We Will Show Them Essays In Honour Of Dov Gabbay

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  • 2015
  • [j23]

    Giles Reger, Howard Barringer, David E. Rydeheard:
    Automata-based Pattern Mining from Imperfect Traces.ACM SIGSOFT Software Engineering Notes40(1): 1-8 (2015)

  • 2014
  • [c40]

    Howard Barringer, David E. Rydeheard, Dov M. Gabbay:
    Reactivity and Grammars: An Exploration.Language, Culture, Computation (1)2014: 103-155

  • 2013
  • [c39]

    Giles Reger, Howard Barringer, David E. Rydeheard:
    A pattern-based approach to parametric specification mining.ASE2013: 658-663

  • 2012
  • [j22]

    Howard Barringer, Dov M. Gabbay, John Woods:
    Temporal, numerical and meta-level dynamics in argumentation networks.Argument & Computation3(2-3): 143-202 (2012)

  • [j21]

    Howard Barringer, Dov M. Gabbay, John Woods:
    Modal and temporal argumentation networks.Argument & Computation3(2-3): 203-227 (2012)

  • [c38]

    Howard Barringer, Yliès Falcone, Klaus Havelund, Giles Reger, David E. Rydeheard:
    Quantified Event Automata: Towards Expressive and Efficient Runtime Monitors.FM2012: 68-84

  • 2011
  • [c37]

    Manuela L. Bujorianu, Marius C. Bujorianu, Howard Barringer:
    Systems theory in an analytic setting.CDC-ECE2011: 2901-2906

  • [c36]

    Howard Barringer, Klaus Havelund:
    TraceContract: A Scala DSL for Trace Analysis.FM2011: 57-72

  • [c35]

    Howard Barringer, Klaus Havelund:
    Internal versus External DSLs for Trace Analysis - (Extended Abstract).RV2011: 1-3

  • 2010
  • [j20]

    Howard Barringer, Alex Groce, Klaus Havelund, Margaret H. Smith:
    Formal Analysis of Log Files.JACIC7(11): 365-390 (2010)

  • [j19]

    Howard Barringer, David E. Rydeheard, Klaus Havelund:
    Rule Systems for Run-time Monitoring: from Eagle to RuleR.J. Log. Comput.20(3): 675-706 (2010)

  • [c34]

  • Abstract

    I present a formalism that combines two methodologies: objective Bayesianism and Bayesian nets. According to objective Bayesianism, an agent’s degrees of belief (i) ought to satisfy the axioms of probability, (ii) ought to satisfy constraints imposed by background knowledge, and (iii) should otherwise be as non-committal as possible (i.e. have maximum entropy). Bayesian nets offer an efficient way of representing and updating probability functions. An objective Bayesian net is a Bayesian net representation of the maximum entropy probability function. I show how objective Bayesian nets can be constructed, updated and combined, and how they can deal with cases in which the agent’s background knowledge includes knowledge of qualitative influence relationships, e.g. causal influences. I then sketch a number of applications of the resulting formalism, showing how it can shed light on probability logic, causal modelling, logical reasoning, semantic reasoning, argumentation


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