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  1. From systems to logic in the early development of nonmonotonic reasoning.Erik Sandewall - 2011 - Artificial Intelligence 175 (1):416-427.
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  • Non-monotonic logic I.Drew McDermott & Jon Doyle - 1980 - Artificial Intelligence 13 (1-2):41-72.
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  • How circuits work.Johan De Kleer - 1984 - Artificial Intelligence 24 (1-3):205-280.
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  • Network-based heuristics for constraint-satisfaction problems.Rina Dechter & Judea Pearl - 1987 - Artificial Intelligence 34 (1):1-38.
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  • Design by derivational analogy:Issues in the automated replay of design plans.Jack Mostow - 1989 - Artificial Intelligence 40 (1-3):119-184.
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  • Enhancement schemes for constraint processing: Backjumping, learning, and cutset decomposition.Rina Dechter - 1990 - Artificial Intelligence 41 (3):273-312.
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  • Representing scientific knowledge for quantitative analysis of physical systems.Soroush Mobasheri & Mehrnoush Shamsfard - 2020 - Applied ontology 15 (4):439-474.
    Representation of scientific knowledge in ontologies suffers so often from the lack of computational knowledge required for inference. This article aims to perform quantitative analysis on physical systems, that is, to answer questions about values of quantitative state variables of a physical system with known structure. For this objective, we incorporate procedural knowledge on two distinct levels. At the domain-specific level, we propose a representation model for scientific knowledge, i.e. variables, theories, and laws of nature. At the domain-independent level, we (...)
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  • Ordering conjunctive queries.David E. Smith & Michael R. Genesereth - 1985 - Artificial Intelligence 26 (2):171-215.
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  • On the relations between intelligent backtracking and failure-driven explanation-based learning in constraint satisfaction and planning.Subbarao Kambhampati - 1998 - Artificial Intelligence 105 (1-2):161-208.
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  • Symbolic reasoning among 3-D models and 2-D images.Rodney A. Brooks - 1981 - Artificial Intelligence 17 (1-3):285-348.
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  • How to register dissatisfaction with A.I.Eugene Charniak - 1978 - Behavioral and Brain Sciences 1 (2):230-231.
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  • Meta-rules: Reasoning about control.Randall Davis - 1980 - Artificial Intelligence 15 (3):179-222.
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  • SALT: A knowledge acquisition language for propose-and-revise systems.Sandra Marcus & John McDermott - 1989 - Artificial Intelligence 39 (1):1-37.
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  • Understanding complex dynamics by visual and symbolic reasoning.Kenneth Man-Kam Yip - 1991 - Artificial Intelligence 51 (1-3):179-221.
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  • Retrospective on “Diagnostic reasoning based on structure and behavior”.Randall Davis - 1993 - Artificial Intelligence 59 (1-2):149-157.
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  • The relation between epistemology and psychology.Alvin I. Goldman - 1985 - Synthese 64 (1):29-68.
    In the wake of Frege's attack on psychologism and the subsequent influence of Logical Positivism, psychological considerations in philosophy came to be viewed with suspicion. Philosophical questions, especially epistemological ones, were viewed as 'logical' questions, and logic was sharply separated from psychology. Various efforts have been made of late to reconnect epistemology with psychology. But there is little agreement about how such connections should be made, and doubts about the place of psychology within epistemology are still much in evidence. It (...)
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  • A truth maintenance system.Jon Doyle - 1979 - Artificial Intelligence 12 (3):231-272.
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  • A perspective on assumption-based truth maintenance.Johan de Kleer - 1993 - Artificial Intelligence 59 (1-2):63-67.
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  • Experimental evaluation of preprocessing algorithms for constraint satisfaction problems.Rina Dechter & Itay Meiri - 1994 - Artificial Intelligence 68 (2):211-241.
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  • Multi-agent oriented constraint satisfaction.Jiming Liu, Han Jing & Y. Y. Tang - 2002 - Artificial Intelligence 136 (1):101-144.
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  • Constraints—A language for expressing almost-hierarchical descriptions.Gerald Jay Sussman & Guy Lewis Steele - 1980 - Artificial Intelligence 14 (1):1-39.
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  • A general framework for reason maintenance.Drew McDermott - 1991 - Artificial Intelligence 50 (3):289-329.
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  • (1 other version)The complexity of propositional proofs.Nathan Segerlind - 2007 - Bulletin of Symbolic Logic 13 (4):417-481.
