Switch to: Citations

Add references

You must login to add references.
  1. Epistemology and probability.John L. Pollock - 1983 - Synthese 55 (2):231-252.
    Probability is sometimes regarded as a universal panacea for epistemology. It has been supposed that the rationality of belief is almost entirely a matter of probabilities. Unfortunately, those philosophers who have thought about this most extensively have tended to be probability theorists first, and epistemologists only secondarily. In my estimation, this has tended to make them insensitive to the complexities exhibited by epistemic justification. In this paper I propose to turn the tables. I begin by laying out some rather simple (...)
    Download  
     
    Export citation  
     
    Bookmark   36 citations  
  • A logic for default reasoning.Ray Reiter - 1980 - Artificial Intelligence 13 (1-2):81-137.
    Download  
     
    Export citation  
     
    Bookmark   640 citations  
  • Defeasible Reasoning.John L. Pollock - 1987 - Cognitive Science 11 (4):481-518.
    There was a long tradition in philosophy according to which good reasoning had to be deductively valid. However, that tradition began to be questioned in the 1960’s, and is now thoroughly discredited. What caused its downfall was the recognition that many familiar kinds of reasoning are not deductively valid, but clearly confer justification on their conclusions. Here are some simple examples.
    Download  
     
    Export citation  
     
    Bookmark   342 citations  
  • A logical framework for default reasoning.David Poole - 1988 - Artificial Intelligence 36 (1):27-47.
    Download  
     
    Export citation  
     
    Bookmark   104 citations  
  • On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games.Phan Minh Dung - 1995 - Artificial Intelligence 77 (2):321-357.
    Download  
     
    Export citation  
     
    Bookmark   471 citations  
  • A Temporal Logic for Reasoning about Processes and Plans.Drew McDermott - 1982 - Cognitive Science 6 (2):101-155.
    Much previous work in artificial intelligence has neglected representing time in all its complexity. In particular, it has neglected continuous change and the indeterminacy of the future. To rectify this, I have developed a first‐order temporal logic, in which it is possible to name and prove things about facts, events, plans, and world histories. In particular, the logic provides analyses of causality, continuous change in quantities, the persistence of facts (the frame problem), and the relationship between tasks and actions. It (...)
    Download  
     
    Export citation  
     
    Bookmark   82 citations  
  • Floating conclusions and zombie paths: Two deep difficulties in the “directly skeptical” approach to defeasible inheritance nets.David Makinson & Karl Schlechta - 1991 - Artificial Intelligence 48 (2):199-209.
    Download  
     
    Export citation  
     
    Bookmark   28 citations  
  • A mathematical treatment of defeasible reasoning and its implementation.Guillermo R. Simari & Ronald P. Loui - 1992 - Artificial Intelligence 53 (2-3):125-157.
    We present a mathematical approach to defeasible reasoning based on arguments. This approach integrates the notion of specificity introduced by Poole and the theory of warrant presented by Pollock. The main contribution of this paper is a precise, well-defined system which exhibits correct behavior when applied to the benchmark examples in the literature. It aims for usability rather than novelty. We prove that an order relation can be introduced among equivalence classes of arguments under the equi-specificity relation. We also prove (...)
    Download  
     
    Export citation  
     
    Bookmark   80 citations  
  • Nonmonotonic logic and temporal projection.Steve Hanks & Drew McDermott - 1987 - Artificial Intelligence 33 (3):379-412.
    Download  
     
    Export citation  
     
    Bookmark   95 citations  
  • Perceiving and reasoning about a changing world.John Pollock - unknown
    A rational agent (artificial or otherwise) residing in a complex changing environment must gather information perceptually, update that information as the world changes, and combine that information with causal information to reason about the changing world. Using the system of defeasible reasoning that is incorporated into the OSCAR architecture for rational agents, a set of reasonschemas is proposed for enabling an agent to perform some of the requisite reasoning. Along the way, solutions are proposed for the Frame Problem, the Qualification (...)
    Download  
     
    Export citation  
     
    Bookmark   9 citations  
  • Justification and defeat.John L. Pollock - 1994 - Artificial Intelligence 67 (2):377-407.
    Download  
     
    Export citation  
     
    Bookmark   47 citations  
  • Applications of Circumscription to Formalizing Common Sense Knowledge.John McCarthy - 1986 - Artificial Intelligence 28 (1):89–116.
    Download  
     
    Export citation  
     
    Bookmark   185 citations  
  • Logical Models of Argument.Ronald Prescott Loui, Carlos Ivan Ches~Nevar & Ana Gabriela Maguitman - 2000 - ACM Computing Surveys 32 (4):337-383.
    Logical models of argument formalize commonsense reasoning while taking process and computation seriously. This survey discusses the main ideas which characterize di erent logical models of argument. It presents the formal features of a few main approaches to the modeling of argumentation. We trace the evolution of argumentationfrom the mid-80's, when argumentsystems emerged as an alternative to nonmonotonic formalisms based on classical logic, to the present, as argument is embedded in di erent complex systems for real-world applications, and allows more (...)
    Download  
     
    Export citation  
     
    Bookmark   32 citations  
  • Direct inference.Isaac Levi - 1977 - Journal of Philosophy 74 (1):5-29.
    Download  
     
    Export citation  
     
    Bookmark   78 citations  
  • (1 other version)The logical foundations of goal-regression planning in autonomous agents.John L. Pollock - 1998 - Artificial Intelligence 106 (2):267-334.
    Download  
     
    Export citation  
     
    Bookmark   8 citations