Institute for Systems Research

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 5,000 randomly generated no-trump deals, Tignum 2 beat the strongest commercially available program by 1394 to 1302, with 2304 ties. These results are statistically significant at the a = 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.

To correct the above problem, we state and prove necessary and sufficient for asymptotic optimality of IDA* on trees. On trees not satisfying our conditions, we show that no best-first limited- memory search algorithm can be asymptotically optimal.

On graphs, IDA* can perform quite poorly. In particular, there are graphs on which IDA* does (22N) node expansions where N is the number of nodes expanded by A*.

2. Experimental tests show that if the heuristic branching factor is low and the node- generation time is high (as in most practical problems), then ITS can provide significant savings in both number of node generations and running time.

3. Our experimental results also suggest that on the Traveling Salesman Problem, both IDA* and ITS are asymptotically optimal on the average if the costs between the cities are drawn from a fixed range. However, if the range of costs grows in proportion to the problem size, then IDA* is not asymptotically optimal. ITS's asymptotic complexity in the later case depends on the amount of memory available to it.

As an example, in this paper we describe the techniques for manufacturing-operation planning used in IMACS (Interactive Manufacturability Analysis and Critiquing System). We compare and contrast them with the techniques used in classical AI planning systems, and point out that some of the techniques used in IMACS may also be useful in other kinds of planning problems.

This paper presents our initial efforts to develop techniques to apply distributed algorithms to the problem of recognizing machining features from solid models. Existing work on recognition of features has focused exclusively on serial computer architectures. Our objective is to show that distributed algorithms can be employed on realistic parts with large numbers of features and many geometric and topological entities to obtain significant improvements in computation time using existing hardware and software tools. Migrating solid modeling applications toward a distributed computing frame-work enables interconnection of many of the autonomous and geographically diverse software tools used in the modern manufacturing enterprise.

This has been implemented on a network of SUN workstations using the ACIS solid modeler and the NIH C++ class library; inter-processor communication is handled with TCP/IP- based network communication tools.

Even after one has decided upon al abstract set of features to use for representing manufacturing operations, the set of feature instances used to represent a complex part is by no means unique. For a complex part, many (sometimes infinitely many) different manufacturing operations can potentially be used to manufacture various portions of the part - - and thus many different feature instances can be used to represent these portions of the part. Some of these feature instances will appear in useful manufacturing plans, and others will not. If the latter feature instances can be discarded at the outset, this will reduce the number of alternative manufacturing plans to be examined in order to find a useful one. Thus, what is required is a systematic means of specifying which feature instances are of interest.

This paper addresses the issue of alternatives by introducing the notion of primary feature instances, which we contend are sufficient to generate all manufacturing plans of interest. To substantiate our argument, we describe how various instances in the primary feature set can be used to produce the desired plans. Furthermore, we discuss how this formulation overcomes computational difficulties faced by previous work, and present some complexity results for this approach in the domain of machined parts.