The Experts below are selected from a list of 159 Experts worldwide ranked by ideXlab platform

Johan J. Lukkien - One of the best experts on this subject based on the ideXlab platform.

  • MPC - An Operational Semantics for the Guarded Command Language
    Lecture Notes in Computer Science, 1993
    Co-Authors: Johan J. Lukkien
    Abstract:

    In [6], Dijkstra and Scholten present an axiomatic semantics for Dijkstra's guarded Command Language through the notions of weakest precondition and weakest liberal precondition. The informal notion of a computation is used as a justification for the various definitions. In this paper we present an operational semantics in which the notion of a computation is made explicit. The novel contribution is a generalization of the notion of weakest precondition. This generalization supports reasoning about general properties of programs (i.e, not just termination in a certain state).

  • an operational semantics for the guarded Command Language
    Mathematics of Program Construction, 1992
    Co-Authors: Johan J. Lukkien
    Abstract:

    In [6], Dijkstra and Scholten present an axiomatic semantics for Dijkstra's guarded Command Language through the notions of weakest precondition and weakest liberal precondition. The informal notion of a computation is used as a justification for the various definitions. In this paper we present an operational semantics in which the notion of a computation is made explicit. The novel contribution is a generalization of the notion of weakest precondition. This generalization supports reasoning about general properties of programs (i.e, not just termination in a certain state).

Annabelle Mciver - One of the best experts on this subject based on the ideXlab platform.

  • operational versus weakest pre expectation semantics for the probabilistic guarded Command Language
    Performance Evaluation, 2014
    Co-Authors: Joost-pieter Katoen, Friedrich Gretz, Annabelle Mciver
    Abstract:

    This paper proposes a simple operational semantics of pGCL, Dijkstra's guarded Command Language extended with probabilistic choice, and relates this to pGCL's wp-semantics by McIver and Morgan. Parametric Markov decision processes whose state rewards depend on the post-expectation at hand are used as the operational model. We show that the weakest pre-expectation of a pGCL-program w.r.t. a post-expectation corresponds to the expected cumulative reward to reach a terminal state in the parametric MDP associated to the program. In a similar way, we show a correspondence between weakest liberal pre-expectations and liberal expected cumulative rewards. The verification of probabilistic programs using wp-semantics and operational semantics is illustrated using a simple running example.

  • operational versus weakest precondition semantics for the probabilistic guarded Command Language
    Quantitative Evaluation of Systems, 2012
    Co-Authors: Friedrich Gretz, Joost-pieter Katoen, Annabelle Mciver
    Abstract:

    This paper proposes a simple operational semanticsof pGCL, Dijkstra's guarded Command Language extended withprobabilistic choice, and relates this to pGCL's wp-semantics byMcIver and Morgan. Parameterised Markov decision processeswhose state rewards depend on the post-expectation at handare used as operational model. We show that the weakest pre-expectationof a pGCL-program w.r.t. a post-expectation correspondsto the expected cumulative reward to reach a terminalstate in the parameterised MDP associated to the program. In asimilar way, we show a correspondence between weakest liberalpre-expectations and liberal expected cumulative rewards.

  • QEST - Operational Versus Weakest Precondition Semantics for the Probabilistic Guarded Command Language
    2012 Ninth International Conference on Quantitative Evaluation of Systems, 2012
    Co-Authors: Friedrich Gretz, Joost-pieter Katoen, Annabelle Mciver
    Abstract:

    This paper proposes a simple operational semanticsof pGCL, Dijkstra's guarded Command Language extended withprobabilistic choice, and relates this to pGCL's wp-semantics byMcIver and Morgan. Parameterised Markov decision processeswhose state rewards depend on the post-expectation at handare used as operational model. We show that the weakest pre-expectationof a pGCL-program w.r.t. a post-expectation correspondsto the expected cumulative reward to reach a terminalstate in the parameterised MDP associated to the program. In asimilar way, we show a correspondence between weakest liberalpre-expectations and liberal expected cumulative rewards.

Matt Banister - One of the best experts on this subject based on the ideXlab platform.

  • a Command Language for taskable virtual agents
    National Conference on Artificial Intelligence, 2010
    Co-Authors: Pat Langley, Nishant Trivedi, Matt Banister
    Abstract:

    In this paper, we report progress on making synthetic characters more taskable. In particular, we present an English-like Command Language that lets one specify complex behaviors an agent should carry out in a virtual environment. We also report compilers that translate English Commands into a formal notation and formal statements into procedures for ICARUS, an agent architecture that supports reactive execution. To demonstrate the benefits of such taskability, we have integrated ICARUS with TWIG, which provides a simulated physical environment with humanoid agents. We use the Command Language to specify three complex activities, including responding to an object contingently, collecting and storing a set of objects, and negotiating with another agent in order to purchase an item. We also discuss related work on controlling synthetic characters, along with paths for additional research on taskability.

