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  1. Controlling recursive inference.David E. Smith, Michael R. Genesereth & Matthew L. Ginsberg - 1986 - Artificial Intelligence 30 (3):343-389.
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  • Planning for conjunctive goals.David Chapman - 1987 - Artificial Intelligence 32 (3):333-377.
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  • Rationality and intelligence.Stuart J. Russell - 1997 - Artificial Intelligence 94 (1-2):57-77.
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  • Cross‐National Comparisons of Complex Problem‐Solving Strategies in Two Microworlds.C. Dominik Güss, Ma Teresa Tuason & Christiane Gerhard - 2010 - Cognitive Science 34 (3):489-520.
    Research in the fields of complex problem solving (CPS) and dynamic decision making using microworlds has been mainly conducted in Western industrialized countries. This study analyzes the CPS process by investigating thinking‐aloud protocols in five countries. Participants were 511 students from Brazil, Germany, India, the Philippines, and the United States who worked on two microworlds. On the basis of cultural‐psychological theories, specific cross‐national differences in CPS strategies were hypothesized. Following theories of situatedness of cognition, hypotheses about the specific frequency of (...)
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  • SOAR: An architecture for general intelligence.John E. Laird, Allen Newell & Paul S. Rosenbloom - 1987 - Artificial Intelligence 33 (1):1-64.
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  • The uses of plans.Martha E. Pollack - 1992 - Artificial Intelligence 57 (1):43-68.
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  • (1 other version)Temporal theories of reasoning.Joeri Engelfriet & Jan Treur - 1995 - Journal of Applied Non-Classical Logics 5 (2):239-261.
    In this paper we describe a general way of formalizing reasoning behaviour. Such a behaviour may be described by all the patterns which are valid for the behaviour. A pattern can be seen as a sequence of information states which describe what has been derived at each time point. A transition from an information state at a point in time to the state at the (or a) next time point is induced by one or more inference steps. We choose to (...)
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  • Content reference: Reasoning about rules.Randall Davis - 1980 - Artificial Intelligence 15 (3):223-239.
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  • Negotiation as a metaphor for distributed problem solving.Randall Davis & Reid G. Smith - 1983 - Artificial Intelligence 20 (1):63-109.
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  • A structural theory of explanation-based learning.Oren Etzioni - 1993 - Artificial Intelligence 60 (1):93-139.
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  • Optimal composition of real-time systems.Shlomo Zilberstein & Stuart Russell - 1996 - Artificial Intelligence 82 (1-2):181-213.
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  • Credit risk assessment and meta-judgment.Suzanne Pinson - 1989 - Theory and Decision 27 (1-2):117-133.
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  • (1 other version)Temporal theories of reasoning.Joeri Engelfriet & Jan Treur - 1995 - Journal of Applied Non-Classical Logics 5 (1):97-119.
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  • Metacognition in computation: A selected research review.Michael T. Cox - 2005 - Artificial Intelligence 169 (2):104-141.
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  • The epistemology of a rule-based expert system —a framework for explanation.William J. Clancey - 1983 - Artificial Intelligence 20 (3):215-251.
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  • Reasoning about nonlinear system identification.Elizabeth Bradley, Matthew Easley & Reinhard Stolle - 2001 - Artificial Intelligence 133 (1-2):139-188.
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  • A method for conceptualising legal domains. An example from the dutch unemployment benefits act.Pepijn Visser, Trevor Bench-Capon & Jaap van den Herik - 1997 - Artificial Intelligence and Law 5 (3):207-242.
    There has been much talk of the need to build intermediate models of the expertise required preparatory to constructing a knowledge-based system in the legal domain. Such models offer advantages for verification, validation, maintenance and reuse. As yet, however, few such models have been reported at a useful level of detail. In this paper we describe a method for conceptualising legal domains as well as its application to a substantial fragment of the Dutch Unemployment Benefits Act (DUBA).We first discuss the (...)
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  • MUSCADET: An automatic theorem proving system using knowledge and metaknowledge in mathematics.Dominique Pastre - 1989 - Artificial Intelligence 38 (3):257-318.
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