Institute for Systems Research

Permanent URI for this communityhttp://hdl.handle.net/1903/4375

Browse

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

Search Results

Now showing 1 - 6 of 6
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    Complexity, Decidability and Undecidability Results for Rule- Based Expert Systems
    (1992) Blanksteen, Scott; Hendler, James A.; Nau, Dana S.; ISR
    We prove the equivalence of domain-independent planning systems and rule-based expert systems. We use this equivalence to examine how the complexity of deriving conclusions in rule-based expert systems depends on the nature of the rules. We show conditions under which conclusion derivation is decidable and undecidable. For those cases where the problem is decidable, we show how the time complexity varies depending on a wide variety of conditions: whether or not function symbols are allowed; whether or not rules may retract facts; whether or not negative conditions are allowed; whether or not the rules are allowed to take arguments; and whether the rules are given as part of the input to the expert system, or instead are fixed in advance.
  • Thumbnail Image
    Item
    Knowledge Representation in PARKA - Part 2: Experiments, Analysis, and Enhancements
    (1992) Spector, Lee; Andersen, William; Hendler, James A.; Kettler, Brian; Schwartzman, Eugene; Woods, Cynthia; Evett, Matthew; ISR
    Our research group has designed and implemented a symbolic knowledge representation system called PARKA which runs on the Connection Machine, a massively parallel SIMD computer [9]. The semantics of this system are discussed in [11]. The details of the Connection Machine implementation and discussions of performance considerations can be found in [3], [4], [5], [6] and [7]. In the past year the PARKA project has made significant advances along several fronts of both theoretical and practical significance. This paper summarizes some of this work and outlines directions for further research.
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    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.