A Computationally Grounded, Weighted Doxastic Logic

Modelling, reasoning and verifying complex situations involving a system of agents is crucial in all phases of the development of a number of safety-critical systems. In particular, it is of fundamental importance to have tools and techniques to reason about the doxastic and epistemic states of agents, to make sure that the agents behave as intended. In this paper we introduce a computationally grounded logic called COGWED and we present two types of semantics that support a range of practical situations. We provide model checking algorithms, complexity characterisations and a prototype implementation. We validate our proposal against a case study from the avionic domain: we assess and verify the situational awareness of pilots flying an aircraft with several automated components in off-nominal conditions.

[1]  Taolue Chen,et al.  PRISM-games: A Model Checker for Stochastic Multi-Player Games , 2013, TACAS.

[2]  Joseph Y. Halpern.  Reasoning about uncertainty , 2003 .

[3]  Alessio Lomuscio,et al.  MCMAS: A Model Checker for the Verification of Multi-Agent Systems , 2009, CAV.

[4]  Mehdi Dastani,et al.  Reasoning about Normative Update , 2013, IJCAI.

[5]  Joseph Y. Halpern,et al.  From Statistical Knowledge Bases to Degrees of Belief , 1996, Artif. Intell..

[6]  Taolue Chen,et al.  Automatic verification of competitive stochastic systems , 2012, Formal Methods in System Design.

[7]  David Chaum,et al.  The dining cryptographers problem: Unconditional sender and recipient untraceability , 1988, Journal of Cryptology.

[8]  Taolue Chen,et al.  Automatic Verification of Competitive Stochastic Systems , 2012, TACAS.

[9]  Ron van der Meyden,et al.  MCK: Model Checking the Logic of Knowledge , 2004, CAV.

[10]  Rafael H. Bordini,et al.  Model checking agent programming languages , 2012, Automated Software Engineering.

[11]  J. Gillis,et al.  Matrix Iterative Analysis , 1961 .

[12]  Franco Raimondi,et al.  A synergistic and extensible framework for multi-agent system verification , 2013, AAMAS.

[13]  Matthew Richardson,et al.  Markov logic networks , 2006, Machine Learning.

[14]  Michael Fisher,et al.  Specifying and reasoning about uncertain agents , 2008, Int. J. Approx. Reason..

[15]  Joseph Y. Halpern,et al.  Plausibility measures and default reasoning , 1996, JACM.

[16]  Marta Z. Kwiatkowska,et al.  PRISM 4.0: Verification of Probabilistic Real-Time Systems , 2011, CAV.

[17]  Michael Wooldridge,et al.  Programming Multi-Agent Systems in AgentSpeak using Jason (Wiley Series in Agent Technology) , 2007 .

[18]  Giuseppe Primiero,et al.  Model checking degrees of belief in a system of agents , 2014, AAMAS.

[19]  Edmund M. Clarke,et al.  Model Checking , 1999, Handbook of Automated Reasoning.

[20]  Ronald Fagin,et al.  Reasoning about knowledge , 1995 .

[21]  H. French,et al.  Psycho-physiological Measures of Situation Awareness. , 2007 .

[22]  Michael Fisher,et al.  Generating Certification Evidence for Autonomous Unmanned Aircraft Using Model Checking and Simulation , 2014, J. Aerosp. Inf. Syst..

[23]  Michael Wooldridge,et al.  An Introduction to MultiAgent Systems John Wiley & Sons , 2002 .

[24]  Cheng Luo,et al.  A logic of probabilistic knowledge and strategy , 2013, AAMAS.

[25]  Barbara Messing,et al.  An Introduction to MultiAgent Systems , 2002, Künstliche Intell..

[26]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[27]  Michael Wooldridge,et al.  Computationally grounded theories of agency , 2000, Proceedings Fourth International Conference on MultiAgent Systems.