Computer Bridge: A Big Win for AI Planning

By Smith, Stephen J. J.; Nau, Dana et al. | AI Magazine, Summer 1998 | Go to article overview

Computer Bridge: A Big Win for AI Planning


Smith, Stephen J. J., Nau, Dana, Throop, Tom, AI Magazine


A computer program that uses AI planning techniques is now the world champion computer prograni 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 such as Chess and Checkers work differently from how humans think about such games. In contrast, our new version of the BRIDGE BARON emulates the way in which a human might plan declarer play in Bridge by using an adaptation of hierarchical task network planning. This article gives an overview of the planning techniques that we have incorporated into the BRIDGE BARON and discusses what the program's victory signifies for research on Al planning and game playing.

One long-standing goal of AI research has been to build programs that play challenging games of strategy well. The classical approach used in Al programs for games of strategy is to do a game tree search using the well-known minimax formula (eq. 1) The minimax computation is basically a bruteforce search: If implemented as formulated here, it would examine every node in the game tree. In practical implementations of minimax game tree searching, a number of techniques are used to improve the efficiency of this computation: putting a bound on the depth of the search, using alpha-beta pruning, doing transposition-table lookup, and so on. However, even with enhancements such as these, minimax computations often involve examining huge numbers of nodes in the game tree. For example, in the recent match between DEEP BLUE and Kasparov, DEEP BLUE examined roughly 60 billion nodes for each move (IBM 1997). In contrast, humans examine, at most, a few dozen board positions before deciding on their next moves (Biermann 1978).

Although computer programs have done well in games such as Chess and Checkers (table 1), they have not done as well in the game of Contract Bridge. Even the best Bridge programs can be beaten by the best players at many local Bridge clubs.

One reason why traditional game tree search techniques do not work well in Bridge is that Bridge is an imperfect-information game. Because Bridge players don't know what cards are in the other players' hands (except for, after the opening lead, what cards are in the dummy's hand), each player has only partial knowledge of the state of the world, the possible actions, and their effects. If we were to construct a game tree that included all the moves a player might be able to make, the size of this tree would vary depending on the particular Bridge deal-but it would include about 5.6 x 1044 leaf nodes in the worst case (Smith 1997, p. 226) and about 2.3 x 1024 leaf nodes in the average case (Lopatin 1992, p. 8). Because a Bridge hand is typically played in just a few minutes, there is not enough time for a game tree search to search enough of this tree to make good decisions.

Our approach to this problem (Smith 1997; Smith, Nau, and Throop 1996a, 1996b, 1996c) grows out of the observation that Bridge is a game of planning. The Bridge literature describes a number of tactical schemes (finessing, ruffing, cross-ruffing, and so on) that people combine into strategic plans for how to play their Bridge hands. We have taken advantage of the planning nature of Bridge by adapting and extending some ideas from hierarchical task network (HTN) planning. We have developed an algorithm for declarer play in Bridge that uses planning techniques to develop game trees whose size depends on the number of different strategies that a player might pursue rather than the number of different possible ways to play the cards. Because the number of sensible strategies is usually much less than the number of possible card plays, we are able to develop game trees that are small enough to be searched completely, as shown in table 2. …

The rest of this article is only available to active members of Questia

Already a member? Log in now.

Notes for this article

Add a new note
If you are trying to select text to create highlights or citations, remember that you must now click or tap on the first word, and then click or tap on the last word.
One moment ...
Default project is now your active project.
Project items

Items saved from this article

This article has been saved
Highlights (0)
Some of your highlights are legacy items.

Highlights saved before July 30, 2012 will not be displayed on their respective source pages.

You can easily re-create the highlights by opening the book page or article, selecting the text, and clicking “Highlight.”

Citations (0)
Some of your citations are legacy items.

Any citation created before July 30, 2012 will labeled as a “Cited page.” New citations will be saved as cited passages, pages or articles.

We also added the ability to view new citations from your projects or the book or article where you created them.

Notes (0)
Bookmarks (0)

You have no saved items from this article

Project items include:
  • Saved book/article
  • Highlights
  • Quotes/citations
  • Notes
  • Bookmarks
Notes
Cite this article

Cited article

Style
Citations are available only to our active members.
Buy instant access to cite pages or passages in MLA, APA and Chicago citation styles.

(Einhorn, 1992, p. 25)

(Einhorn 25)

1. Lois J. Einhorn, Abraham Lincoln, the Orator: Penetrating the Lincoln Legend (Westport, CT: Greenwood Press, 1992), 25, http://www.questia.com/read/27419298.

Cited article

Computer Bridge: A Big Win for AI Planning
Settings

Settings

Typeface
Text size Smaller Larger Reset View mode
Search within

Search within this article

Look up

Look up a word

  • Dictionary
  • Thesaurus
Please submit a word or phrase above.
Print this page

Print this page

Why can't I print more than one page at a time?

Help
Full screen

matching results for page

    Questia reader help

    How to highlight and cite specific passages

    1. Click or tap the first word you want to select.
    2. Click or tap the last word you want to select, and you’ll see everything in between get selected.
    3. You’ll then get a menu of options like creating a highlight or a citation from that passage of text.

    OK, got it!

    Cited passage

    Style
    Citations are available only to our active members.
    Buy instant access to cite pages or passages in MLA, APA and Chicago citation styles.

    "Portraying himself as an honest, ordinary person helped Lincoln identify with his audiences." (Einhorn, 1992, p. 25).

    "Portraying himself as an honest, ordinary person helped Lincoln identify with his audiences." (Einhorn 25)

    "Portraying himself as an honest, ordinary person helped Lincoln identify with his audiences."1

    1. Lois J. Einhorn, Abraham Lincoln, the Orator: Penetrating the Lincoln Legend (Westport, CT: Greenwood Press, 1992), 25, http://www.questia.com/read/27419298.

    Cited passage

    Thanks for trying Questia!

    Please continue trying out our research tools, but please note, full functionality is available only to our active members.

    Your work will be lost once you leave this Web page.

    Buy instant access to save your work.

    Already a member? Log in now.

    Author Advanced search

    Oops!

    An unknown error has occurred. Please click the button below to reload the page. If the problem persists, please try again in a little while.