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An assumption-based TMS

Artificial Intelligence 28 (2):127-162 (1986)

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  1. In defense of the Turing test.Eric Neufeld & Sonje Finnestad - 2020 - AI and Society 35 (4):819-827.
    In 2014, widespread reports in the popular media that a chatbot named Eugene Goostman had passed the Turing test became further grist for those who argue that the diversionary tactics of chatbots like Goostman and others, such as those who participate in the Loebner competition, are enabled by the open-ended dialog of the Turing test. Some claim a new kind of test of machine intelligence is needed, and one community has advanced the Winograd schema competition to address this gap. We (...)
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  • Maintenance of datalog materialisations revisited.Boris Motik, Yavor Nenov, Robert Piro & Ian Horrocks - 2019 - Artificial Intelligence 269 (C):76-136.
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  • A general framework for reason maintenance.Drew McDermott - 1991 - Artificial Intelligence 50 (3):289-329.
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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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  • A context for belief revision: forward chaining-normal nonmonotomic rule systems.V. W. Marek, A. Nerode & J. B. Remmel - 1994 - Annals of Pure and Applied Logic 67 (1-3):269-323.
    A number of nonmonotonic reasoning formalisms have been introduced to model the set of beliefs of an agent. These include the extensions of a default logic, the stable models of a general logic program, and the extensions of a truth maintenance system among others. In [13] and [16], the authors introduced nonmonotomic rule systems as a nonlogical generalization of all essential features of such formulisms so that theorems applying to all could be proven once and for all. In this paper, (...)
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  • Maintaining mental models of agents who have existential misconceptions.Anthony S. Maida - 1991 - Artificial Intelligence 50 (3):331-383.
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  • Verification of knowledge bases based on containment checking.Alon Y. Levy & Marie-Christine Rousset - 1998 - Artificial Intelligence 101 (1-2):227-250.
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  • Problem solving by searching for models with a theorem prover.Shie-Jue Lee & David A. Plaisted - 1994 - Artificial Intelligence 69 (1-2):205-233.
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  • Maxi-Adjustment and Possibilistic Deduction for Adaptive Information Agents.Raymond Lau, Arthur H. M. ter Hofstede & Peter D. Bruza - 2001 - Journal of Applied Non-Classical Logics 11 (1-2):169-201.
    The expressive power of logic is believed to be able to model most of the fundamental aspects of information retrieval. However, it is also understood that classical logic is ineffective for handling partiality and uncertainty in IR. Applying non-classical logics such as the AGM belief revision logic and the possibilistic logic to adaptive information retrieval is appealing since they provide a powerful and rigorous framework to model partiality and uncertainty inherent in any IR processes. The maxi-adjustment method, which is an (...)
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  • Assumptions, beliefs and probabilities.Kathryn Blackmond Laskey & Paul E. Lehner - 1989 - Artificial Intelligence 41 (1):65-77.
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  • Diagnosis based on explicit means-end models.Jan Eric Larsson - 1996 - Artificial Intelligence 80 (1):29-93.
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  • Relevance from an epistemic perspective.Gerhard Lakemeyer - 1997 - Artificial Intelligence 97 (1-2):137-167.
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  • The Use of Logical Models in Legal Problem Solving.Robert Kowalski & Marek Sergot - 1990 - Ratio Juris 3 (2):201-218.
    The authors describe a logic programming approach to the representation of legislative texts. They consider the potential uses of simple systems which incorporate a single, fixed interpretation of a text. These include assisting in the routine administration of complex areas of the law. The authors also consider the possibility of constructing more complex systems which incorporate several, possibly conflicting interpretations. Such systems are needed for dealing with ambiguity and vagueness in the law. Moreover, they are more suitable than single interpretation (...)
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  • Probabilistic argumentation systems.Jürg Kohlas - 2003 - Journal of Applied Logic 1 (3-4):225-253.
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  • Local consistency in parallel constraint satisfaction networks.Simon Kasif & Arthur L. Delcher - 1994 - Artificial Intelligence 69 (1-2):307-327.
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  • A Proof-theoretic View of Necessity.Reinhard Kahle - 2006 - Synthese 148 (3):659-673.
