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123
An Efficient Unification Algorithm
 TRANSACTIONS ON PROGRAMMING LANGUAGES AND SYSTEMS (TOPLAS)
, 1982
"... The unification problem in firstorder predicate calculus is described in general terms as the solution of a system of equations, and a nondeterministic algorithm is given. A new unification algorithm, characterized by having the acyclicity test efficiently embedded into it, is derived from the nond ..."
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Cited by 333 (1 self)
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The unification problem in firstorder predicate calculus is described in general terms as the solution of a system of equations, and a nondeterministic algorithm is given. A new unification algorithm, characterized by having the acyclicity test efficiently embedded into it, is derived from the nondeterministic one, and a PASCAL implementation is given. A comparison with other wellknown unification algorithms shows that the algorithm described here performs well in all cases.
Bayesian Networks Without Tears
 AI MAGAZINE
, 1991
"... I give an introduction to Bayesian networks for AI researchers with a limited grounding in probability theory. Over the last few years, this method of reasoning using probabilities has become popular within the AI probability and uncertainty community. Indeed, it is probably fair to say that Bayesia ..."
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Cited by 235 (2 self)
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I give an introduction to Bayesian networks for AI researchers with a limited grounding in probability theory. Over the last few years, this method of reasoning using probabilities has become popular within the AI probability and uncertainty community. Indeed, it is probably fair to say that Bayesian networks are to a large segment of the AIuncertainty community what resolution theorem proving is to the AIlogic community. Nevertheless, despite what seems to be their obvious importance, the ideas and techniques have not spread much beyond the research community responsible for them. This is probably because the ideas and techniques are not that easy to understand. I hope to rectify this situation by making Bayesian networks more accessible to the probabilistically unsophisticated.
A model for types and levels of human interaction with automation
 IEEE Transactions on Systems Man and Cybernetics – Part A: Systems and Humans
"... Abstract—Technical developments in computer hardware and software now make it possible to introduce automation into virtually all aspects of humanmachine systems. Given these technical capabilities, which system functions should be automated and to what extent? We outline a model for types and leve ..."
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Cited by 176 (12 self)
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Abstract—Technical developments in computer hardware and software now make it possible to introduce automation into virtually all aspects of humanmachine systems. Given these technical capabilities, which system functions should be automated and to what extent? We outline a model for types and levels of automation that provides a framework and an objective basis for making such choices. Appropriate selection is important because automation does not merely supplant but changes human activity and can impose new coordination demands on the human operator. We propose that automation can be applied to four broad classes of functions: 1) information acquisition; 2) information analysis; 3) decision and action selection; and 4) action implementation. Within each of these types, automation can be applied across a continuum of levels from low to high, i.e., from fully manual to fully automatic. A particular system can involve automation of all four types at different levels. The human performance consequences of particular types and levels of automation constitute primary evaluative criteria for automation design using our model. Secondary evaluative criteria include automation reliability and the costs of decision/action consequences, among others. Examples of recommended types and levels of automation are provided to illustrate the application of the model to automation design. Index Terms—Automation, cognitive engineering, function allocation, humancomputer interaction, human factors, humanmachine systems, interface design. I.
Decision Theory in Expert Systems and Artificial Intelligence
 International Journal of Approximate Reasoning
, 1988
"... Despite their different perspectives, artificial intelligence (AI) and the disciplines of decision science have common roots and strive for similar goals. This paper surveys the potential for addressing problems in representation, inference, knowledge engineering, and explanation within the decision ..."
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Cited by 93 (18 self)
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Despite their different perspectives, artificial intelligence (AI) and the disciplines of decision science have common roots and strive for similar goals. This paper surveys the potential for addressing problems in representation, inference, knowledge engineering, and explanation within the decisiontheoretic framework. Recent analyses of the restrictions of several traditional AI reasoning techniques, coupled with the development of more tractable and expressive decisiontheoretic representation and inference strategies, have stimulated renewed interest in decision theory and decision analysis. We describe early experience with simple probabilistic schemes for automated reasoning, review the dominant expertsystem paradigm, and survey some recent research at the crossroads of AI and decision science. In particular, we present the belief network and influence diagram representations. Finally, we discuss issues that have not been studied in detail within the expertsystems sett...
Toward normative expert systems: Part I. The pathfinder project
 Methods Inf. Med
, 1992
"... Pathfinder is an expert system that assists surgical pathologists with the diagnosis of lymphnode diseases. The program is one of a growing number of normative expert systems that use probability and decision theory to acquire, represent, manipulate, and explain uncertain medical knowledge. In this ..."
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Cited by 81 (15 self)
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Pathfinder is an expert system that assists surgical pathologists with the diagnosis of lymphnode diseases. The program is one of a growing number of normative expert systems that use probability and decision theory to acquire, represent, manipulate, and explain uncertain medical knowledge. In this article, we describe Pathfinder and our research in uncertainreasoning paradigms that was stimulated by the development of the program. We discuss limitations with early decisiontheoretic methods for reasoning under uncertainty and our initial attempts to use nondecisiontheoretic methods. Then, we describe experimental and theoretical results that directed us to return to reasoning methods based in probability and decision theory.
Reconstructing Proofs at the Assertion Level
, 1994
"... Most automated theorem provers suffer from the problem that they can produce proofs only in formalisms difficult to understand even for experienced mathematicians. Effort has been made to reconstruct natural deduction (ND) proofs from such machine generated proofs. Although the single steps in ND pr ..."
