|Scholar:||a learned or erudite person, especially one who has profound knowledge of a particular subject.|
|Thesis:||a dissertation resulting from original research, especially when submitted by a candidate for a degree or diploma.|
|Treatise:||a systematic, usually extensive written discourse on a subject.|
|Dissertation:||a lengthy, formal treatise, especially one written by a candidate for the doctoral degree at a university; also referred to as a thesis.|
Foundation of this List
At many universities, colleges, educational institutes, and places of higher education the student will base his or her final written paper on the card game of bridge and attempt to prove either the unpredictability or predictability of certain aspects of the game from the first phase of bidding to the second phase of declaring to the third phase of defending.
It is our attempt to collect these papers for the interest of the reader, who seeks and wishes for more than a simple convention. Please take the opportunity and study some of these most interesting, absorbing, and intriguing scholarly thoughts.
Additions will be made once discovered as this list will never be completed.
Gregg Silveira: Bidding a Bridge Hand: A Thesis on Knowledge Acquisition and Application. Authored in the year 1991 at the Rochester Institute of Technology. His thesis, presented in .pdf file format, was submitted to The Faculty of the Computer Science Department in partial fulfillment of the requirements for the degree of Master of Science in Computer Science. This information has also only been preserved and archived on this site in .pdf file format for future reference.
Computerizing the game of Bridge has not yet met with much success. The efforts to date have fallen short of any reasonable technical proficiency. The game does appear to be perfectly suited for an expert system, however, since the game can be segmented into three contexts (Bidding, Play of the Hand, and Defense), each context can be described by a set of rules, and a series of inferences can be used to fire those rules.
Each of the contexts is reviewed, then bidding is chosen for further research. This thesis claims that the set of all hands subdivides into 11 bidding classifications, based on a number of selection criteria. One of these subsets, Invitational Hands, is studied in detail. Classic knowledge acquisition techniques are used to define Invitational Hands, assimilate the knowledge, then translate the facts, inferences, deductions and suppositions into a knowledge base.
Changes in the state of the auction as bidding progresses are stored in state variables. These state variables are used to navigate the knowledge base to find the next bid. The interaction of state variable settings and facts firing rules in the knowledge base implement a frame architecture.
Paul Bethe: The State of Automated Bridge Play. Dated January 17, 2010. New York University.
The game of Bridge provides a number of research areas to AI researchers due to the many components that constitute the game. Bdding provides the subtle challenge of potential outcome meximization while learning through information gathering, but constrained to a limited rule set. Declarer play can be accomplished through planning and inference. This information has also only been preserved and archived on this site in .pdf file format for future reference.
Prahalad Rajkumar: A Survey of Monte-Carlo Techniques in Games, a Master's Scholarly Paper, University of Maryland.
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 tecdhynique in a computer program's arsenal, augmented by good evaluation functions and clever secondary strategies. In other perfect-information games such as go and clobber, there is very little intuition as to how good a position is, and consequently constructing a good evaluation function is not easy. Furthermore, go has a high branching factor. It turns out that Monte-Carlo simulations, i.e. producing repeated random samples and considering their average in making a decision, work surprisingly well in these games. In imperfect-information games such as bridge and scrabble (the latter game has inherent randomness associated with it as well), Monte-Carlo simulations once again turn out to be useful. This paper examines the use of Monte-Carlo simulations in bridge, scrabble, go, clobber, and backgammon, and reports on how this technique impacts each of these games. This information has also only been preserved and archived on this site in .pdf file format for future reference.
Matthew L. Ginsberg: GIB: Steps toward an expert-level bridge playing program, 1999, University of Oregon, Eugene, Oregon.
Note: GIB is an abbreviation of Goren In a Box
This paper describes GIB, the first bridge- playing program to approach the level of a hu- man expert. ( G I B finished twelfth in a hand- picked field of thirty-four experts at an invita-tional event at the 1998 World Bridge Cham- pionships.) We give a basic overview of the algorithms used, describe their strengths and weaknesses, and present the results of experi- ments comparing GIB to both human opponents and other programs. This information has also only been preserved and archived on this site in .pdf file format for future reference.
Stephen J. J. Smith, Dana Nau, and Tom Throop: A Big Win For AI Planning, published in AI Magazine, Volume 19, Number 2.This information has also only been preserved and archived on this site in .pdf file format for future reference.
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 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 AI planning and game playing.
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