Computer Bridge - A Big Win for AI Planning

  title={Computer Bridge - A Big Win for AI Planning},
  author={Stephen J. J. Smith and Dana S. Nau and Thomas A. Throop},
  journal={AI Mag.},
A computer program that uses AI planning techniques is now the world champion computer program in the game of Contract Bridge. As reported in The New York Times and The Washington Post, this program -- a new version of Great Game Products' BRIDGE BARON program -- won the Baron Barclay World Bridge Computer Challenge, an international competition hosted in July 1997 by the American Contract Bridge League. It is well known that the game tree search techniques used in computer programs for games… 

Figures and Tables from this paper

Planning for an AI based virtual agents game∗
This work presents here the ongoing work on building a game, ALIVE, that is oriented towards the intensive use of AI controlled Bots, and the work on applying planning techniques for building one such agent.
A Gamut of Games
The past successes, current projects, and future research directions for AI using computer games as a research test bed are reviewed.
Using Probabilistic Knowledge and Simulation to Play Poker
Progress is described in developing a high-performance pokerplaying program that introduces a new betting strategy that returns a probabilistic betting decision, a probability triple, that gives the likelihood of a fold, call or raise occurring in a given situation.
The games computers (and people) play
The dark side of the board: advances in chess Kriegspiel
This thesis presents, documents and tests a multi-sided effort towards making a strong Kriegspiel player, using heuristic searching, retrograde analysis and Monte Carlo tree search algorithms to achieve increasingly higher levels of play.
A Survey of Monte-Carlo Techniques in Games Master ’ s Scholarly Paper
The way computer programs play strategy games is quite different from the way humans play. In perfect-information games like chess and checkers, a game-tree search is the core technique in a computer
The State of Automated Bridge Play
The game of Bridge provides a number of research areas to AI researchers due to the many components that constitute the game, particularly double-dummy play, but researchers have made much progress in each of these sub-fields over the years, but are yet to produce a consistent expert level player.
Combining deliberation and reactive behavior for AI players in the Mini-Tichu card-game
This work focuses on finite state machines, a technique that has been traditionally used for specifying the behavior of non-player characters in video-games, and investigates how they can be coupled with simple search-based methods as a means for developing strong AI players in a simplified version of the game that is called Mini-Tichu.
Learning to bid in bridge
A new decision-making algorithm that allows models to be used for both opponent agents and partners, while utilizing a novel model-based Monte Carlo sampling method to overcome the problem of hidden information is presented.
A Competitive Texas Hold'em Poker Player via Automated Abstraction and Real-Time Equilibrium Computation
It is demonstrated that the game theory-based heads-up Texas Hold'em poker player, GS1, which incorporates very little poker-specific knowledge, is competitive with leading poker-playing programs which incorporate extensive domain knowledge, as well as with advanced human players.


Total-Order Multi-Agent Task-Network Planning for Contract Bridge
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.
A Structure for Plans and Behavior
Progress to date in the ability of a computer system to understand and reason about actions is described, and the structure of a plan of actions is as important for problem solving and execution monitoring as the nature of the actions themselves.
Although game‐tree search works well in perfect‐information games, it is less suitable for imperfect‐information games such as contract bridge. The lack of knowledge about the opponents’ possible
Best-First Minimax Search: Othello Results
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.
Task-network planning using total-order forward search, and applications to bridge and to microwave module manufacture
Because most real-world planning problems are difficult, AI planning researchers have needed to make simplifying assumptions in order to solve some of these problems at all. These simplifying
Generating Project Networks
The planner (NONLIN) and the Task Formalism (TF) used to hierarchically specify a domain are described, which can aid in the generation of project networks.
Applying an AI Planner to Military Operations Planning
The Socap problem domain, how SIPE-2 was used to address this problem, and the strengths and weaknesses of the approach are described.
Plan-Refinement Strategies and Search-Space Size
Current studies have shown that several versions of the popular “least commitment” plan refinement strategy are often outperformed by a fewest alternatives first (FAF) strategy that chooses to refine the plan element that has the smallest number of alternative refinement options.
Constraining Influence Diagram Structure by Generative Planning: An Application to the Optimization of Oil Spill Response
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.