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20
Axioms of Causal Relevance
 Artificial Intelligence
, 1996
"... This paper develops axioms and formal semantics for statements of the form "X is causally irrelevant to Y in context Z," which we interpret to mean "Changing X will not affect Y if we hold Z constant." The axiomization of causal irrelevance is contrasted with the axiomization ..."
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Cited by 54 (13 self)
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This paper develops axioms and formal semantics for statements of the form "X is causally irrelevant to Y in context Z," which we interpret to mean "Changing X will not affect Y if we hold Z constant." The axiomization of causal irrelevance is contrasted with the axiomization of informational irrelevance, as in "Learning X will not alter our belief in Y , once we know Z." Two versions of causal irrelevance are analyzed, probabilistic and deterministic. We show that, unless stability is assumed, the probabilistic definition yields a very loose structure, that is governed by just two trivial axioms. Under the stability assumption, probabilistic causal irrelevance is isomorphic to path interception in cyclic graphs. Under the deterministic definition, causal irrelevance complies with all of the axioms of path interception in cyclic graphs, with the exception of transitivity. We compare our formalism to that of [Lewis, 1973], and offer a graphical method of proving theorems abou...
Useful Counterfactuals
 ETAI (ELECTRONIC TRANSACTIONS ON ARTIFICIAL INTELLIGENCE
, 1999
"... Counterfactual conditional sentences can be useful in articial intelligence as they are in human aairs. In particular, they allow reasoners to learn from experiences that they did not quite have. Our tools for making inferences from counterfactuals permit inferring sentences that are not themselves ..."
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Cited by 18 (2 self)
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Counterfactual conditional sentences can be useful in articial intelligence as they are in human aairs. In particular, they allow reasoners to learn from experiences that they did not quite have. Our tools for making inferences from counterfactuals permit inferring sentences that are not themselves counterfactual. This is what makes them useful. A simple class of useful counterfactuals involves a change of one component of a point in a space provided with a cartesian product structure. We call these cartesian counterfactuals. Cartesian counterfactuals can be modeled by assignment and contents functions as in program semantics. We also consider the more general treestructured counterfactuals.
Probabilities of causation: Three counterfactual interpretations and their identification
 SYNTHESE
, 1999
"... According to common judicial standard, judgment in favor of plaintiff should be made if and only if it is "more probable than not" that the defendant's action was the cause for the plaintiff's damage (or death). This paper provides formal semantics, based on structural models ..."
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Cited by 18 (3 self)
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According to common judicial standard, judgment in favor of plaintiff should be made if and only if it is "more probable than not" that the defendant's action was the cause for the plaintiff's damage (or death). This paper provides formal semantics, based on structural models of counterfactuals, for the probability that event x was a necessary or sufficient cause (or both) of another event y. The paper then explicates conditions under which the probability of necessary (or sufficient) causation can be learned from statistical data, and shows how data from both experimental and nonexperimental studies can be combined to yield information that neither study alone can provide. Finally,weshow that necessity and sufficiency are two independent aspects of causation, and that both should be invoked in the construction of causal explanations for specific scenarios.
Settheoretic completeness for epistemic and conditional logic
 Annals of Mathematics and Artificial Intelligence
, 1999
"... The standard approach to logic in the literature in philosophy and mathematics, which has also been adopted in computer science, is to define a language (the syntax), an appropriate class of models together with an interpretation of formulas in the language (the semantics), a collection of axioms an ..."
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Cited by 16 (3 self)
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The standard approach to logic in the literature in philosophy and mathematics, which has also been adopted in computer science, is to define a language (the syntax), an appropriate class of models together with an interpretation of formulas in the language (the semantics), a collection of axioms and rules of inference characterizing reasoning (the proof theory), and then relate the proof theory to the semantics via soundness and completeness results. Here we consider an approach that is more common in the economics literature, which works purely at the semantic, settheoretic level. We provide settheoretic completeness results for a number of epistemic and conditional logics, and contrast the expressive power of the syntactic and settheoretic approaches.
Contextual Deontic Logic
, 2000
"... In this article we propose contextual deontic logic. Contextual obligations are written as O(ffjfinfl), and are to be read as `ff should be the case if fi is the case, unless fl is the case'. The unless clause is analogous to the justification in Reiter's default rules. We show how conte ..."
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Cited by 9 (4 self)
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In this article we propose contextual deontic logic. Contextual obligations are written as O(ffjfinfl), and are to be read as `ff should be the case if fi is the case, unless fl is the case'. The unless clause is analogous to the justification in Reiter's default rules. We show how contextual obligations can be used to solve certain aspects of contrarytoduty paradoxes of dyadic deontic logic.
Levels of organization in general intelligence
 Artificial General Intelligence
, 2005
"... Section 1 discusses the conceptual foundations of general intelligence as a discipline, orienting it within the Integrated Causal Model of Tooby and Cosmides; Section 2 constitutes the bulk of the paper and discusses the functional decomposition of general intelligence into a complex supersystem of ..."
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Cited by 7 (0 self)
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Section 1 discusses the conceptual foundations of general intelligence as a discipline, orienting it within the Integrated Causal Model of Tooby and Cosmides; Section 2 constitutes the bulk of the paper and discusses the functional decomposition of general intelligence into a complex supersystem of interdependent internally specialized processes, and structures the description using five successive levels of functional organization: Code, sensory modalities, concepts, thoughts, and deliberation. Section 3 discusses probable differences between humans and AIs and points out several fundamental advantages that mindsingeneral potentially possess relative to current evolved intelligences, especially with respect to recursive selfimprovement.
On the Definition of Actual Cause
, 1998
"... This report is based on lecture notes written for CS 262C, Spring 1998, and is organized as follows. Following a review of the SL framework (Section 2) Section 3 provides a comparison to other approaches to causation and suggests an explanation of why the notion of actual cause has encountered diffi ..."
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Cited by 5 (1 self)
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This report is based on lecture notes written for CS 262C, Spring 1998, and is organized as follows. Following a review of the SL framework (Section 2) Section 3 provides a comparison to other approaches to causation and suggests an explanation of why the notion of actual cause has encountered difficulties in those approaches. Section 3 defines "actual cause" and illustrates, through examples, how the "probability that event X = x actually caused event
Causation and Nonmonotonic Temporal Reasoning
 KI97: Advances in Artifical Intelligence, number 1303 in Springer Lecture Notes in Artifical Intelligence
, 1997
"... . We introduce a new approach to reasoning about action and change using nonmonotonic logic. The approach is arrived at by applying Pearl's theory of causal networks to logical formalizations of temporal reasoning domains. It comes in two versions: version S0 that works for logical theories in ..."
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Cited by 3 (2 self)
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. We introduce a new approach to reasoning about action and change using nonmonotonic logic. The approach is arrived at by applying Pearl's theory of causal networks to logical formalizations of temporal reasoning domains. It comes in two versions: version S0 that works for logical theories in which causal knowledge is represented explicitly, and version I0 that works for logical theories in which this is not the case. It turns out that various restrictions of S0 are equivalent to various existing approaches that are explicitly based on causation. Similarly, two of the most wellknown noncausal approaches, namely Baker's account and `chronological minimization with filter preferential entailment', can be reinterpreted as approximations of I0 . We thus provide a reinterpretation in terms of causal network theory of much of the work done in nonmonotonic temporal reasoning. 1 Introduction Any approach to commonsense temporal reasoning must address the notorious frame problem [14]: how...