    Propositional proof complexity is the study of the sizes of propositional proofs, and more generally, the resources necessary to certify propositional tautologies. Questions about proof sizes have connections with computational complexity, theories of arithmetic, and satisfiability algorithms. This is article includes a broad survey of the field, and a technical exposition of some recently developed techniques for proving lower bounds on proof sizes.
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  • The satisfiability constraint gap.Ian P. Gent & Toby Walsh - 1996 - Artificial Intelligence 81 (1-2):59-80.
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  • Refining the phase transition in combinatorial search.Tad Hogg - 1996 - Artificial Intelligence 81 (1-2):127-154.
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  • Cmpositional modeling: finding the right model for the job.Brian Falkenhainer & Kenneth D. Forbus - 1991 - Artificial Intelligence 51 (1-3):95-143.
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  • Qualitative analysis of MOS circuits.Brian C. Williams - 1984 - Artificial Intelligence 24 (1-3):281-346.
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  • An assumption-based TMS.Johan de Kleer - 1986 - Artificial Intelligence 28 (2):127-162.
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  • Hybrid backtracking bounded by tree-decomposition of constraint networks.Philippe Jégou & Cyril Terrioux - 2003 - Artificial Intelligence 146 (1):43-75.
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  • The organization of expert systems, a tutorial.Mark Stefik, Jan Aikins, Robert Balzer, John Benoit, Lawrence Birnbaum, Frederick Hayes-Roth & Earl Sacerdoti - 1982 - Artificial Intelligence 18 (2):135-173.
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  • Backjump-based backtracking for constraint satisfaction problems.Rina Dechter & Daniel Frost - 2002 - Artificial Intelligence 136 (2):147-188.
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  • Planning and Acting.Drew McDermott - 1978 - Cognitive Science 2 (2):71-100.
    A new theory of problem solving is presented, which embeds problem solving in the theory of action; in this theory, a problem is just a difficult action. Making this work requires a sophisticated language for‐talking about plans and their execution. This language allows a broad range of types of action, and can also be used to express rules for choosing and scheduling plans. To ensure flexibility, the problem solver consists of an interpreter driven by a theorem prover which actually manipulates (...)
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  • Making compromises among antagonist constraints in a planner.Yannick Descotte & Jean-Claude Latombe - 1985 - Artificial Intelligence 27 (2):183-217.
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  • Qualitative process theory.Kenneth D. Forbus - 1984 - Artificial Intelligence 24 (1-3):85-168.
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  • CyclePad: An articulate virtual laboratory for engineering thermodynamics.Kenneth D. Forbus, Peter B. Whalley, John O. Everett, Leo Ureel, Mike Brokowski, Julie Baher & Sven E. Kuehne - 1999 - Artificial Intelligence 114 (1-2):297-347.
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  • Diagnostic reasoning based on structure and behavior.Randall Davis - 1984 - Artificial Intelligence 24 (1-3):347-410.
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  • Qualitative reasoning about physical systems: A return to roots.Brian C. Williams & Johan de Kleer - 1991 - Artificial Intelligence 51 (1-3):1-9.
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  • Generating multiple new designs from a sketch.Thomas F. Stahovich, Randall Davis & Howard Shrobe - 1998 - Artificial Intelligence 104 (1-2):211-264.
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  • The psychology of knights and knaves.Lance J. Rips - 1989 - Cognition 31 (2):85-116.
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  • A model for belief revision.João P. Martins & Stuart C. Shapiro - 1988 - Artificial Intelligence 35 (1):25-79.
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  • The roles of associational and causal reasoning in problem solving.Reid G. Simmons - 1992 - Artificial Intelligence 53 (2-3):159-207.
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  • The anomalous extension problem in default reasoning.Paul H. Morris - 1988 - Artificial Intelligence 35 (3):383-399.
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  • Backtracking techniques for the job shop scheduling constraint satisfaction problem.Norman Sadeh, Katia Sycara & Yalin Xiong - 1995 - Artificial Intelligence 76 (1-2):455-480.
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  • Conservative augmentation of classical theories.J. D. Mackenzie - 1986 - Australasian Journal of Philosophy 64 (2):150 – 157.
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  • A conflict-directed approach to chance-constrained mixed logical linear programming.Cheng Fang & Brian C. Williams - 2023 - Artificial Intelligence 323 (C):103972.
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  • A rational reconstruction of nonmonotonic truth maintenance systems.Charles Elkan - 1990 - Artificial Intelligence 43 (2):219-234.
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  • Theories of causal ordering.Johan de Kleer & John Seely Brown - 1986 - Artificial Intelligence 29 (1):33-61.
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