Stephen J. J. Smith

Bio: Stephen J. J. Smith is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Game tree & Hierarchical task network. The author has an hindex of 9, co-authored 15 publications receiving 402 citations. Previous affiliations of Stephen J. J. Smith include Hood College.

Total-order multi-agent task-network planning for contract bridge

Abstract: This paper describes the results of applying a modified version of hierarchical task-network (HTN) planning to the problem of declarer play in contract bridge. 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. This approach avoids the difficulties that traditional game-tree search techniques have with imperfect-information games such as bridge--but it also differs in several significant ways from the planning techniques used in typical HTN planners. We describe why these modifications were needed in order to build a successful planner for bridge. This same modified HTN planning strategy appears to be useful in a variety of application domains--for example, we have used the same planning techniques in a process-planning system for the manufacture of complex electro-mechanical devices (Hebbar et al. 1996). We discuss why the same technique has been successful in two such diverse domains.

A planning approach to declarer play in contract bridge

Abstract: 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 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 α= 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.

Strategic planning for imperfect-information games

Abstract: Although game-tree search works well in perfectinformation games, there are problems in trying to use it for imperfect-information games such as bridge. The lack of knowledge about the opponents’ possible moves gives the game tree a very large branching factor, making the ~ree so immense that game-tree searching is infeasible. In this paper, we describe our approach for overcoming this problem. We develop a model of imperfect-information games, and describe how to represent information about the game using a modified version of a task network that is extended to represent multi-agency and uncertainty. We present a game-playing procedure that uses this approach 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. In our tests of this approach on the game of bridge, we found that it generated trees having a much smaller branching factor than would have been generated by conventional game-tree search techniques. Thus, even ill the worst case, the game tree contained only about 1300 nodes, as opposed to the approximately 6.01 x 1044 nodes that would have been produced by a brute-force game tree search in tile worst case. Furthermore, our approach successfully solved typical bridge problems that matched situations in its knowledge base. These preliminary tests suggest that our approach has the potential to yield bridge-playing programs much better than existing ones--and thus we have begun to build a full implementation. This work supported in part by an ATT Levy and Newborn, 1982), checkers (Samuel, 1967), and othello (Lee and Mahajan, 1990)), it does not ways work so well in other games. One example is the game of bridge. Bridge is an imperfect-information game, in which no player has complete knowledge about the state of the world, the possible actions, and their effects. As a consequence, the branching factor of the game tree--and thus the size of the tree itself--is very large. Searching this game tree is completely infeasible, because the bridge deal must be played in just a few minutes (in contrast to a chess game, which can go on for several hours). Thus, a different approach is needed. In this paper, we describe an approach to this problem, based on the observation that bridge is a game of planning. The bridge literature describes a number of tactical and strategic schemes for dealing with various card-playing situations. It appears that there is a small number of such schemes for each bridge hand, and that each of them can be expressed relatively simply. To play bridge, many humans use these schemes to create plans. They then follow those plans for some number of tricks, replanning when appropriate. We have taken advantage of the planning nature of bridge, by adapting and extending some ideas from task-network planning. To represent the tactical and strategic schemes of card-playing in bridge, we use instances of multi-agent methods--structures similar to the task decompositions used in hierarchical singleagent planning systems such as Nonlin (Tate, 1976; Tate, 1977), NOAH (Sacerdoti, 1977), and MOLGEN (Stefik, 1981), but modified to represent multi-agency and uncertainty. To generate game trees, we use a procedure similar to task-network decomposition. This approach produces a game tree in which the number of branches from each state is determined not by the number of actions that an agent can perform, but instead by the number of different tactical and strategic schemes that the agent can employ. If at each node of the tree, the number of applicable schemes is From: AAAI Technical Report FS-93-02. Compilation copyright © 1993, AAAI (www.aaai.org). All rights reserved.

Plan-Based Evaluation of Designs for Microwave Modules

Abstract: This paper describes EDAPS, an integrated system for designing and planning the manufacture of microwave modules. Microwave modules are complex devices having both electrical and mechanical properties, and EDAPS integrates electrical design, mechanical design, and process planning for both the mechanical and electrical domains. Since EDAPS generates process plans concurrently with design, we are developing ways for EDAPS to provide feedback about manufacturability, cost, and lead time to the designers, based on the process plans to be used in the manufacture of their designs.

Toward an analysis of forward pruning

Abstract: Sever0,1 early game-playing computer programs used forward pruning (i.e., the practice of delib- erately ignoring nodes that are believed unlikely to affect a game tree's minimax value), but this technique did not seem to result in good decision- making. The poor performance of forward prun- ing presents a major puzzle for AI research on game playing, because some version of forward pruning seems to be "what people do," and the best chess-playing programs still do not play as well as the best humans. As a step toward deeper understanding of how for- ward pruning affects quality of play, in this paper we set up a model of forward pruning on two ab- stract classes of binary game trees, and we use this model to investigate how forward pruning affects the accuracy of the minimax values returned. The primary result of our study is that forward prun- ing does better when there is a high correlation among the minimax values of sibling nodes in a game tree. This result suggests that forward pruning may possibly be a useful decision-making technique in certain kinds of games. In particular, we believe that bridge may be such a game.

Planning Algorithms: Introductory Material

Abstract: Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

Bandit based monte-carlo planning

Abstract: For large state-space Markovian Decision Problems Monte-Carlo planning is one of the few viable approaches to find near-optimal solutions. In this paper we introduce a new algorithm, UCT, that applies bandit ideas to guide Monte-Carlo planning. In finite-horizon or discounted MDPs the algorithm is shown to be consistent and finite sample bounds are derived on the estimation error due to sampling. Experimental results show that in several domains, UCT is significantly more efficient than its alternatives.

SHOP2: an HTN planning system

Abstract: The SHOP2 planning system received one of the awards for distinguished performance in the 2002 International Planning Competition. This paper describes the features of SHOP2 which enabled it to excel in the competition, especially those aspects of SHOP2 that deal with temporal and metric planning domains.

SHOP: Simple Hierarchical Ordered Planner

Abstract: SHOP (Simple Hierarchical Ordered Planner) is a domain-independent HTN planning system with the following characteristics. • SHOP plans for tasks in the same order that they will later be executed. This avoids some goal-interaction issues that arise in other HTN planners, so that the planning algorithm is relatively simple. • Since SHOP knows the complete world-state at each step of the planning process, it can use highly expressive domain representations. For example, it can do planning problems that require complex numeric computations. • In our tests, SHOP was several orders of magnitude faster man Blackbox and several times faster than TLpian, even though SHOP is coded in Lisp and the other planners are coded in C.

Character-based interactive storytelling

Abstract: Interactive storytelling is a privileged application of intelligent visual actor technology. The authors introduce their character-based interactive storytelling prototype that uses hierarchical task network planning techniques, which support story generation and any-time user intervention.