    We give a reading of binary necessity statements of the form “ϕ is necessary for ψ” in terms of proofs. This reading is based on the idea of interpreting such statements as “Every proof of ψ uses ϕ”.
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  • Reasoning From Inconsistency to Consistency.P. N. Johnson-Laird, Vittorio Girotto & Paolo Legrenzi - 2004 - Psychological Review 111 (3):640-661.
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  • Coordinating Agents in Design: A Formal Analysis.U. V. Johar - 1998 - Journal of Intelligent Systems 8 (3-4):291-314.
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  • Logical reasoning in natural language: It is all about knowledge. [REVIEW]Lucja Iwańska - 1993 - Minds and Machines 3 (4):475-510.
    A formal, computational, semantically clean representation of natural language is presented. This representation captures the fact that logical inferences in natural language crucially depend on the semantic relation of entailment between sentential constituents such as determiner, noun, adjective, adverb, preposition, and verb phrases.The representation parallels natural language in that it accounts for human intuition about entailment of sentences, it preserves its structure, it reflects the semantics of different syntactic categories, it simulates conjunction, disjunction, and negation in natural language by computable (...)
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  • A Reason Maintenance Perspective On Relevant Ramsey Conditionals.Haythem Ismail - 2010 - Logic Journal of the IGPL 18 (4):508-529.
    This paper presents a Ramsey account of conditionals within the framework of an implemented reason maintenance system. The reason maintenance system is built on top of a deductive reasoning engine based on relevance logic. Thus, the account of conditionals provided is not susceptible to the fallacies of relevance. In addition, it is shown that independently motivated requirements on practical relevant reason maintenance allow us to gracefully circumvent Gärdenfors's triviality result.
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  • Linear resolution for consequence finding.Katsumi Inoue - 1992 - Artificial Intelligence 56 (2-3):301-353.
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  • A theory of measurement in diagnosis from first principles.Aimin Hou - 1994 - Artificial Intelligence 65 (2):281-328.
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  • Real Logic is Nonmonotonic.Henry E. Kyburg - 2001 - Minds and Machines 11 (4):577-595.
    Charles Morgan has argued that nonmonotonic logic is ``impossible''. We show here that those arguments are mistaken, and that Morgan's preferred alternative, the representation of nonmonotonic reasoning by ``presuppositions'' fails to provide a framework in which nonmonotonic reasoning can be constructively criticised. We argue that an inductive logic, based on probabilistic acceptance, offers more than Morgan's approach through presuppositions.
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  • How not to change the theory of theory change: A reply to Tennant.Sven Ove Hansson & Hans Rott - 1995 - British Journal for the Philosophy of Science 46 (3):361-380.
    A number of seminal papers on the logic of belief change by Alchourrön, Gärden-fors, and Makinson have given rise to what is now known as the AGM paradigm. The present discussion note is a response to Neil Tennant's [1994], which aims at a critical appraisal of the AGM approach and the introduction of an alternative approach. We show that important parts of Tennants's critical remarks are based on misunderstandings or on lack of information. In the course of doing this, we (...)
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  • Modeling digital circuits for troubleshooting.Walter C. Hamscher - 1991 - Artificial Intelligence 51 (1-3):223-271.
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  • The many uses of 'belief' in AI.Robert F. Hadley - 1991 - Minds and Machines 1 (1):55-74.
    Within AI and the cognitively related disciplines, there exist a multiplicity of uses of belief. On the face of it, these differing uses reflect differing views about the nature of an objective phenomenon called belief. In this paper I distinguish six distinct ways in which belief is used in AI. I shall argue that not all these uses reflect a difference of opinion about an objective feature of reality. Rather, in some cases, the differing uses reflect differing concerns with special (...)
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  • A sense-based, process model of belief.Robert F. Hadley - 1991 - Minds and Machines 1 (3):279-320.
    A process-oriented model of belief is presented which permits the representation of nested propositional attitudes within first-order logic. The model (NIM, for nested intensional model) is axiomatized, sense-based (via intensions), and sanctions inferences involving nested epistemic attitudes, with different agents and different times. Because NIM is grounded upon senses, it provides a framework in which agents may reason about the beliefs of another agent while remaining neutral with respect to the syntactic forms used to express the latter agent's beliefs. Moreover, (...)
