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Some philosophical problems from the standpoint of artificial intelligence
 AI, IN MACHINE INTELLIGENCE 4, MELTZER AND MICHIE (EDS
, 1969
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On the Complexity of Propositional Knowledge Base Revision, Updates, and Counterfactuals
 ARTIFICIAL INTELLIGENCE
, 1992
"... We study the complexity of several recently proposed methods for updating or revising propositional knowledge bases. In particular, we derive complexity results for the following problem: given a knowledge base T , an update p, and a formula q, decide whether q is derivable from T p, the updated (or ..."
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Cited by 186 (12 self)
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We study the complexity of several recently proposed methods for updating or revising propositional knowledge bases. In particular, we derive complexity results for the following problem: given a knowledge base T , an update p, and a formula q, decide whether q is derivable from T p, the updated (or revised) knowledge base. This problem amounts to evaluating the counterfactual p > q over T . Besides the general case, also subcases are considered, in particular where T is a conjunction of Horn clauses, or where the size of p is bounded by a constant.
Logical Models of Argument
 ACM COMPUTING SURVEYS
, 2000
"... Logical models of argument formalize commonsense reasoning while taking process and computation seriously. This survey discusses the main ideas which characterize different logical models of argument. It presents the formal features of a few main approaches to the modeling of argumentation. We trace ..."
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Cited by 144 (33 self)
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Logical models of argument formalize commonsense reasoning while taking process and computation seriously. This survey discusses the main ideas which characterize different logical models of argument. It presents the formal features of a few main approaches to the modeling of argumentation. We trace the
Process And Policy: ResourceBounded NonDemonstrative Reasoning
, 1993
"... This paper investigates the appropriateness of formal dialectics as a basis for nonmonotonic reasoning and defeasible reasoning that takes computational limits seriously. Rules that can come into conflict should be regarded as policies, which are inputs to deliberative processes. Dialectical protoc ..."
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Cited by 83 (4 self)
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This paper investigates the appropriateness of formal dialectics as a basis for nonmonotonic reasoning and defeasible reasoning that takes computational limits seriously. Rules that can come into conflict should be regarded as policies, which are inputs to deliberative processes. Dialectical protocols are appropriate for such deliberations when resources are bounded and search is serial. AI, it is claimed here, is now perfectly positioned to correct many misconceptions about reasoning that have resulted from mathematical logic's enormous success in this century: among them, (1) that all reasons are demonstrative, (2) that rational belief is constrained, not constructed, (3) that process and disputation are not essential to reasoning. AI mainly provides new impetus to formalize the alternative (but older) conception of reasoning, and AI provides mechanisms with which to create compelling formalism that describes the control of processes. The technical contributions here are: the partial justification of dialectic based on controlling search; the observation that nonmonotonic reasoning can be subsumed under certain kinds of dialectics; the portrayal of inference in knowledge bases as policy reasoning; the review of logics of dialogue and proposed extensions; and the preformal and initial formal discussion of aspects and variations of dialectical systems with nondemonstrative reasons. 1. ARGUMENTS AND DEMONSTRATION
How Hard is it to Revise a Belief Base?
, 1996
"... If a new piece of information contradicts our previously held beliefs, we have to revise our beliefs. This problem of belief revision arises in a number of areas in Computer Science and Artificial Intelligence, e.g., in updating logical database, in hypothetical reasoning, and in machine learning. M ..."
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Cited by 38 (0 self)
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If a new piece of information contradicts our previously held beliefs, we have to revise our beliefs. This problem of belief revision arises in a number of areas in Computer Science and Artificial Intelligence, e.g., in updating logical database, in hypothetical reasoning, and in machine learning. Most of the research in this area is influenced by work in philosophical logic, in particular by Gardenfors and his colleagues, who developed the theory of belief revision. Here we will focus on the computational aspects of this theory, surveying results that address the issue of the computational complexity of belief revision.
Counterfactual Reasoning Based on Revising Assumptions
, 1991
"... We present a semantics for counterfactual implication relative to positive programs with integrity constraints and protected rules (unrevisable). A counterfactual truth evaluation is presented based on Contradiction Removal Semantics. We show the adequacy of CRS to define the notion of similarity ne ..."
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Cited by 23 (13 self)
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We present a semantics for counterfactual implication relative to positive programs with integrity constraints and protected rules (unrevisable). A counterfactual truth evaluation is presented based on Contradiction Removal Semantics. We show the adequacy of CRS to define the notion of similarity needed for defining counterfactual truth. The concepts introduced by CRS are adequate to support the revision process required by contravening hypoteses when evaluating counterfactuals. 1 Introduction We present a semantics for counterfactual implication relative to positive logic programs with integrity constraints and protected (unrevisable) rules.The Contradiction Removal Semantics (CRS ) [PAA91a] setting employed is a suitable extension to well founded model semantics (WFS) [VGRS90], to deal with classical negation and contradiction, by allowing the revising of assumptions. Lewis [Lew73] is a classic essay on counterfactuals, and our basic definitions and concepts drawn on it. In the next...