SHOP2: an HTN planning system
01 Dec 2003-Journal of Artificial Intelligence Research (AI ACCESS FOUNDATION)-Vol. 20, Iss: 1, pp 379-404
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.
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06 Jul 2004
TL;DR: This paper shows how to use OWL-S in conjunction with Web service standards, and explains and illustrates the value added by the semantics expressed in OWl-S.
Abstract: Service interface description languages such as WSDL, and related standards, are evolving rapidly to provide a foundation for interoperation between Web services. At the same time, Semantic Web service technologies, such as the Ontology Web Language for Services (OWL-S), are developing the means by which services can be given richer semantic specifications. Richer semantics can enable fuller, more flexible automation of service provision and use, and support the construction of more powerful tools and methodologies. Both sets of technologies can benefit from complementary uses and cross-fertilization of ideas. This paper shows how to use OWL-S in conjunction with Web service standards, and explains and illustrates the value added by the semantics expressed in OWL-S.
896 citations
TL;DR: A sound and complete algorithm is provided to translate OWL-S service descriptions to a SHOP2 domain and it is proved the correctness of the algorithm by showing the correspondence to the situation calculus semantics of OWl-S.
819 citations
TL;DR: This paper reexamine behavioral hierarchy and its neural substrates from the point of view of recent developments in computational reinforcement learning and considers a set of approaches known collectively as hierarchical reinforcement learning, which extend the reinforcement learning paradigm by allowing the learning agent to aggregate actions into reusable subroutines or skills.
568 citations
TL;DR: OWL-S can be used to automate a variety of service-related activities involving service discovery, interoperation, and composition, and has led to the creation of many open-source tools for developing, reasoning about, and dynamically utilizing Web Services.
Abstract: Current industry standards for describing Web Services focus on ensuring interoperability across diverse platforms, but do not provide a good foundation for automating the use of Web Services. Representational techniques being developed for the Semantic Web can be used to augment these standards. The resulting Web Service specifications enable the development of software programs that can interpret descriptions of unfamiliar Web Services and then employ those services to satisfy user goals. OWL-S ("OWL for Services") is a set of notations for expressing such specifications, based on the Semantic Web ontology language OWL. It consists of three interrelated parts: a profile ontology, used to describe what the service does; a process ontology and corresponding presentation syntax, used to describe how the service is used; and a grounding ontology, used to describe how to interact with the service. OWL-S can be used to automate a variety of service-related activities involving service discovery, interoperation, and composition. A large body of research on OWL-S has led to the creation of many open-source tools for developing, reasoning about, and dynamically utilizing Web Services.
546 citations
Proceedings Article•
01 Jan 2004TL;DR: Pellet has the usual suite of optimizations including lazy unfolding, absorption, dependency directed backjumping, and semantic branching, and incorporates datatype reasoning for the built-in primitive XML Schema datatypes.
Abstract: In order to gain experience with description logic reasoner, and to contribute to the OWL Candidate Recommendation process, a small team at MINDSWAP set out to implement a tableau reasoner for the Lite and DL dialects of OWL (corresponding roughly to the description logics SHIF(D) and SHION(D)). Our group found existing, available DL reasoners lacking for our purposes, because we needed an open-source tool that provides ABox reasoning, that does not make Unique Name assumption, supports entailment checks and works with XML Schema datatypes. Pellet has been developed to addresses these issues and has become both our test bed for experiments with DL and Semantic Web reasoning, as well as our standard reasoning component. While not (yet) in the performance range of Racer or Fact, it has many usability features that makes it a good choice for various lighter weight situations. Technically, Pellet is a sound and complete tableau reasoners for SHIN(D) and SHON(D) (with ABoxes), and a sound but incomplete tableau reasoner for SHION(D) (with ABoxes). Pellet has the usual suite of optimizations including lazy unfolding, absorption, dependency directed backjumping, and semantic branching. It incorporates datatype reasoning for the built-in primitive XML Schema datatypes. Pellet is implemented in pure Java and available as open source software.
518 citations
References
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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
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
TL;DR: This work shows how domain dependent search control knowledge can be represented in a temporal logic, and then utilized to effectively control a forward-chaining planner.
631 citations
Book•
15 Sep 1988TL;DR: In this paper, the authors present a hierarchical planning as a hierarchy of different abstraction levels for SIPE and compare it with other systems with different resources: Reusable, Consumable, Temporal, Search, and Reactivity.
Abstract: 1 Reasoning about Actions and Planning 2 Basic Assumptions and Limitations 3 SIPE and Its Representations 4 Hierarchical Planning as Differing Abstraction Levels 5 Constraints 6. The Truth Criterion 7 Deductive Causal Theories 8 Plan Critics 9 Resources: Reusable, Consumable, Temporal 10 Search 11 Replanning During Execution 12 Planning and Reactivity 13 Achieving Heuristic Adequacy 14 Comparison with Other Systems
551 citations
Proceedings Article•
03 Sep 1975TL;DR: A new information structure is described, called the procedural net, that represents a plan as a partial ordering of actions with respec to time, so that a problem-solving system using this representation can deal easily and directly with problems that are otherwise very difficult to solve.
Abstract: We usually think of plans as linear sequences of actions. This is because plans are usually executed one step at a time. But plans themselves are not constrained by limitations of linearity. This paper describes a new information structure, called the procedural net, that represents a plan as a partial ordering of actions with respec to time. By avoiding premature commitments to a particular order for achieving subgoals, a problem-solving system using this representation can deal easily and directly with problems that are otherwise very difficult to solve.
532 citations