Institute for Systems Research

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Author: search.filters.author.Hendler, James A.Subject: search.filters.subject.algorithms

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    Advances in High Performance Knowledge Representation
    (1996) Stoffel, K.; Taylor, M.; Hendler, James A.; Saltz, J.; Andersen, William; ISR
    Real world applications are demanding that KR systems provide support for knowledge bases containing millions of assertions. We present Parka-DB, a high-performance reimplementation of the Parka KR language which uses a standard relational DBMS. The integration of a DBMS and the Parka KR language allows us to efficiently support complex queries on extremely large KBs using a single processor, as opposed to our earlier massively parallel system. In addition, the system can make good use of secondary memory, with the whole system needing less than 16MB of RAM to hold a KB of over 2,000,000 assertions. We demonstrate empirically that this reduction in primary storage requires only about 10% overhead in time, and decreases the load time of very large KBs by more than two orders of magnitude.
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    A Critical Look at Critics in HTN Planning
    (1995) Erol, Kutluhan; Hendler, James A.; Nau, D.S.; Tsuneto, R.; ISR
    Detecting interactions and resolving conflicts in one of the key issues for generative planning systems. Hierarchical Task Network (HTN) planning systems use critics for this purpose. Critics have provided extra efficiency and flexibility to HTN planning systems, but their procedural -- and sometimes domain- specific - - nature has not been amenable to analytical studies. As a result, little work is available on the correctness or efficiency of critics. This paper describes a principled approach to handling conflicts, as implemented in UMCP, an HTN planning system. Critics in UMCP have desirable properties such as systematicity, and the preservation of soundness and completeness.
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    Complexity Results for HTN Planning
    (1995) Erol, Kutluhan; Hendler, James A.; Nau, D.S.; ISR
    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, and how it compares to STRIPS- style planning.

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    Semantics for Hierarchical Task-Network Planning
    (1995) Erol, Kutluhan; Hendler, James A.; Nau, Dana S.; ISR
    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. We also develop several definitions of expressivity for planning languages and prove that HTN planning is strictly more expressive than STRIPS- style planning according to those definitions.

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    PRA: Massively Parallel Heuristic Search
    (1991) Evett, Matthew; Hendler, James A.; Mahanti, Ambuj; Nau, D.; ISR
    In this paper we describe a variant of A* search designed to run on the massively parallel, SIMD Connection Machine. The algorithm is designed to run in a limited memory by use of a retraction technique which allows nodes with poor heuristic values to be removed from the open list, until such time as they may need reexpansion, more promising paths having failed. Our algorithm, called PRA* (for Parallel Retraction A*), is designed to maximize use of the Connection Machine's memory and processors. In addition, the algorithm is guaranteed to return an optimal path when an admissable heuristic is used. Results comparing PRA* to Korf's IDA* for the fifteen-puzzle show significantly fewer node expansions for PRA*. In addition, empirical results show significant parallel speedups, indicative of the algorithm's design for high processor utilization.
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    Computer Similarity in a Reuse Library System: An AI-based Approach
    (1991) Ostertag, Eduardo J.; Hendler, James A.; Prieto-Diaz, Ruben; Braun, Christine; ISR
    This paper presents an AI-based library system for software reuse, called AIRS, that allows a developer to browse a software library in search of components that best meet some stated requirement. A component is described by a set of (feature,term) pairs. A feature represents a classification criterion, and is defined by a set of related terms. AIRS also allows for the representation of packages, that is, logical units that group a set of related components. As with components, packages are described in terms of features. Unlike components, a package description includes a set of member components. Candidate reuse components (and packages) are selected from the library based on the degree of similarity between their descriptions and a given target description. Similarity is quantified by a non-negative magnitude (called distance) that represents the expected effort required to obtain the target given a candidate. Distances are computed by functions called comparators. Three such functions are presented: the subsumption, the closeness, and the package comparators. We present a formalization of the concepts on which the AIRS classification approach is based. The functionality of a prototype implementation of the AIRS system is illustrated by application to two different software libraries: a set of Ada packages for data structure manipulation, and a set of C components for use in Command, Control, and Information Systems. Finally, we discuss some of the ideas we are currently exploring to automate the construction of AIRS classification libraries.