  • AIIDE - A Command Language for taskable virtual agents
    2010
    Co-Authors: Pat Langley, Nishant Trivedi, Matt Banister
    Abstract:

    In this paper, we report progress on making synthetic characters more taskable. In particular, we present an English-like Command Language that lets one specify complex behaviors an agent should carry out in a virtual environment. We also report compilers that translate English Commands into a formal notation and formal statements into procedures for ICARUS, an agent architecture that supports reactive execution. To demonstrate the benefits of such taskability, we have integrated ICARUS with TWIG, which provides a simulated physical environment with humanoid agents. We use the Command Language to specify three complex activities, including responding to an object contingently, collecting and storing a set of objects, and negotiating with another agent in order to purchase an item. We also discuss related work on controlling synthetic characters, along with paths for additional research on taskability.

James J Alpigini - One of the best experts on this subject based on the ideXlab platform.

  • rough sets guarded Command Language and decision rules
    Lecture Notes in Computer Science, 2002
    Co-Authors: Frederick V Ramsey, James J Alpigini
    Abstract:

    The rough set approach is a mathematical tool for dealing with imprecision, uncertainty, and vagueness in data. Guarded Command Languages provide logical approaches for representing constrained nondeterminacy in an otherwise deterministic system without incorporating probabilistic elements. Although from dramatically different functional and mathematical origins, both approaches attempt to resolve observed or anticipated discontinuities between specific pre- and postcondition states of a given information system. This paper investigates the use of a guarded Command Language in the generation of rough data from explicit decision rules, and in the extraction of implicit decision rules from rough experimental data. Based on these findings, rough sets and guarded Command Languages appear to be compatible and complementary in their approaches to imprecision and uncertainty. As the association between rough sets and guarded Command Language represents a new and heretofore untested research direction, possible research alternatives are suggested.

  • Rough Sets and Current Trends in Computing - Rough Sets, Guarded Command Language, and Decision Rules
    Rough Sets and Current Trends in Computing, 2002
    Co-Authors: Frederick V Ramsey, James J Alpigini
    Abstract:

    The rough set approach is a mathematical tool for dealing with imprecision, uncertainty, and vagueness in data. Guarded Command Languages provide logical approaches for representing constrained nondeterminacy in an otherwise deterministic system without incorporating probabilistic elements. Although from dramatically different functional and mathematical origins, both approaches attempt to resolve observed or anticipated discontinuities between specific pre- and postcondition states of a given information system. This paper investigates the use of a guarded Command Language in the generation of rough data from explicit decision rules, and in the extraction of implicit decision rules from rough experimental data. Based on these findings, rough sets and guarded Command Languages appear to be compatible and complementary in their approaches to imprecision and uncertainty. As the association between rough sets and guarded Command Language represents a new and heretofore untested research direction, possible research alternatives are suggested.

Friedrich Gretz - One of the best experts on this subject based on the ideXlab platform.

  • operational versus weakest pre expectation semantics for the probabilistic guarded Command Language
    Performance Evaluation, 2014
    Co-Authors: Joost-pieter Katoen, Friedrich Gretz, Annabelle Mciver
    Abstract:

    This paper proposes a simple operational semantics of pGCL, Dijkstra's guarded Command Language extended with probabilistic choice, and relates this to pGCL's wp-semantics by McIver and Morgan. Parametric Markov decision processes whose state rewards depend on the post-expectation at hand are used as the operational model. We show that the weakest pre-expectation of a pGCL-program w.r.t. a post-expectation corresponds to the expected cumulative reward to reach a terminal state in the parametric MDP associated to the program. In a similar way, we show a correspondence between weakest liberal pre-expectations and liberal expected cumulative rewards. The verification of probabilistic programs using wp-semantics and operational semantics is illustrated using a simple running example.

  • operational versus weakest precondition semantics for the probabilistic guarded Command Language
    Quantitative Evaluation of Systems, 2012
    Co-Authors: Friedrich Gretz, Joost-pieter Katoen, Annabelle Mciver
    Abstract:

    This paper proposes a simple operational semanticsof pGCL, Dijkstra's guarded Command Language extended withprobabilistic choice, and relates this to pGCL's wp-semantics byMcIver and Morgan. Parameterised Markov decision processeswhose state rewards depend on the post-expectation at handare used as operational model. We show that the weakest pre-expectationof a pGCL-program w.r.t. a post-expectation correspondsto the expected cumulative reward to reach a terminalstate in the parameterised MDP associated to the program. In asimilar way, we show a correspondence between weakest liberalpre-expectations and liberal expected cumulative rewards.

  • QEST - Operational Versus Weakest Precondition Semantics for the Probabilistic Guarded Command Language
    2012 Ninth International Conference on Quantitative Evaluation of Systems, 2012
    Co-Authors: Friedrich Gretz, Joost-pieter Katoen, Annabelle Mciver
    Abstract:

    This paper proposes a simple operational semanticsof pGCL, Dijkstra's guarded Command Language extended withprobabilistic choice, and relates this to pGCL's wp-semantics byMcIver and Morgan. Parameterised Markov decision processeswhose state rewards depend on the post-expectation at handare used as operational model. We show that the weakest pre-expectationof a pGCL-program w.r.t. a post-expectation correspondsto the expected cumulative reward to reach a terminalstate in the parameterised MDP associated to the program. In asimilar way, we show a correspondence between weakest liberalpre-expectations and liberal expected cumulative rewards.