Open AccessProceedings Article
Success in spades: using AI planning techniques to win the world championship of computer bridge
Stephen J. J. Smith,Dana S. Nau,Thomas A. Throop +2 more
- pp 1079-1086
Reads0
Chats0
TLDR
The latest world-championship competition for computer bridge programs was the Baron Barclay World Bridge Computer Challenge, hosted in July 1997 by the American Contract Bridge League, and the winner was a new version of Great Game Products' Bridge Baron program, which uses Hierarchical Task-Network planning techniques.Abstract:
The latest world-championship competition for computer bridge programs was the Baron Barclay World Bridge Computer Challenge, hosted in July 1997 by the American Contract Bridge League. As reported in The New York Times and The Washington Post, the competition's winner was a new version of Great Game Products' Bridge Baron program. This version, Bridge Baron 8, has since gone on the market; and during the last three months of 1997 it was purchased by more than 1000 customers.The Bridge Baron's success also represents a significant success for research on AI planning systems, because Bridge Baron 8 uses Hierarchical Task-Network (HTN) planning techniques to plan its declarer play. This paper gives an overview of those techniques and how they are used.read more
Citations
More filters
Journal ArticleDOI
SHOP2: an HTN planning system
TL;DR: The SHOP2 planning system as discussed by the authors received one of the awards for distinguished performance in the 2002 International Planning Competition and described the features that enabled it to excel in the competition, especially those aspects of SHOP 2 that deal with temporal and metric planning domains.
Journal ArticleDOI
A Gamut of Games
TL;DR: The past successes, current projects, and future research directions for AI using computer games as a research test bed are reviewed.
Proceedings Article
Control strategies in HTN planning: theory versus practice
TL;DR: This paper describes how to overcome the difficulties that can result from the use of backward chaining and partial-order planning by adapting Hierarchical Task-Network planning to use a total-order control strategy that generates the steps of a plan in the same order that those steps will be executed.
Proceedings Article
Hierarchical plan representations for encoding strategic game AI
TL;DR: HTN planners can be used to generate correct plans for coordinated team AI behavior modeled with TMK representations, and TMK models are of interest to game AI because, as it is shown, they are as expressive as HTNs but have more convenient syntax.
Book ChapterDOI
The Games Computers (and People) Play
TL;DR: 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.
References
More filters
Proceedings Article
Total-order multi-agent task-network planning for contract bridge
TL;DR: This paper describes the results of applying a modified version of hierarchical task-network (HTN) planning to the problem of declarer play in contract bridge, and explains why the same technique has been successful in two such diverse domains.
Journal ArticleDOI
A planning approach to declarer play in contract bridge
TL;DR: Although game‐tree search works well in perfect‐information games, it is less suitable for imperfect‐ information games such as contract bridge because of the lack of knowledge about the opponents’ possible moves.
Proceedings Article
Commitment strategies in hierarchical task network planning
TL;DR: This paper compares three commitment strategies for HTN planning: a strategy that delays variable bindings as much as possible, a strategy in which no non-primitive task is expanded until all variable constraints are committed, and a strategies that chooses between expansion and variable instantiation based on the number of branches that will be created in the search tree.
Proceedings Article
Best-first minimax search: othello results
TL;DR: A very simple selective search algorithm for two-player games that always expands next the frontier node that determines the minimax value of the root, and its time overhead per node is similar to that of alpha-beta minimax.
Posted Content
Constraining Influence Diagram Structure by Generative Planning: An Application to the Optimization of Oil Spill Response
TL;DR: This paper works through the optimization of a real world planning problem, with a combination of a generative planning tool and an influence diagram solver, and finds an optimum solution to the employment problem based on the objective function.