This paper presents a simple framework for Horn-clause abduction, with probabilities associated with hypotheses. The framework incorporates assumptions about the rule base and independence assumptions amongst hypotheses. It is shown how any probabilistic knowledge representable in a discrete Bayesian belief network can be represented in this framework. The main contribution is in finding a relationship between logical and probabilistic notions of evidential reasoning. This provides a useful representation language in its own right, providing a compromise between heuristic and epistemic adequacy. It also shows how Bayesian networks can be extended beyond a propositional language. This paper also shows how a language with only (unconditionally) independent hypotheses can represent any probabilistic knowledge, and argues that it is better to invent new hypotheses to explain dependence rather than having to worry about dependence in the language. Scholar, Canadian Institute for Advanced...

Renewed motives for space exploration have inspired NASA to work toward the goal of establishing a virtual presence in space, through heterogeneous effets of robotic explorers. Information technology, and Artificial Intelligence in particular, will play a central role in this endeavor by endowing these explorers with a form of computational intelligence that we call remote agents. In this paper we describe the Remote Agent, a specific autonomous agent architecture based on the principles of model-based programming, on-board deduction and search, and goal-directed closed-loop commanding, that takes a significant step toward enabling this future. This architecture addresses the unique characteristics of the spacecraft domain that require highly reliable autonomous operations over long periods of time with tight deadlines, resource constraints, and concurrent activity among tightly coupled subsystems. The Remote Agent integrates constraint-based temporal planning and scheduling, robust multi-threaded execution, and model-based mode identification and reconfiguration. The demonstration of the integrated system as an on-board controller for Deep Space One, NASA's rst New Millennium mission, is scheduled for a period of a week in late 1998. The development of the Remote Agent also provided the opportunity to reassess some of AI's conventional wisdom about the challenges of implementing embedded systems, tractable reasoning, and knowledge representation. We discuss these issues, and our often contrary experiences, throughout the paper.

Abduction is an important inference process underlying much of human intelligent activities, including text understanding, plan recognition, disease diagnosis, and physical device diagnosis. In this paper, we describe some problems encountered using abduction to understand text, and present some solutions to overcome these problems. The solutions we propose center around the use of a different criterion, called explanatory coherence, as the primary measure to evaluate the quality of an explanation. In addition, explanatory coherence plays an important role in the construction of explanations, both in determining the appropriate level of specificity of a preferred explanation, and in guiding the heuristic search to efficiently compute explanations of sufficiently high quality.

In this paper we propose a semantics for dyadic deontic logic with an explicit preference ordering between worlds, representing different degrees of ideality. We argue that this ideality ordering can be used in two ways to evaluate formulas, which we call ordering and minimizing. Ordering uses all preference relations between relevant worlds, whereas minimizing uses the most preferred worlds only. We show that ordering corresponds to strengthening of the antecedent, and minimizing to weakening of the consequent. Moreover, we show that in some cases ordering and minimizing have to be combined to obtain certain desirable conclusions, and that this can only be done in a so-called twophase deontic logic. In the first phase, the preference ordering is constructed, and in the second phase the ordering is used for minimization. If these two phases are not distinguished, then counterintuitive conclusions follow. 1 Introduction Preference-based deontic logics are deontic logics of which the se...

Several artificial intelligence architectures and systems based on "deep" models of a domain have been proposed, in particular for the diagnostic task. These systems have several advantages over traditional knowledge based systems, but they have a main limitation in their computational complexity. One of the ways to face this problem is to rely on a knowledge compilation phase, which produces knowledge that can be used more effectively with respect to the original one. In this paper we show how a specific knowledge compilation approach can focus reasoning in abductive diagnosis, and, in particular, can improve the performances of AID, an abductive diagnosis system. The approach aims at focusing the overall diagnostic cycle in two interdependent ways: avoiding the generation of candidate solutions to be discarded a-posteriori and integrating the generation of candidate solutions with discrimination among different candidates. Knowledge compilation is used off-line to produce operational...

A decision method for Reiter's default logic is developed. It can determine whether a default theory has an extension, whether a formula is in some extension of a default theory and whether a formula is in every extension of a default theory. The method handles full propositional default logic. It can be implemented to work in polynomial space and by using only a theorem prover for the underlying propositional logic as a subroutine. The method divides default reasoning into two major subtasks: the search task of examining every alternative for extensions, which is solved by backtracking search, and the classical reasoning task, which can be implemented by a theorem prover for the underlying classical logic. Special emphasis is given to the search problem. The decision method employs a new compact representation of extensions which reduces the search space. Efficient techniques for pruning the search space further are developed.

In this paper we show how propositional default theories can be characterized by classical propositional theories: for each finite default theory, we show a classical propositional theory such that there is a one-to-one correspondence between models for the latter and extensions of the former. This means that computing extensions and answering queries about coherence, set-membership and set-entailment are reducible to propositional satisfiability. The general transformation is exponential but tractable for a subset which we call 2-DT --- a superset of network default theories and disjunction-free default theories. Consequently, coherence and set-membership for the class 2-DT is NP-complete and set-entailment is co-NP-complete. This work paves the way for the application of decades of research on efficient algorithms for the satisfiability problem to default reasoning. For example, since propositional satisfiability can be regarded as a constraint satisfaction problem (CSP...

Many systems � such as large manufacturing systems� telecommunication networks � or home automation sys� tems � require distributed monitoring and diagnosis. In this article � we introduce a meta�logic interpreter for vivid agents which allows to develop distributed mon� itoring and diagnosis systems consisting of a variety of scalable knowledge � and perception�based agents. The interpreter is based on PVM�Prolog � an exten� sion of standard Prolog with message passing facili� ties. We show how to specify and run vivid diagnosis agents carrying out fault�tolerant diagnosis of a com� puter network.

This paper considers the representation problem: namely how to go from an abstract problem to a formal representation of the problem. We consider this for two conceptions of logic-based diagnosis, namely abductive and consistency-based diagnosis. We show how to represent diagnostic problems that can be conceptualised causally in each of the frameworks, and show that both representations of the same problems give the same answers. This is a local transformation that allows for an expressive (albeit propositional) language for giving the constraints on what symptoms and causes can coexist, including non-strict causation. This non-strict causation can be represented in each framework without adding special reasoning constructs to either framework. This is presented as a starting point for a study of the representation problem in diagnosis, rather than as an end in itself. 1 Introduction This paper defines an abstract "knowledge representation" problem and considers the problem of represe...

This paper investigates the problem of finding subclasses of nonmonotonic reasoning which can be implemented efficiently. The ability to "define" propositions using default assumptions about the same propositions is identified as a major source of computational complexity in nonmonotonic reasoning. If such constructs are not allowed, i.e. stratied knowledge bases are considered, a significant computational advantage is obtained. This is demonstrated by developing an iterative algorithm for propositional stratified autoepistemic theories the complexity of which is dominated by required classical reasoning. Thus efficient subclasses of stratied nonmonotonic reasoning can be obtained by further restricting the form of sentences in a knowledge base. As an example quadratic and linear time algorithms for specific subclasses of stratified autoepistemic theories are derived. The results are shown to imply efficient reasoning methods for stratied cases of default logic, logic programs, truth maintenance systems, and nonmonotonic modal logics.