A translation approach to portable ontology specifications

Recent work in Artificial Intelligence is exploring the use of formal ontologies as a way of specifying content-specific agreements for the sharing and reuse of knowledge among software entities. We take an engineering perspective on the development of such ontologies. Formal ontologies are viewed as designed artifacts, formulated for specific purposes and evaluated against objective design criteria. We describe the role of ontologies in supporting knowledge sharing activities, and then present a set of criteria to guide the development of ontologies for these purposes. We show how these criteria are applied in case studies from the design of ontologies for engineering mathematics and bibliographic data. Selected design decisions are discussed, and alternative representation choices and evaluated against the design criteria.

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Toward principles for the design of ontologies used for knowledge sharing

Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

Planning Algorithms: Introductory Material

This paper gives an overview of the development of the field of Knowledge Engineering over the last 15 years. We discuss the paradigm shift from a transfer view to a modeling view and describe two approaches which considerably shaped research in Knowledge Engineering: Role-limiting Methods and Generic Tasks. To illustrate various concepts and methods which evolved in recent years we describe three modeling frameworks: CommonKADS, MIKE and PROTEGE-II. This description is supplemented by discussing some important methodological developments in more detail: specification languages for knowledge-based systems, problem-solving methods and ontologies. We conclude by outlining the relationship of Knowledge Engineering to Software Engineering, Information Integration and Knowledge Management.

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Knowledge engineering: principles and methods

There is disagreement in the literature about the exact nature of the phenomenon of empathy. There are emotional, cogni- tive, and conditioning views, applying in varying degrees across species. An adequate description of the ultimate and proximate mecha- nism can integrate these views. Proximately, the perception of an object's state activates the subject's corresponding representations, which in turn activate somatic and autonomic responses. This mechanism supports basic behaviors (e.g., alarm, social facilitation, vicar- iousness of emotions, mother-infant responsiveness, and the modeling of competitors and predators) that are crucial for the reproduc- tive success of animals living in groups. The Perception-Action Model (PAM), together with an understanding of how representations change with experience, can explain the major empirical effects in the literature (similarity, familiarity, past experience, explicit teach- ing, and salience). It can also predict a variety of empathy disorders. The interaction between the PAM and prefrontal functioning can also explain different levels of empathy across species and age groups. This view can advance our evolutionary understanding of empa- thy beyond inclusive fitness and reciprocal altruism and can explain different levels of empathy across individuals, species, stages of de- velopment, and situations.

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Empathy: Its ultimate and proximate bases.

We describe and evaluate the algorithmic techniques that are used in the FF planning system. Like the HSP system, FF relies on forward state space search, using a heuristic that estimates goal distances by ignoring delete lists. Unlike HSP's heuristic, our method does not assume facts to be independent. We introduce a novel search strategy that combines hill-climbing with systematic search, and we show how other powerful heuristic information can be extracted and used to prune the search space. FF was the most successful automatic planner at the recent AIPS-2000 planning competition. We review the results of the competition, give data for other benchmark domains, and investigate the reasons for the runtime performance of FF compared to HSP.

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The FF planning system: fast plan generation through heuristic search

Preface Chapter 1 Robotic Mapping: A Survey Sebastian Thrun Chapter 2 D-Learning: What Learning in Dogs Tells Us About Building Characters That Learn What They Ought to Learn Bruce Blumberg Chapter 3 Identifying Semantic Relations in Text Daniel Gildea and Daniel Jurafsky Chapter 4 Planning with Generic Types Derek Long and Maria Fox Chapter 5 Bayesian Inference of Visual Motion Boundaries David J Fleet, Michael J Black, and Oscar Nestares, CSIC Chapter 6 Qualitative Spatio-Temporal Representation and Reasoning: A Computational Perspective Frank Wolter and Michael Zakharyaschev Chapter 7 Extending Virtual Humans to Support Team Training in Virtual Reality Jeff Rickel and W Lewis Johnson Chapter 8 Understanding Belief Propagation and Its Generalizations Jonathan Yedidia, William T Freeman, and Yair Weiss Chapter 9 Learning Theory and Language Modeling David McAllester and Robert E Schapire Chapter 10 A First-Order-Logic Davis-Putnam-Logemann-Loveland Procedure Peter Baumgartner Chapter 11 New Tractable Constraint Classes from Old David Cohen, Peter Jeavons, and Richard Gault Chapter 12 User-Oriented Evaluation Methods for Information Retrieval: A Case Study Based on Conceptual Models for Query Expansion Jaana Kekalainen and Kalervo Jarvelin Chapter 13 Data Mining for Manufacturing Control: An Application in Optimizing IC Test Tony Fountain, Thomas Dietterich

Exploring artificial intelligence in the new millennium

We have previously reported a number of tractable planning problems defined in the SAS + formalism. This article complements these results by providing a complete map over the complexity of SAS + planning under all combinations of the previously considered restrictions. We analyze the complexity of both finding a minimal plan and finding any plan. In contrast to other complexity surveys of planning, we study not only the complexity of the decision problems but also the complexity of the generation problems. We prove that the SAS + -PUS problem is the maximal tractable problem under the restrictions we have considered if we want to generate minimal plans. If we are satisfied with any plan, then we can generalize further to the SAS + -US problem, which we prove to be the maximal tractable problem in this case.

Complexity results for sas+ planning

Representation and management of knowledge.- A hybrid representation formalism.- Reasoning in the formalism.- Terminological cycles.- Belief revision.- The revision in terminological systems.- Terminological reason maintenance.- Summary and outlook.- The universal term-forming formalism U.- Overview of formalism and systems.

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Reasoning and Revision in Hybrid Representation Systems

We introduce a new subclass of Allen's interval algebra we call “ORD-Horn subclass,” which is a strict superset of the “pointisable subclass.” We prove that reasoning in the ORD-Horn subclass is a polynomial-time problem and show that the path-consistency method is sufficient for deciding satisfiability. Further, using an extensive machine-generated case analysis, we show that the ORD-Horn subclass is a maximal tractable subclass of the full algebra (assuming P ≠ NP). In fact, it is the unique greatest tractable subclass amongst the subclasses that contain all basic relations.

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Bernhard Nebel

Papers

The FF planning system: fast plan generation through heuristic search

Exploring artificial intelligence in the new millennium

Complexity results for sas+ planning

Reasoning and Revision in Hybrid Representation Systems

Reasoning about temporal relations: a maximal tractable subclass of Allen's interval algebra