Success in spades: using AI planning techniques to win the world championship of computer bridge

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.

Control strategies in HTN planning: theory versus practice

Abstract: AI planning techniques are beginning to find use in a number of practical planning domains. However, the backward-chaining and partial-order-planning control strategies traditionally used in AI planning systems are not necessarily the best ones to use for practical planning problems. In this paper, we discuss some of the difficulties that can result from the use of backward chaining and partial-order planning, and we describe how these difficulties can be overcome by adapting Hierarchical Task-Network (HTN) planning to use a total-order control strategy that generates the steps of a plan in the same order that those steps will be executed. We also examine how introducing the total-order restriction into HTN planning affects its expressive power, and propose a way to relax the total-order restriction to increase its expressive power and range of applicability.

A Gamut of Games

Abstract: In 1950, Claude Shannon published his seminal work on how to program a computer to play chess. Since then, developing game-playing programs that can compete with (and even exceed) the abilities of the human world champions has been a long-sought-after goal of the AI research community. In Shannon's time, it would have seemed unlikely that only a scant 50 years would be needed to develop programs that play world-class backgammon, checkers, chess, Othello, and Scrabble. These remarkable achievements are the result of a better understanding of the problems being solved, major algorithmic insights, and tremendous advances in hardware technology. Computer games research is one of the important success stories of AI. This article reviews the past successes, current projects, and future research directions for AI using computer games as a research test bed.

Hierarchical plan representations for encoding strategic game AI

Abstract: In this paper we explore the use of Hierarchical-Task-Network (HTN) representations to model strategic game AI. We will present two case studies. The first one reports on an experiment using HTNs to model strategies for Unreal Tournament® (UT) bots. We will argue that it is possible to encode strategies that coordinate teams of bots in first-person shooter games using HTNs. The second one compares an alternative to HTNs called Task-Method-Knowledge (TMK) process models. TMK models are of interest to game AI because, as we will show, they are as expressive as HTNs but have more convenient syntax. Therefore, HTN planners can be used to generate correct plans for coordinated team AI behavior modeled with TMK representations.

The Games Computers (and People) Play

Abstract: The development of high-performance game-playing programs has been one of the major successes of artificial intelligence research. The results have been outstanding but, with one notable exception (Deep Blue), they have not been widely disseminated. This talk will discuss the past, present, and future of the development of games-playing programs. Case studies for backgammon, bridge, checkers, chess, go, hex, Othello, poker, and Scrabble will be used. The research emphasis of the past has been on high performance (synonymous with brute-force search) for twoplayer perfect-information games. The research emphasis of the present encompasses multi-player imperfect/nondeterministic information games. And what of the future? There are some surprising changes of direction occurring that will result in games being more of an experimental testbed for mainstream AI research, with less emphasis on building world-championship-caliber programs. One of the most profound contributions to mankind’s knowledge has been made by the artificial intelligence (AI) research community: the realization that intelligence is not uniquely human. 1 Using computers, it is possible to achieve human-like behavior in nonhumans. In other words, the illusion of human intelligence can be created in a computer. This idea has been vividly illustrated throughout the history of computer games research. Unlike most of the early work in AI, game researchers were interested in developing high-performance, real-time solutions to challenging problems. This led to an ends-justify-the-means attitude: the result—a strong chess program—was all that mattered, not the means by which it was achieved. In contrast, much of the mainstream AI work used simplified domains, while eschewing real-time performance objectives. This research typically used human intelligence as a model: one only had to emulate the human example to achieve intelligent behavior. The battle (and philosophical) lines were drawn. The difference in philosophy can be easily illustrated. The human brain and the computer are different machines, each with its own sets of strengths and weaknesses. Humans are good at, for example, learning, reasoning by analogy, and

Planning in a hierarchy of abstraction spaces

Abstract: A problem domain can be represented as a hierarchy of abstraction spaces in which successively finer levels of detail are introduced. The problem solver ABSTRIPS, a modification of STRIPS, can define an abstraction space hierarchy from the STRIPS representation of a problem domain, and it can utilize the hierarchy in solving problems. Examples of the system's performance are presented that demonstrate the significant increases in problem-solving power that this approach provides. Then some further implications of the hierarchical planning approach are explored.

HTN planning: complexity and expressivity

Abstract: Most practical work on AI planning systems during the last fifteen years has been based on hierarchical task network (HTN) decomposition, but until now, there has been very little analytical work on the properties of HTN planners. This paper describes how the complexity of HTN planning varies with various conditions on the task networks.

Generating project networks

Abstract: Procedures for optimization and resource allocation in Operations Research first require a project network for the task to be specified. The specification of a project network is at present done in an intuitive way. AI work in plan formation has developed formalisms for specifying primitive activities, and recent work by Sacerdoti (1975a) has developed a planner able to generate a plan as a partially ordered network of actions. The "planning: a joint AI/OR approach" project at Edinburgh has extended such work and provided a hierarchic planner which can aid in the generation of project networks. This paper describes the planner (NONLIN) and the Task Formalism (TF) used to hierarchically specify a domain.

Planning in a hierarchy of abstraction spaces

Abstract: A problem domain can be represented as a hierarchy of abstraction spaces in which successively finer levels of detail are introduced. The problem sotver ABSTRIPS, a modification of STRIPS, can define an abstraction space hierarchy from the STRIPS representatien of a problem domain, and it can utilize the hierarchy in solving problems. Examples of the system's performance are presented that demonstrate the significant increases in problem-solving power that this approach provides. Then some further implications of the hierarchical planning approach are explored.

UMCP: a sound and complete procedure for hierarchical task-network planning

Abstract: One big obstacle to understanding the nature of hierarchical task network (HTN) planning has been the lack of a clear theoretical framework In particular, no one has yet presented a clear and concise HTN algorithm that is sound and complete In this paper, we present a formal syntax and semantics for HTN planning Based on this syntax and semantics, we are able to define an algorithm for HTN planning and prove it sound and complete