Institute for Systems Research

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

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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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    Using the Parka Parallel Knowledge Representation System (Version 3.2)
    (1995) Kettler, Brian; Andersen, William; Hendler, James A.; Luke, Sean; ISR
    Parka is a symbolic, semantic network knowledge representation system that takes advantage of the massive parallelism of supercomputers such as the Connection Machine. The Parka language has many of the features of traditional semantic net/frame-based knowledge representation languages but also supports several kinds of rapid parallel inference mechanisms that scale to large knowledge-bases of hundreds of thousands of frames or more. Parka is intended for general-purpose use and has been used thus far to support, A.I. systems for case-based reasoning and data mining.

    This document is a user manual for the current version of Parka, version 3.2. It describes the Parka language and presents some examples of knowledge representation using Parka. Details about the parallel algorithms, implementation, and empirical results are presented elsewhere.

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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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    Complexity Results for HTN Planning
    (1994) 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 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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    Linking Symbolic and Subsymbolic Computing
    (1993) Wilson, A.; Hendler, James A.; ISR
    The growing interest in integrating symbolic and subsymbolic computing techniques is manifested by the increasing number of hybrid systems that employ both methods of processing. This paper presents an analysis of some of these systems with respect to their symbolic/subsymbolic interactions. Then, a general purpose mechanism for linking symbolic and sub symbolic computing is introduced. Through the use of programming abstractions, an intermediary agent called a supervisor is created and bound to each subsymbolic network. The role of a supervisor is to monitor and control the network behavior and interpret its output. Details of the subsymbolic computation are hidden behind a higher level interface, enabling symbolic and subsymbolic components to interact at corresponding conceptual levels. Module level parallelism is achieved because subsymbolic modules execute independently. Methods for construction of hierarchical systems of subsymbolic modules are also provided.