Browsing by Author "Hendler, James"
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Item The Case for Structure-based Representations(1998-10-15) Sanders, Kathryn E.; Kettler, Brian P.; Hendler, JamesCase-based reasoning involves reasoning from {\em cases}: specific pieces of experience, the reasoner's or another's, that can be used to solve problems. As a result, case representation is critical: an incomplete case representation limits the system's reasoning power. In this paper we argue for {\em structure-based} case representations, which express arbitrary relations among objects in a flexible way, over more limited or inflexible methods. We motivate the distinction between these kinds of representations with examples from information retrieval systems, CBR systems, and computational models of human analogical reasoning. Structure-based representations provide the benefits of greater expressivity and economy. We give examples of these benefits from two case-based planning systems we have developed, CaPER and CHIRON, and show how the case matching and case acquisition costs can be reduced through the use of massively parallel techniques. (Also cross-referenced as UMIACS-TR-95-56)Item The Challenges of Real-Time AI(1998-10-15) Musliner, David John; Hendler, James; Agrawala, Ashok K.; Durfee, Edmund H.; Strosnider, Jay K.; Paul, C. J.The research agendas of two major areas of computer science are converging: Artificial Intelligence (AI) methods are moving towards more realistic domains requiring real-time responses, and real-time systems are moving towards more complex applications requiring intelligent behavior. Together, they meet at the crossroads of interest in "real-time intelligent control," or "real-time AI." This subfield is still being defined by the common interests of researchers from both real-time and AI systems. As a result, the precise goals for various real-time AI systems are still in flux. This paper describes an organizing conceptual structure for current real-time AI research, clarifying the different meanings this term has acquired for various researchers. Having identified the various goals of real-time AI research, we then specify some of the necessary steps towards reaching those goals. This in turn enables us to identify promising areas for future research in both AI and real-time systems techniques. (Also cross-referenced as UMIACS-TR-94-69)Item Complexity Results for HTN Planning(1998-10-15) Erol, Kutluhan; Hendler, James; Nau, Dana S.(Also cross-referenced as ISR-TR-95-10) 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. (Also cross-referenced as UMIACS-TR-94-32)Item Semantics for HTN Planning(1998-10-15) Erol, Kutluhan; Hendler, James; Nau, Dana S.(Also cross-referenced as ISR-TR-95-9) 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. (Also cross-referenced as UMIACS-TR-94-31)Item SHOE: A Knowledge Representation Language for Internet Applications(1999-10-28) Heflin, Jeff; Hendler, James; Luke, SeanIt is our contention that the World Wide Web poses challenges to knowledge representation systems that fundamentally change the way we should design KR languages. In this paper, we describe the Simple HTML Ontology Extensions (SHOE), a KR language which allows web pages to be annotated with semantics. We present a formalism for the language and discuss the features which make it well suited for the Web. We describe the syntax and semantics of this language, and discuss the differences from traditional KR systems that make it more suited to modern web applications. We also describe some generic tools for using the language and demonstrate its capabilities by describing two prototype systems that use it. We also discuss some future tools currently being developed for the language. The language, tools, and details of the applications are all available on the World Wide Web at http://www.cs.umd.edu/projects/plus/SHOE. (Also cross-referenced as UMIACS-TR-99-71)Item UM Translog: A Planning Domain for the Development and Benchmarking of Planning Systems(1998-10-15) Andrews, Scott; Kettler, Brian; Erol, Kutluhan; Hendler, JamesThe last twenty years of AI planning research has discovered a wide variety of planning techniques such as state-space search, hierarchical planning, case-based planning and reactive planning. These techniques have been implemented in numerous planning systems (e.g., STRIPS, SNLP, UCPOP, NONLIN, SIPE). Initially, a number of simple toy domains have been devised to assist in the analysis and evaluation of planning systems and techniques. The most well known examples are ``Blocks World'' and ``Towers of Hanoi.'' As planning systems grow in sophistication and capabilities, however, there is a clear need for planning benchmarks with matching complexity to evaluate those new features and capabilities. UM Translog is a planning domain designed specifically for this purpose. UM Translog was inspired by the CMU Transport Logistics domain developed by Manuela Veloso. UM Translog is an order of magnitude larger in size (41 actions versus 6), number of features and types interactions. It provides a rich set of entities, attributes, actions and conditions, which can be used to specify rather complex planning problems with a variety of plan interactions. The detailed set of operators provides long plans (~40 steps) with many possible solutions to the same problem, and thus this domain can also be used to evaluate the solution quality of planning systems. The UM Translog domain has been used with the UMCP, UM Nonlin, and CaPER planning systems thus far. (Also cross-referenced as UMIACS-TR-95-69)Item Using the Parka Parallel Knowledge Representation System (Version 3.2)(1998-10-15) Kettler, Brian; Andersen, William; Hendler, James; Luke, SeanParka 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. (Also cross-referenced as UMIACS-TR-95-68)