Computer Bridge: A Big Win for AI Planning
15 Jun 1998-Ai Magazine (American Association for Artificial Intelligence)-Vol. 19, Iss: 2, pp 93-106
TL;DR: An overview of the planning techniques that are incorporated into the BRIDGE BARON and what the program's victory signifies for research on AI planning and game playing are discussed.
Abstract: 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.
Citations
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01 Jan 2006
TL;DR: This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms, into planning under differential constraints that arise when automating the motions of virtually any mechanical system.
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.
6,340 citations
Cites background from "Computer Bridge: A Big Win for AI P..."
...They are also good at games such as chess or bridge [899]....
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TL;DR: The SHOP2 planning system as discussed by the authors received one of the awards for distinguished performance in the 2002 International Planning Competition and described the features that enabled it to excel in the competition, especially those aspects of SHOP 2 that deal with temporal and metric planning domains.
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.
838 citations
Proceedings Article•
31 Jul 1999TL;DR: In the authors' 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.
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.
499 citations
Book•
01 Aug 2016TL;DR: This book presents a comprehensive paradigm of planning and acting using the most recent and advanced automated-planning techniques, and explains the computational deliberation capabilities that allow an actor to reason about its actions, choose them, organize them purposefully, and act deliberately to achieve an objective.
Abstract: Autonomous AI systems need complex computational techniques for planning and performing actions. Planning and acting require significant deliberation because an intelligent system must coordinate and integrate these activities in order to act effectively in the real world. This book presents a comprehensive paradigm of planning and acting using the most recent and advanced automated-planning techniques. It explains the computational deliberation capabilities that allow an actor, whether physical or virtual, to reason about its actions, choose them, organize them purposefully, and act deliberately to achieve an objective. Useful for students, practitioners, and researchers, this book covers state-of-the-art planning techniques, acting techniques, and their integration which will allow readers to design intelligent systems that are able to act effectively in the real world.
311 citations
Cites background from "Computer Bridge: A Big Win for AI P..."
...Some examples include scheduling [604], logistics and crisis management [133, 562, 72], spacecraft planning and scheduling [1, 183], equipment configuration [6], manufacturing process planning [550], evacuation planning [438], computer games [551, 113], and robotics [430, 431]....
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15 Jul 2002
TL;DR: This paper describes user interaction in a fully-implemented prototype of an interactive storytelling system, and describes the planning techniques used to control autonomous characters, which derive from HTN planning.
Abstract: In recent years, several paradigms have emerged for interactive storytelling. In character-based storytelling, plot generation is based on the behaviour of autonomous characters. In this paper, we describe user interaction in a fully-implemented prototype of an interactive storytelling system. We describe the planning techniques used to control autonomous characters, which derive from HTN planning. The hierarchical task network representing a characters' potential behaviour constitute a target for user intervention, both in terms of narrative goals and in terms of physical actions carried out on stage. We introduce two different mechanisms for user interaction: direct physical interaction with virtual objects and interaction with synthetic characters through speech understanding. Physical intervention exists for the user in on-stage interaction through an invisible avatar: this enables him to remove or displace objects of narrative significance that are resources for character's actions, thus causing these actions to fail. Through linguistic intervention, the user can influence the autonomous characters in various ways, by providing them with information that will solve some of their narrative goals, instructing them to take direct action, or giving advice on the most appropriate behaviour. We illustrate these functionalities with examples of system-generated behaviour and conclude with a discussion of scalability issues.
184 citations
References
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Book•
01 Jan 1977
TL;DR: Progress to date in the ability of a computer system to understand and reason about actions is described, and the structure of a plan of actions is as important for problem solving and execution monitoring as the nature of the actions themselves.
Abstract: : This report describes progress to date in the ability of a computer system to understand and reason about actions. A new method of representing actions within a computer's memory has been developed, and this new representation, called the "procedural net," has been employed in developing new strategies for solving problems and monitoring the execution of the resulting solutions. A set of running computer programs, called the NOAH (Nets Of Action Hierarchies) system, embodies this representation. Its major goal is to provide a framework for storing expertise about the actions of a particular task domain, and to impart that expertise to a human in the cooperative achievement of nontrivial tasks. A problem is presented to NOAH as a statement that is to be made true by applying a sequence of actions in an initial state of the world. The actions are drawn from a set of actions previously defined to the system. NOAH first creates a one-step solution to the problem, then it progressively expands the level of detail of the solution, filling in ever more detailed actions. All the individual actions, composed into plans at differing levels of detail, are stored in the procedural net. The system avoids imposing unnecessary constraints on the order of the actions in a plan. Thus, plans are represented as partial orderings of actions, rather than as linear sequences. The same data structure is used to guide the human user through a task. Since the system has planned the task at varying levels of detail, it can issue requests for action to the user at varying levels of detail, depending on his/her competence and understanding of the higher level actions. If more detail is needed than was originally planned for, or if an unexpected event causes the plan to go awry, the system can continue to plan from any point during execution. In essence, the structure of a plan of actions is as important for problem solving and execution monitoring as the nature of the actions themselves.
1,267 citations
TL;DR: Examples of the ABSTRIPS system's performance are presented that demonstrate the significant increases in problem-solving power that this approach provides, and some further implications of the hierarchical planning approach are explored.
1,239 citations
Proceedings Article•
01 Aug 1994TL;DR: How the complexity of HTN planning varies with various conditions on the task networks is described.
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.
747 citations
Additional excerpts
...A formal characterization of HTN planning now exists that shows it to be strictly more expressive than planning with STRIPS-style operators (Erol, Nau, and Hendler 1994), which has made it possible to establish a number of formal properties, such as soundness and completeness of planning algorithms…...
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Proceedings Article•
22 Aug 1977TL;DR: The planner (NONLIN) and the Task Formalism (TF) used to hierarchically specify a domain are described, which can aid in the generation of 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.
717 citations
Proceedings Article•
13 Jun 1994TL;DR: This paper presents a formal syntax and semantics for HTn planning and is able to define an algorithm for HTN planning and prove it sound and complete.
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
389 citations
"Computer Bridge: A Big Win for AI P..." refers background in this paper
...…and Hendler 1994), which has made it possible to establish a number of formal properties, such as soundness and completeness of planning algorithms (Erol, Hendler, and Nau 1994), complexity (Erol, Hendler, and Nau 1997), and the relative efficiency of various control strategies (Tsuneto, Nau, and…...
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