Proceedings Article•
Control strategies in HTN planning: theory versus practice
01 Jul 1998-pp 1127-1133
TL;DR: This paper describes how to overcome the difficulties that can result from the use of backward chaining and partial-order planning by adapting Hierarchical Task-Network 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.
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.
Citations
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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
TL;DR: 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.
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.
341 citations
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
Proceedings Article•
31 Jul 1999TL;DR: The main objective is to show that a carefully chosen IP formulation significantly improves the "strength" of the LP relaxation, and that the resultant LPs are useful in solving the IP and the associated planning problems.
Abstract: Recent research has shown the promise of using propositional reasoning and search to solve AI planning problems In this paper, we further explore this area by applying Integer Programming to solve AI planning problems The application of Integer Programming to AI planning has a potentially significant advantage, as it allows quite naturally for the incorporation of numerical constraints and objectives into the planning domain Moreover, the application of Integer Programming to AI planning addresses one of the challenges in propositional reasoning posed by Kautz and Selman, who conjectured that the principal technique used to solve Integer Programs--the linear programming (LP) relaxation--is not useful when applied to propositional search
We discuss various IP formulations for the class of planning problems based on STRIPS-style planning operators Our main objective is to show that a carefully chosen IP formulation significantly improves the "strength" of the LP relaxation, and that the resultant LPs are useful in solving the IP and the associated planning problems Our results clearly show the importance of choosing the "right" representation, and more generally the promise of using Integer Programming techniques in the AI planning domain
120 citations
References
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Book•
01 Jan 1980TL;DR: This classic introduction to artificial intelligence describes fundamental AI ideas that underlie applications such as natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, and intelligent data retrieval.
Abstract: A classic introduction to artificial intelligence intended to bridge the gap between theory and practice, "Principles of Artificial Intelligence" describes fundamental AI ideas that underlie applications such as natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, and intelligent data retrieval. Rather than focusing on the subject matter of the applications, the book is organized around general computational concepts involving the kinds of data structures used, the types of operations performed on the data structures, and the properties of the control strategies used. "Principles of Artificial Intelligence"evolved from the author's courses and seminars at Stanford University and University of Massachusetts, Amherst, and is suitable for text use in a senior or graduate AI course, or for individual study.
3,754 citations
Additional excerpts
...While total-order HTN planning is strictly more expressive than planning with STRIPS-style operators, it is also strictly less expressive than unrestricted HTN planning....
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...Any problem represented in STRIPS language can be mapped to a total-order HTN planning problem....
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...We have also examined the expressive power of totalorder HTN planning, and concluded that while it is strictly more expressive than planning with STRIPS-style operators, it is strictly less expressive than unrestricted HTN planning....
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...Theoretical Considerations In STRIPS-style planning, total versus partial-order planning is an algorithmic issue; it does not affect the set of planning problems that can be represented or solved using STRIPS-style planning operators....
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...From the perspective of operational and model-theoretic expressivity, total-order HTN planning lies strictly between STRIPS-style planning and unrestricted HTN planning....
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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
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
"Control strategies in HTN planning:..." refers background in this paper
...Formal analyses of HTN planning have shown that it is strictly more expressive than planning with STRIPS-style operators (Erol et al. 1994b), and have established properties such as soundness and completeness (Erol et al. 1994a), complexity (Erol et al. 1996), and the efficiency of various control…...
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...…that it is strictly more expressive than planning with STRIPS-style operators (Erol et al. 1994b), and have established properties such as soundness and completeness (Erol et al. 1994a), complexity (Erol et al. 1996), and the efficiency of various control strategies (Tsuneto et al. 1996, 1997)....
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...This assertion can be proved using the definitions of complexity-based expressivity, model-theoretic expressivity, and operational expressivity developed by Erol et al. (1994b)....
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...As reported in Erol et al. (1994b), the complexity of totally-ordered HTN planning is EXPSPACE-hard, and in DOUBLE-EXPTIME....
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...Erol et al. (1994b) presented a sound and complete HTN planning system called UMCP....
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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
"Control strategies in HTN planning:..." refers background in this paper
...Although computer programs have done very well in games such as chess, checkers, and Othello (Schaeffer 1993; Korf 1994), human experts still outperform computers in the game of bridge....
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...Overview of HTN Planning The basic ideas used in HTN planning were originally developed more than 20 years ago (Sacerdoti 1977; Tate 1977)....
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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