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Cited by 63 (9 self)
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Most automated theorem provers suffer from the problem that they can produce proofs only in formalisms difficult to understand even for experienced mathematicians. Effort has been made to reconstruct natural deduction (ND) proofs from such machine generated proofs. Although the single steps in ND proofs are easy to understand, the entire proof is usually at a low level of abstraction, containing too many tedious steps. To obtain proofs similar to those found in mathematical textbooks, we propose a new formalism, called ND style proofs at the assertion level , where derivations are mostly justified by the application of a definition or a theorem. After characterizing the structure of compound ND proof segments allowing assertion level justification, we show that the same derivations can be achieved by domainspecific inference rules as well. Furthermore, these rules can be represented compactly in a tre structure. Finally, we describe a system called PROVERB , which substantially sh...
Multivalued Logics: A Uniform Approach to Inference in Artificial Intelligence
 Computational Intelligence
, 1988
"... This paper describes a uniform formalization of much of the current work in AI on inference systems. We show that many of these systems, including firstorder theorem provers, assumptionbased truth maintenance systems (atms's) and unimplemented formal systems such as default logic or circumscriptio ..."
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Cited by 56 (0 self)
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This paper describes a uniform formalization of much of the current work in AI on inference systems. We show that many of these systems, including firstorder theorem provers, assumptionbased truth maintenance systems (atms's) and unimplemented formal systems such as default logic or circumscription can be subsumed under a single general framework. We begin by defining this framework, which is based on a mathematical structure known as a bilattice. We present a formal definition of inference using this structure, and show that this definition generalizes work involving atms's and some simple nonmonotonic logics. Following the theoretical description, we describe a constructive approach to inference in this setting; the resulting generalization of both conventional inference and atms's is achieved without incurring any substantial computational overhead. We show that our approach can also be used to implement a default reasoner, and discuss a combination of default and atms methods th...
DEBUGGING AND REPAIR OF OWL ONTOLOGIES
, 2006
"... With the advent of Semantic Web languages such as OWL (Web Ontology Language), the expressive Description Logic SHOIN is exposed to a wider audience of ontology users and developers. As an increasingly large number of OWL ontologies become available on the Semantic Web and the descriptions in the on ..."
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Cited by 39 (0 self)
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With the advent of Semantic Web languages such as OWL (Web Ontology Language), the expressive Description Logic SHOIN is exposed to a wider audience of ontology users and developers. As an increasingly large number of OWL ontologies become available on the Semantic Web and the descriptions in the ontologies become more complicated, finding the cause of errors becomes an extremely hard task even for experts. The problem is worse for newcomers to OWL who have little or no experience with DLbased knowledge representation. Existing ontology development environments, in conjunction with a reasoner, provide some limited debugging support, however this is restricted to merely reporting errors in the ontology, whereas bug diagnosis and resolution is usually left to the user. In this thesis, I present a complete endtoend framework for explaining, pinpointing and repairing semantic defects in OWLDL ontologies (or in other words, a SHOIN knowledge base). Semantic defects are logical contradictions that manifest as either inconsistent ontologies or unsatisfiable concepts. Where possible, I show extensions to handle related defects such as unsatisfiable roles, unintended entailments and nonentailments,
Generic User Modeling Systems
, 2001
"... The paper reviews the development of generic user modeling systems over the past twenty years. It describes their purposes, their services within useradaptive systems, and the different design requirements for research prototypes and commercially deployed servers. It discusses the architectures tha ..."
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Cited by 38 (0 self)
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The paper reviews the development of generic user modeling systems over the past twenty years. It describes their purposes, their services within useradaptive systems, and the different design requirements for research prototypes and commercially deployed servers. It discusses the architectures that have been explored so far, namely shell systems that form part ofthe application, central server systems that communicate with several applications, and possible future user modeling agents that physically follow the user. Several implemented research prototypes and commercial systems are briefly described.
Converting a rulebased expert system into a belief network
 Medical Informatics
, 1993
"... The theory of belief networks offers a relatively new approach for dealing with uncertain information in knowledgebased (expert) systems. In contrast with the heuristic techniques for reasoning with uncertainty employed in many rulebased expert systems, the theory of belief networks is mathematica ..."
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Cited by 37 (6 self)
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The theory of belief networks offers a relatively new approach for dealing with uncertain information in knowledgebased (expert) systems. In contrast with the heuristic techniques for reasoning with uncertainty employed in many rulebased expert systems, the theory of belief networks is mathematically sound, based on techniques from probability theory. It therefore seems attractive to convert existing rulebased expert systems into belief networks. In this article, we discuss the design of a belief network reformulation of the diagnostic rulebased expert system HEPAR. For the purpose of this experiment, we have studied several typical pieces of medical knowledge represented in the HEPAR system. It turned out that, due to the differences in the type of knowledge represented and in the formalism used to represent uncertainty, much of the medical knowledge required for building the belief network concerned could not be extracted from HEPAR. As a consequence, significant additional knowledge acquisition was required. However, the objects and attributes defined in the HEPAR system, as well as the conditions in production rules mentioning these objects and attributes were useful for guiding the selection of the statistical variables for building the belief network. The mapping of objects and attributes in HEPAR to statistical variables is discussed in detail.