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  • The common order-theoretic structure of version spaces and ATMSs.Carl A. Gunter, Teow-Hin Ngair & Devika Subramanian - 1997 - Artificial Intelligence 95 (2):357-407.
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  • The Carneades model of argument and burden of proof.Thomas F. Gordon, Henry Prakken & Douglas Walton - 2007 - Artificial Intelligence 171 (10-15):875-896.
    We present a formal, mathematical model of argument structure and evaluation, taking seriously the procedural and dialogical aspects of argumentation. The model applies proof standards to determine the acceptability of statements on an issue-by-issue basis. The model uses different types of premises (ordinary premises, assumptions and exceptions) and information about the dialectical status of statements (stated, questioned, accepted or rejected) to allow the burden of proof to be allocated to the proponent or the respondent, as appropriate, for each premise separately. (...)
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  • Reasoning about action I.Matthew L. Ginsberg & David E. Smith - 1988 - Artificial Intelligence 35 (2):165-195.
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  • Counterfactuals.Matthew L. Ginsberg - 1986 - Artificial Intelligence 30 (1):35-79.
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  • A circumscriptive theorem prover.Matthew L. Ginsberg - 1989 - Artificial Intelligence 39 (2):209-230.
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  • Conditional entailment: Bridging two approaches to default reasoning.Hector Geffner & Judea Pearl - 1992 - Artificial Intelligence 53 (2-3):209-244.
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  • Theory contraction through base contraction.André Fuhrmann - 1991 - Journal of Philosophical Logic 20 (2):175 - 203.
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  • Towards automated first-order abduction: the cut-based approach.Marcelo Finger - 2012 - Logic Journal of the IGPL 20 (2):370-387.
    Traditional abduction imposes as a precondition the restriction that the background information may not derive the goal data. In first-order logic such precondition is, in general, undecidable. To avoid such problem, we present a first-order cut-based abduction method, which has KE-tableaux as its underlying inference system. This inference system allows for the automation of non-analytic proofs in a tableau setting, which permits a generalization of traditional abduction that avoids the undecidable precondition problem. After demonstrating the correctness of the method, we (...)
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  • AGM 25 Years: Twenty-Five Years of Research in Belief Change.Eduardo Fermé & Sven Ove Hansson - 2011 - Journal of Philosophical Logic 40 (2):295 - 331.
    The 1985 paper by Carlos Alchourrón (1931–1996), Peter Gärdenfors, and David Makinson (AGM), "On the Logic of Theory Change: Partial Meet Contraction and Revision Functions" was the starting-point of a large and rapidly growing literature that employs formal models in the investigation of changes in belief states and databases. In this review, the first twentyfive years of this development are summarized. The topics covered include equivalent characterizations of AGM operations, extended representations of the belief states, change operators not included in (...)
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  • AGM 25 Years: Twenty-Five Years of Research in Belief Change.Eduardo Fermé & Sven Ove Hansson - 2011 - Journal of Philosophical Logic 40 (2):295-331.
    The 1985 paper by Carlos Alchourrón, Peter Gärdenfors, and David Makinson, “On the Logic of Theory Change: Partial Meet Contraction and Revision Functions” was the starting-point of a large and rapidly growing literature that employs formal models in the investigation of changes in belief states and databases. In this review, the first twenty-five years of this development are summarized. The topics covered include equivalent characterizations of AGM operations, extended representations of the belief states, change operators not included in the original (...)
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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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  • Building large knowledge-based systems: Representation and inference in the cyc project.Charles Elkan & Russell Greiner - 1993 - Artificial Intelligence 61 (1):41-52.
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  • Belief change as propositional update.Renée Elio & Francis Jeffry Pelletier - 1997 - Cognitive Science 21 (4):419-460.
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  • On the complexity of propositional knowledge base revision, updates, and counterfactuals.Thomas Eiter & Georg Gottlob - 1992 - Artificial Intelligence 57 (2-3):227-270.
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