Raymond Reiter

A logic for default reasoning

The need to make default assumptions is frequently encountered in reasoning about incompletely specified worlds. Inferences sanctioned by default are best viewed as beliefs which may well be modified or rejected by subsequent observations. It is this property which leads to the non-monotonicity of any logic of defaults. In this paper we propose a logic for default reasoning. We then specialize our treatment to a very large class of commonly occuring defaults. For this class we develop a complete proof theory and show how to interface it with a top down resolution theorem prover. Finally, we provide criteria under which the revision of derived beliefs must be effected.

On reasoning by default

This paper surveys a number of kinds of default reasoning in Artificial Intelligence, specifically, default assignments to variables, the closed world assumption, the frame default for causal worlds, exceptions as defaults, and negation in Artificial Intelligence programming languages. Some of these defaults provide clear representational and computaional advantanges over their corresponding first order theories. Finally, the paper discusses various difficulties associated with default theories.

A theory of diagnosis from first principles

C i t b d Di i Consistency-based Diagnosis 1 Introduction 2 Diagnosis as constrain propagation plus register of 2 Diagnosis as constrain propagation plus register of dependencies 3 General Diagnostic Engine:GDE 4 A theory of diagnosis from first principles 5 CBD without on-line dependency-recording: the possible conflict approach possible conflict approach 6 Current research areas and open problems

Some Representational Issues in Default Reasoning

Abstract Although most commonly occurring default rules are normal when viewed in isolation, they can interact with each other in ways that lead to the derivation of anomalous default assumptions. In order to deal with such anomalies it is necessary to re-represent these rules, in some cases by introducing non-normal defaults. The need to consider such potential interactions leads to a new concept of integrity, distinct from the conventional integrity issues of first order data bases. The non-normal default rules required to deal with default interactions all have a common pattern. Default theories conforming to this pattern are considerably more complex than normal default theories. For example, they need not have extensions, and they lack the property of semi-monotonicity. Current semantic network representations fail to reason correctly with defaults. However, when viewed as indexing schemes on logical formulae, networks can be seen to provide computationally feasible heuristics for the consistency checks required by default reasoning.

SOME REPRESENTATIONAL ISSUES IN DEFAULT REASONING

Although most commonly occurring default rules are normal when viewed in isolation, they can interact with each other in ways that lead to the derivation of anomalous default assumptions. In order to deal with such anomalies it is necessary to re-represent these rules, in some cases by introducing non-normal defaults. The need to consider such potential interactions leads to a new concept of integrity, distinct from the conventional integrity issues of first order data bases. The non-normal default rules required to deal with default interactions all have a common pattern. Default theories conforming to this pattern are considerably more complex than normal default theories. For example, they need not have extensions, and they lack the property of semi-monotonicity. Current semantic network representations fail to reason correctly with defaults. However, when viewed as indexing schemes on logical formulae, networks can be seen to provide computationally feasible heuristics for the consistency checks required by default reasoning.