champion checkers program CHINOOK (Schaeffer, 1997), is also reporting significant success in the poker domain (Billings, Papp, Schaeffer, & Szafron, 1998).

The situation in bridge has been bleaker. In addition, because the American Contract Bridge League (ACBL) does not rank the bulk of its players in meaningful ways, it is difficult to compare the strengths of competing programs or players.

In general, performance at bridge is measured by playing the same deal twice or more, with the cards held by one pair of players being given to another pair during the replay and the results then being compared. 2 A "team" in a bridge match thus typically consists of two pairs, with one pair playing the North/South (N/S) cards at one table and the other pair playing the E/W cards at the other table. The results obtained by the two pairs are added; if the sum is positive, the team wins this particular deal and if negative, they lose it.

In general, the numeric sum of the results obtained by the two pairs is converted to International Match Points, or IMPs. The purpose of the conversion is to diminish the impact of single deals on the total, lest an abnormal result on one particular deal have an unduly large impact on the result of an entire match.

Jeff Goldsmith 1 reports that the standard deviation on a single deal in bridge is about 5.5 IMPs, so that if two roughly equal pairs were to play the deal, it would not be surprising if one team beat the other by about this amount. It also appears that the difference between an average club player and an expert is about 1.5 IMPs (per deal played); the strongest players in the world are approximately 0.5 IMPs/deal better still. Excepting GIB, the strongest bridge playing programs appear to be slightly weaker than average club players.

Progress in computer bridge has been slow.  An incorporation of planning techniques into Bridge Baron, for example, appears to have led to a performance increment of approximately 1/3 IMP per deal (Smith, Nau, & Throop, 1996). This modest improvement still leaves Bridge Baron far shy of expert-level (or even good amateur-level) performance.

Prior to 1997, bridge programs generally attempted to duplicate human bridge-playing methodology in that they proceeded by attempting to recognize the class into which any particular deal fell: finesse, end play, squeeze, etc. Smith et al.'s work on the Bridge Baron program uses planning to extend this approach, but the plans continue to be constructed from human bridge techniques. Nygate and Sterling's early work on PYTHON (Sterling & Nygate, 1990) produced an expert system that could recognize squeezes but not prepare for them. In retrospect, perhaps we should have expected this approach to have limited success; certainly chess-playing programs that have attempted to mimic human methodology, such as paradise (Wilkins, 1980), have fared poorly.

GIB, introduced in 1998, works differently. Instead of modeling its play on techniques used by humans, GIB uses brute-force search to analyze the situation in which it finds itself.

A variety of techniques are then used to suggest plays based on the results of the brute-force search. This tenique has been so successful that all competitive bridge programs have switched from a knowledge-based approach to a search-based approach.

GIB's cardplay based on brute-force techniques was at the expert level (see Section 3)
en without some of the extensions that we discuss in Section 5 and subsequently. The weakest part of GIB's game is bidding, where it relies on a large database of rules describing 2. The rules of bridge are summarized in Appendix A.