting intelligently in the world must have a general representation of the world in terms of which its inputs are interpreted. Designing such a program requires commitments about what knowledge

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A Shannon C-type strategy program, VICTOR, is written for Connect-Four, based on nine strategic rules. Each of these rules is proven to be correct, implying that conclusions made by VICTOR are correct. Using VICTOR, strategic rules where found which can be used by Black to at least draw the game, on each 7 × (2n) board, provided that White does not start at the middle column, as well as on any 6 × (2n) board. In combination with conspiracy-number search, search tables and depth-first search, VICTOR was able to show that White can win on the standard 7 × 6 board. Using a database of approximately half a million positions, VICTOR can play real

This thesis investigates problem-solving in domains featuring incomplete information and multiple agents with opposing goals. In particular, we describe Finesse --- a system that forms plans for the problem of declarer play in the game of Bridge. We begin by examining the problem of search. We formalise a best defence model of incomplete information games in which equilibrium point strategies can be identified, and identify two specific problems that can affect algorithms in such domains. In Bridge, we show that the best defence model corresponds to the typical model analysed in expert texts, and examine search algorithms which overcome the problems we have identified. Next, we look at how planning algorithms can be made to cope with the difficulties of such domains. This calls for the development of new techniques for representing uncertainty and actions with disjunctive effects, for coping with an opposition, and for reasoning about compound actions. We tackle these problems with a...

Introduction Search is a universal problem-solving mechanism in artificial intelligence (AI). In AI problems, the sequence of steps required for solution of a problem are not known a priori, but often must be determined by a systematic trial-and-error exploration of alternatives. The problems that have been addressed by AI search algorithms fall into three general classes: single-agent pathfinding problems, two-player games, and constraint-satisfaction problems. Classic examples in the AI literature of pathfinding problems are the sliding-tile puzzles, including the 3 \Theta 3 Eight Puzzle (see Fig. 1) and its larger relatives the 4 \Theta 4 Fifteen Puzzle, and 5 \Theta 5 Twenty-Four Puzzle. The Eight Puzzle consists of a 3 \Theta 3 square frame containing eight numbered square tiles, and an empty position called the blank. The legal operators are to slide any tile that is h

In the 40 years since Arthur Samuel's 1960 Advances in Computers chapter, enormous progress has been made in developing programs to play games of skill at a level comparable to, and in some cases beyond, what the best humans can achieve. In Samuel's time, it would have seemed unlikely that only a scant 40 years would be needed to develop programs that play world-class backgammon, checkers, chess, Othello, and Scrabble. These remarkable achievements are the result of a better understanding of the problems being solved, major algorithmic insights, and tremendous advances in hardware technology. Computer games research is one of the major success stories of articial intelligence. This chapter can be viewed as a successor to Samuel's work. A review of the scientic advances made in developing computer games is given. These ideas are the ingredients required for a successful program. Case studies for the games of backgammon, bridge, checkers, chess, Othello, poker, and Scrabb...

This thesis is about inductive learning, or learning from examples. The goal has been to investigate ways of improving learning algorithms. The chess end-game "King and Rook against King" (KRK) was chosen, and a number of benchmark learning tasks were defined within this domain, sufficient to over-challenge stateof -the-art learning algorithms. The tasks comprised learning rules to distinguish (1) illegal positions and (2) legal positions won optimally in a fixed number of moves. From our experimental results with task (1) the best-performing algorithm was selected and a number of improvements were made. The principal extension to this generalisation method was to alter its representation from classical logic to a non-monotonic formalism. A novel algorithm was developed in this framework to implement rule specialisation, relying on the invention of new predicates. When experimentally tested this combined approach did not at first deliver the expected performance gains due to restrictio...

Several early game-playing computer programs used forward pruning (i.e., the practice of deliberately ignoring nodes that are believed unlikely to a ect a game tree's minimax value), but this technique did not seem to result in good decision-making. The poor performance of forward pruning presents a major puzzle for AI research ongameplaying, because some version of forward pruning seems to be \what people do," and the best chess-playing programs still do not play as well as the best humans. As a step toward deeper understanding of forward pruning, we have set up models of forward pruning on two di erent kinds of game trees, and used these models to investigate how forward pruning a ects the probability ofchoosing the correct move. In our studies, forward pruning did better than minimaxing when there was a high correlation among the minimax values of sibling nodes in a game tree. This result suggests that forward pruning may possibly be a useful decision-making technique in certain kinds of games. In particular, we believe that bridge may be such a game.

This paper introduces experience-based learning. This term refers to that type of unsupervised reinforcement learning in which almost all responsibility for the learning process is given to the system. These responsibilities include state evaluation, operator (move) selection, feature discovery and feature significance. As a learning framework experience-based learning can be applied to many problem domains (Levinson et al., 1992) . The types of problem domains considered here are restricted to complex problem domains characterized by three features. First the problem must have a formulation as a state space search. Further, reinforcement is only provided occasionally, and for many problems only at the end of a given search. Finally, the cardinality of the state space must be sufficiently large so that attempting to store all states is impractical.

Games with imperfect information are an interesting and important class of games. They include most card games (e.g., bridge and poker), as well as many economic and political models. Here, we investigate algorithms for solving imperfect information games expressed in their extensive (game-tree) form. In particular, we consider algorithms for the simplest form of solution --- a pure-strategy equilibrium point. We introduce to the artificial intelligence (AI) community a classic algorithm due to Wilson, that finds a pure-strategy equilibrium point in one-player games with perfect recall. Wilson's algorithm, which we call IMP-minimax, runs in time linear in the size of the game-tree searched. In contrast to Wilson's result, Koller & Meggido have shown that finding a pure-strategy equilibrium point in one-player games without perfect recall is NP-hard. Here, we provide another contrast to Wilson's result --- we show that in games with perfect recall, finding a pure-strategy equilibrium p...