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Pat Langley [16]Patrick W. Langley [2]Patrick Langley [1]
  1.  6
    Scientific Discovery.Pat Langley, Herbert A. Simon, Gary L. Bradshaw & Jan M. Zytkow - 1993 - In Alvin Goldman (ed.), Readings in Philosophy and Cognitive Science. Cambridge: MIT Press.
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  2.  8
    Ability, Breadth, and Parsimony in Computational Models of Higher-Order Cognition.Nicholas Cassimatis, Paul Bello & Pat Langley - 2008 - Cognitive Science 32 (8):1304-1322.
  3. Scientific Discovery as Problem Solving.Herbert A. Simon, Patrick W. Langley & Gary L. Bradshaw - 1981 - Synthese 47 (1):3 – 14.
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  4.  9
    Scientific Discovery as Problem Solving.Herbert A. Simon, Patrick W. Langley & Gary L. Bradshaw - 1981 - Synthese 47 (1):1-27.
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  5.  95
    Two Kinds of Knowledge in Scientific Discovery.Will Bridewell & Pat Langley - 2010 - Topics in Cognitive Science 2 (1):36-52.
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  6.  13
    Data‐Driven Discovery of Physical Laws.Pat Langley - 1981 - Cognitive Science 5 (1):31-54.
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  7.  11
    Abductive Understanding of Dialogues About Joint Activities.Pat Langley, Ben Meadows, Alfredo Gabaldon & Richard Heald - 2014 - Interaction Studies 15 (3):426-454.
    This paper examines the task of understanding dialogues in terms of the mental states of the participating agents. We present a motivating example that clarifies the challenges this problem involves and then outline a theory of dialogue interpretation based on abductive inference of these unobserved beliefs and goals, incremental construction of explanations, and reliance on domain-independent knowledge. After this, we describe UMBRA, an implementation of the theory that embodies these assumptions. We report experiments with the system that demonstrate its ability (...)
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  8.  8
    Abductive Understanding of Dialogues About Joint Activities.Pat Langley, Ben Meadows, Alfredo Gabaldon & Richard Heald - 2014 - Interaction Studies: Social Behaviour and Communication in Biological and Artificial Systems 15 (3):426-454.
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  9.  12
    Efforts to Encourage Multidisciplinarity in the Cognitive Science Society.James G. Greeno, William J. Clancey, Clayton Lewis, Mark Seidenberg, Sharon Derry, Morton Ann Gernsbacher, Patrick Langley, Michael Shafto, Dedre Gentner, Alan Lesgold & Colleen M. Seifert - 1998 - Cognitive Science 22 (1):131-132.
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  10.  28
    Claims and Challenges in Evaluating Human-Level Intelligent Systems.John E. Laird, Robert Wray, Robert Marinier & Pat Langley - 2009 - In B. Goertzel, P. Hitzler & M. Hutter (eds.), Proceedings of the Second Conference on Artificial General Intelligence. Atlantis Press.
  11.  8
    Computational Discovery of Communicable Scientific Knowledge.Pat Langley, Jeff Shrager & Kazumi Saito - 2002 - In L. Magnani, N. J. Nersessian & C. Pizzi (eds.), Logical and Computational Aspects of Model-Based Reasoning. Kluwer Academic Publishers. pp. 201--225.
  12.  22
    Learning to Search: From Weak Methods to Domain‐Specific Heuristics.Pat Langley - 1985 - Cognitive Science 9 (2):217-260.
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  13.  7
    Scientific Discovery, Causal Explanation, and Process Model Induction.Pat Langley - 2019 - Mind and Society 18 (1):43-56.
    In this paper, I review two related lines of computational research: discovery of scientific knowledge and causal models of scientific phenomena. I also report research on quantitative process models that falls at the intersection of these two themes. This framework represents models as a set of interacting processes, each with associated differential equations that express influences among variables. Simulating such a quantitative process model produces trajectories for variables over time that one can compare to observations. Background knowledge about candidate processes (...)
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  14.  9
    Induction and Explanation: Complementary Models of Learning.Pat Langley - 1986 - Behavioral and Brain Sciences 9 (4):661-662.
  15.  8
    Structure and Process in Schema-Based Architectures.Pat Langley - 1987 - Behavioral and Brain Sciences 10 (3):442-442.
  16.  7
    Approaches to Learning and Representation.Pat Langley - 1990 - Behavioral and Brain Sciences 13 (3):500-501.
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  17.  8
    Template Sampling for Leveraging Domain Knowledge in Information Extraction.Christopher Cox, Christopher Manning & Pat Langley - unknown
    We initially describe a feature-rich discriminative Conditional Random Field (CRF) model for Information Extraction in the workshop announcements domain, which offers good baseline performance in the PASCAL shared task. We then propose a method for leveraging domain knowledge in Information Extraction tasks, scoring candidate document labellings as one-value-per-field templates according to domain feasibility after generating sample labellings from a trained sequence classifier. Our relational models evaluate these templates according to our intuitions about agreement in the domain: workshop acronyms should resemble (...)
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  18.  6
    Bounded Rationality in Problem Solving: Guiding Search with Domain-Independent Heuristics.Pat Langley, Chris Pearce, Mike Barley & Miranda Emery - 2014 - Mind and Society 13 (1):83-95.
    Humans exhibit the remarkable ability to solve complex, multi-step problems despite their limited capacity for search. We review the standard theory of problem solving, which posits that heuristic guidance makes this possible, but we also note that most studies have emphasized the role of domain-specific heuristics, which are not available for unfamiliar tasks, over more general ones. We describe FPS, a flexible architecture for problem solving that supports a variety of different strategies and heuristics, and we report its use in (...)
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