A Planning Approach to Declarer Play in Contract Bridge

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 moves gives the game tree a very large branching factor, making it impossible to search a significant portion of this tree in a reasonable amount of time. This paper describes our approach for overcoming this problem. We represent information about bridge in a task network that is extended to represent multi-agency and uncertainty. Our game-playing procedure uses this task network to generate game trees in which the set of alternative choices is determined not by the set of possible actions, but by the set of available tactical and strategic schemes. We have tested this approach on declarer play in the game of bridge, in an implementation called Tignum 2. On 5000 randomly generated notrump deals, Tignum 2 beat the strongest commercially available program by 1394 to 1302, with 2304 ties. These results are statistically significant at the alpha = 0.05 level. Tignum~2 searched an average of only 8745.6 moves per deal in an average time of only 27.5 seconds per deal on a Sun SPARCstation 10. Further enhancements to Tignum~2 are currently underway. (Also cross-referenced as UMIACS-TR-95-85

A Planning Approach To Declarer Play In Contract Bridge

ly, we will consider the current state S (or any other state) to be a collection of ground atoms (that is, completely instantiated predicates) of some function- A Planning Approach to Declarer Play in Contract Bridge 5 free first-order language L that is generated by finitely many constant symbols and predicate symbols. We do not care whether this is how S would actually be represented in an implementation of a game-playing program. Among other things, S will contain information about who the players are, and whose turn it is to move. To represent this information, we will consider S to include a ground atom Agent(x) for each player x, and a ground atom Turn(y) for the player y whose turn it is to move. For example, in the game of bridge, S would include the ground atoms Agent(North), Agent(South), Agent(East), and Agent(West). If it were South's turn to move, then S would include the ground atom Turn(South). We will be considering S from the point of view of a particular player P ..

Compliance flow: an intelligent workflow management system to support engineering processes

This work is about extending the scope of current workflow management systems to support engineering processes. On the one hand engineering processes are relatively dynamic, and on the other their specification and performance are constrained by industry standards and guidelines for the sake of product acceptability, such as IEC 61508 for safety and ISO 9001 for quality. A number of technologies have been proposed to increase the adaptability of current workflow systems to deal with dynamic situations. A primary concern is how to support open-ended processes that cannot be completely specified in detail prior to their execution. A survey of adaptive workflow systems is given and the enabling technologies are discussed. Engineering processes are studied and their characteristics are identified and discussed. Current workflow systems have been successfully used in managing "administrative" processes for some time, but they lack the flexibility to support dynamic, unpredictable, collaborative, and highly interdependent engineering processes. [Continues.