A Gamut of Games
Reads0
Chats0
TLDR
The past successes, current projects, and future research directions for AI using computer games as a research test bed are reviewed.Abstract:
In 1950, Claude Shannon published his seminal work on how to program a computer to play chess. Since then, developing game-playing programs that can compete with (and even exceed) the abilities of the human world champions has been a long-sought-after goal of the AI research community. In Shannon's time, it would have seemed unlikely that only a scant 50 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 important success stories of AI. This article reviews the past successes, current projects, and future research directions for AI using computer games as a research test bed.read more
Citations
More filters
Journal ArticleDOI
Adaptive game AI with dynamic scripting
TL;DR: It is concluded that dynamic scripting can be successfully applied to the online adaptation of game AI in commercial computer games by implementing the technique in the game Neverwinter Nights.
Book ChapterDOI
Learning to win: case-based plan selection in a real-time strategy game
TL;DR: A plan retrieval algorithm is introduced that, by using three key sources of domain knowledge, removes the assumption of a static opponent and significantly outperforms the best among a set of genetically evolved plans when tested against random Wargus opponents.
Journal ArticleDOI
Is chess the drosophila of artificial intelligence? A social history of an algorithm:
TL;DR: This paper explores the emergence of chess as an experimental technology, its significance in the developing research practices of the AI community, and the unique ways in which the decision to focus on chess shaped the program of AI research in the decade of the 1970s.
Journal ArticleDOI
Computer poker: A review
Jonathan Rubin,Ian Watson +1 more
TL;DR: This paper begins with the first serious attempts to create strong computerised poker players by constructing knowledge-based and simulation-based systems, followed by the use of computational game theory to construct robust poker agents and the advances that have been made in this area.
Journal ArticleDOI
A Review of Real-Time Strategy Game AI
Glen Robertson,Ian Watson +1 more
TL;DR: The main areas of current academic research are in tactical and strategic decision-making, plan recognition, and learning, and the research contributions in each of these areas are outlined, and standardised evaluation methods are proposed to produce comparable re- sults between studies.
References
More filters
Journal ArticleDOI
Learning to Predict by the Methods of Temporal Differences
TL;DR: This article introduces a class of incremental learning procedures specialized for prediction – that is, for using past experience with an incompletely known system to predict its future behavior – and proves their convergence and optimality for special cases and relation to supervised-learning methods.
Journal ArticleDOI
Some studies in machine learning using the game of checkers
TL;DR: In this article, two machine learning procedures have been investigated in some detail using the game of checkers, and enough work has been done to verify the fact that a computer can be programmed so that it will lear...
Journal ArticleDOI
Temporal difference learning and TD-Gammon
TL;DR: The domain of complex board games such as Go, chess, checkers, Othello, and backgammon has been widely regarded as an ideal testing ground for exploring a variety of concepts and approaches in artificial intelligence and machine learning.
Journal ArticleDOI
Some studies in machine learning using the game of checkers
TL;DR: Enough work has been done to verify the fact that a computer can be programmed so that it will learn to play a better game of checkers than can be played by the person who wrote the program.