Proceedings Article•
UMCP: a sound and complete procedure for hierarchical task-network planning
13 Jun 1994-pp 249-254
TL;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
Citations
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01 Oct 2002
TL;DR: The primary contribution of the paper is to show empirically that distributed negotiation mechanisms such as MURDOCH are viable and effective for coordinating physical multirobot systems.
Abstract: The key to utilizing the potential of multirobot systems is cooperation. How can we achieve cooperation in systems composed of failure-prone autonomous robots operating in noisy, dynamic environments? We present a method of dynamic task allocation for groups of such robots. We implemented and tested an auction-based task allocation system which we call MURDOCH, built upon a principled, resource centric, publish/subscribe communication model. A variant of the Contract Net Protocol, MURDOCH produces a distributed approximation to a global optimum of resource usage. We validated MURDOCH in two very different domains: a tightly coupled multirobot physical manipulation task and a loosely coupled multirobot experiment in long-term autonomy. The primary contribution of the paper is to show empirically that distributed negotiation mechanisms such as MURDOCH are viable and effective for coordinating physical multirobot systems.
1,067 citations
Cites methods from "UMCP: a sound and complete procedur..."
...Alternatively, an offline planner, such as that described in [ 29 ], endowed with knowledge of the preconditions, postconditions, and interdependences of the tasks involved could be employed....
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IBM1
TL;DR: This work discusses what makes the Web service composition so special and derive challenges for the AI planning community and compares these approaches to the problems of modeling, composing, executing, and verifying Web services.
Abstract: Composition of Web services has received much interest to support business-to-business or enterprise application integration. On the one side, the business world has developed a number of XML-based standards to formalize the specification of Web services, their flow composition and execution. This approach is primarily syntactical: Web service interfaces are like remote procedure call and the interaction protocols are manually written. On the other side, the Semantic Web community focuses on reasoning about web resources by explicitly declaring their preconditions and effects with terms precisely defined in ontologies. For the composition of Web services, they draw on the goal-oriented inferencing from planning. So far, both approaches have been developed rather independently from each other. We compare these approaches and discuss their solutions to the problems of modeling, composing, executing, and verifying Web services. We discuss what makes the Web service composition so special and derive challenges for the AI planning community.
585 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: It is argued that delegation requires a shared hierarchical task model between supervisor and subordinates, used to delegate tasks at various levels, and offer instruction on performing them, and an architecture for machine-based delegation systems based on the metaphor of a sports team's “playbook” is developed.
Abstract: OBJECTIVE: To develop a method enabling human-like, flexible supervisory control via delegation to automation. BACKGROUND: Real-time supervisory relationships with automation are rarely as flexible as human task delegation to other humans. Flexibility in human-adaptable automation can provide important benefits, including improved situation awareness, more accurate automation usage, more balanced mental workload, increased user acceptance, and improved overall performance. METHOD: We review problems with static and adaptive (as opposed to "adaptable") automation; contrast these approaches with human-human task delegation, which can mitigate many of the problems; and revise the concept of a "level of automation" as a pattern of task-based roles and authorizations. We argue that delegation requires a shared hierarchical task model between supervisor and subordinates, used to delegate tasks at various levels, and offer instruction on performing them. A prototype implementation called Playbook is described. RESULTS: On the basis of these analyses, we propose methods for supporting human-machine delegation interactions that parallel human-human delegation in important respects. We develop an architecture for machine-based delegation systems based on the metaphor of a sports team's "playbook." Finally, we describe a prototype implementation of this architecture, with an accompanying user interface and usage scenario, for mission planning for uninhabited air vehicles. CONCLUSION: Delegation offers a viable method for flexible, multilevel human-automation interaction to enhance system performance while maintaining user workload at a manageable level. APPLICATION: Most applications of adaptive automation (aviation, air traffic control, robotics, process control, etc.) are potential avenues for the adaptable, delegation approach we advocate. We present an extended example for uninhabited air vehicle mission planning. Language: en
407 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 "UMCP: a sound and complete procedur..."
...Theoretical models for plan-space HTN planning began to be developed in the early 1990s [615, 309], culminating in a formal semantics [180], a provably correct planning algorithm [181], and analysis showing that HTN planning has greater expressive power than classical planning [179]....
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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
Book•
31 Oct 1995
TL;DR: In this article, the authors describe a problem solver called STRIPS that attempts to find a sequence of operators in a spcce of world models to transform a given initial world model into a model in which a given goal formula can be proven to be true.
Abstract: We describe a new problem solver called STRIPS that attempts to find a sequence of operators in a spcce of world models to transform a given initial world model into a model in which a given goal formula can be proven to be true. STRIPS represents a world n,~del as an arbitrary collection of first-order predicate calculus formulas and is designed to work with .models consisting of large numbers of formulas. It employs a resolution theorem prover to answer questions of particular models and uses means-ends analysis to guide it to the desired goal-satisfying model.
1,793 citations
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: Theorems that suggest that efficient general purpose planning with more expressive action representations is impossible are presented, and ways to avoid this problem are suggested.
1,162 citations
"UMCP: a sound and complete procedur..." refers methods or result in this paper
...In contrast, although the past few years have seen much analysis of planning using strips-style operators, (Chapman 1987; McAllester et al. 1991; Erol et al. 1992b; Erol et al. 1992a), there has been very little analytical work on htn planners....
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...In contrast to the abundance of well understood strips-style planning algorithms (such as (Fikes et al. 1971; Chapman 1987; Barett et al. 1992; Kambhampati 1992)), htn planning algorithms have typically not been proven to be sound or complete....
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...In htn planning, the world and the basic actions that can be performed are represented in a manner similar to the representations used in strips (Fikes et al. 1971; Chapman 1987)....
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Proceedings Article•
25 Oct 1992
TL;DR: It is proved ucpop is both sound and complete for this representation and a practical implementation that succeeds on all of Pednault's and McDermott's examples, including the infamous "Yale Stacking Problem".
Abstract: We describe the ucpop partial order planning algorithm which handles a subset of Pednault's ADL action representation. In particular, ucpop operates with actions that have conditional e ects, universally quanti ed preconditions and e ects, and with universally quanti ed goals. We prove ucpop is both sound and complete for this representation and describe a practical implementation that succeeds on all of Pednault's and McDermott's examples, including the infamous \Yale Stacking Problem" [McDermott 1991].
819 citations