28 found
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  1.  43
    Intention is choice with commitment.Philip R. Cohen & Hector J. Levesque - 1990 - Artificial Intelligence 42 (2-3):213-261.
    This paper explores principles governing the rational balance among an agent's beliefs, goals, actions, and intentions. Such principles provide specifications for artificial agents, and approximate a theory of human action (as philosophers use the term). By making explicit the conditions under which an agent can drop his goals, i.e., by specifying how the agent is committed to his goals, the formalism captures a number of important properties of intention. Specifically, the formalism provides analyses for Bratman's three characteristic functional roles played (...)
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  2.  61
    Logic and the complexity of reasoning.Hector J. Levesque - 1988 - Journal of Philosophical Logic 17 (4):355 - 389.
  3.  14
    Foundations of a functional approach to knowledge representation.Hector J. Levesque - 1984 - Artificial Intelligence 23 (2):155-212.
  4.  33
    All I know: A study in autoepistemic logic.Hector J. Levesque - 1990 - Artificial Intelligence 42 (2-3):263-309.
  5. Teamwork.Philip R. Cohen & Hector J. Levesque - 1991 - Noûs 25 (4):487-512.
    What is involved when a group of agents decide to do something together? Joint action by a team appears to involve more than just the union of simultaneous individual actions, even when those actions are coordinated. We would not say that there is any teamwork involved in ordinary automobile traffic, even though the drivers act simultaneously and are coordinated (one hopes) by the traffic signs and rules of the road. But when a group of drivers decide to do something together, (...)
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  6.  5
    Making believers out of computers.Hector J. Levesque - 1986 - Artificial Intelligence 30 (1):81-108.
  7.  6
    Knowledge, action, and the frame problem.Richard B. Scherl & Hector J. Levesque - 2003 - Artificial Intelligence 144 (1-2):1-39.
  8.  18
    Reasoning about noisy sensors and effectors in the situation calculus.Fahiem Bacchus, Joseph Y. Halpern & Hector J. Levesque - 1999 - Artificial Intelligence 111 (1-2):171-208.
  9.  11
    Iterated belief change in the situation calculus.Steven Shapiro, Maurice Pagnucco, Yves Lespérance & Hector J. Levesque - 2011 - Artificial Intelligence 175 (1):165-192.
  10.  8
    On our Best Behaviour.Hector J. Levesque - 2014 - Artificial Intelligence 213 (C):27-35.
  11.  8
    Reasoning about discrete and continuous noisy sensors and effectors in dynamical systems.Vaishak Belle & Hector J. Levesque - 2018 - Artificial Intelligence 262 (C):189-221.
  12.  14
    The Logic of Knowledge Bases.Hector J. Levesque & Gerhard Lakemeyer - 2001 - MIT Press.
    This book describes in detail the relationship between symbolic representations of knowledge and abstract states of knowledge, exploring along the way the foundations of knowledge, knowledge bases, knowledge-based systems, and knowledge representation and reasoning. The idea of knowledge bases lies at the heart of symbolic, or "traditional," artificial intelligence. A knowledge-based system decides how to act by running formal reasoning procedures over a body of explicitly represented knowledge—a knowledge base. The system is not programmed for specific tasks; rather, it is (...)
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  13.  6
    Regression and progression in stochastic domains.Vaishak Belle & Hector J. Levesque - 2020 - Artificial Intelligence 281 (C):103247.
  14.  11
    Generating hard satisfiability problems.Bart Selman, David G. Mitchell & Hector J. Levesque - 1996 - Artificial Intelligence 81 (1-2):17-29.
  15.  84
    Ability and knowing how in the situation calculus.Yves Lespérance, Hector J. Levesque, Fangzhen Lin & Richard B. Scherl - 2000 - Studia Logica 66 (1):165-186.
    Most agents can acquire information about their environments as they operate. A good plan for such an agent is one that not only achieves the goal, but is also executable, i.e., ensures that the agent has enough information at every step to know what to do next. In this paper, we present a formal account of what it means for an agent to know how to execute a plan and to be able to achieve a goal. Such a theory is (...)
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  16.  9
    ConGolog, a concurrent programming language based on the situation calculus.Giuseppe De Giacomo, Yves Lespérance & Hector J. Levesque - 2000 - Artificial Intelligence 121 (1-2):109-169.
  17.  8
    A semantic characterization of a useful fragment of the situation calculus with knowledge.Gerhard Lakemeyer & Hector J. Levesque - 2011 - Artificial Intelligence 175 (1):142-164.
  18.  6
    Indexical knowledge and robot action—a logical account.Yves Lespérance & Hector J. Levesque - 1995 - Artificial Intelligence 73 (1-2):69-115.
  19.  14
    The Consistency of Syntactical Treatments of Knowledge.Hector J. Levesque - 1988 - Journal of Symbolic Logic 53 (2):665-666.
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  20.  13
    Robot location estimation in the situation calculus.Vaishak Belle & Hector J. Levesque - 2015 - Journal of Applied Logic 13 (4):397-413.
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  21.  13
    A Logical Theory of Localization.Vaishak Belle & Hector J. Levesque - 2016 - Studia Logica 104 (4):741-772.
    A central problem in applying logical knowledge representation formalisms to traditional robotics is that the treatment of belief change is categorical in the former, while probabilistic in the latter. A typical example is the fundamental capability of localization where a robot uses its noisy sensors to situate itself in a dynamic world. Domain designers are then left with the rather unfortunate task of abstracting probabilistic sensors in terms of categorical ones, or more drastically, completely abandoning the inner workings of sensors (...)
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  22.  7
    Support set selection for a bductive and default reasoning.Bart Selman & Hector J. Levesque - 1996 - Artificial Intelligence 82 (1-2):259-272.
  23.  2
    What robots can do: robot programs and effective achievability.Fangzhen Lin & Hector J. Levesque - 1998 - Artificial Intelligence 101 (1-2):201-226.
  24.  10
    The complexity of path-based defeasible inheritance.Bart Selman & Hector J. Levesque - 1993 - Artificial Intelligence 62 (2):303-339.
  25.  8
    How to progress a database III.Stavros Vassos & Hector J. Levesque - 2013 - Artificial Intelligence 195 (C):203-221.
  26.  3
    Introduction to the special volume on knowledge representation.Ronald J. Brachman, Hector J. Levesque & Ray Reiter - 1991 - Artificial Intelligence 49 (1-3):1-3.
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  27.  1
    Proceedings of the First International Conference on Principles of Knowledge Representation and Reasoning.Ronald J. Brachman, Hector J. Levesque & Ray Reiter - 1989 - Morgan Kaufmann Publishers.
    Proceedings held May 1989. Topics include temporal logic, hierarchical knowledge bases, default theories, nonmonotonic and analogical reasoning, formal theories of belief revision, and metareasoning. Annotation copyright Book News, Inc. Portland, Or.
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  28.  20
    An Epistemic Approach to Nondeterminism: Believing in the Simplest Course of Events.James P. Delgrande & Hector J. Levesque - 2019 - Studia Logica 107 (5):859-886.
    This paper describes an approach for reasoning in a dynamic domain with nondeterministic actions in which an agent’s beliefs correspond to the simplest, or most plausible, course of events consistent with the agent’s observations and beliefs. The account is based on an epistemic extension of the situation calculus, a first-order theory of reasoning about action that accommodates sensing actions. In particular, the account is based on a qualitative theory of nondeterminism. Our position is that for commonsense reasoning, the world is (...)
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