An Answer Set Programming Framework for Reasoning about Agents' Beliefs and Truthfulness of Statements

The paper proposes a framework for capturing how an agent’s beliefs evolve over time in response to observations and for answering the question of whether statements made by a third party can be believed. The basic components of the framework are a formalism for reasoning about actions, changes, and observations and a formalism for default reasoning. The paper describes a concrete implementation that leverages answer set programming for determining the evolution of an agent’s “belief state”, based on observations, knowledge about the effects of actions, and a theory about how these influence an agent’s beliefs. The beliefs are then used to assess whether statements made by a third party can be accepted as truthful. The paper investigates an application of the proposed framework in the detection of man-in-themiddle attacks targeting computers and cyber-physical systems. Finally, we briefly discuss related work and possible extensions.

[1]  Neil C. Rowe.  Deception in Electronic Goods and Services , 2008 .

[2]  Jamal Bentahar,et al.  Computationally Grounded Quantitative Trust with Time , 2020, AAMAS.

[3]  Victor W. Marek,et al.  Stable models and an alternative logic programming paradigm , 1998, The Logic Programming Paradigm.

[4]  Enrico Pontelli,et al.  An Answer Set Programming Framework for Reasoning About Truthfulness of Statements by Agents , 2016, ICLP.

[5]  Zhuo Chen,et al.  Modeling and Reasoning in Event Calculus Using Goal-Directed Constraint Answer Set Programming , 2019, LOPSTR.

[6]  Martin Gebser,et al.  ASP-Core-2 Input Language Format , 2019, Theory and Practice of Logic Programming.

[7]  Marek J. Sergot,et al.  A logic-based calculus of events , 1989, New Generation Computing.

[8]  Jordi Sabater-Mir,et al.  Review on Computational Trust and Reputation Models , 2005, Artificial Intelligence Review.

[9]  Yolanda Gil,et al.  A survey of trust in computer science and the Semantic Web , 2007, J. Web Semant..

[10]  Nathan Griffiths,et al.  Addressing class imbalance in trust and stereotype assessment , 2019 .

[11]  Sheila A. McIlraith,et al.  Towards a formal account of diagnostic problem-solving , 1997 .

[12]  Enrico Pontelli,et al.  Reasoning about Truthfulness of Agents Using Answer Set Programming , 2015, KR.

[13]  Alexandru Nedelcu,et al.  An Approach and Tool for Reasoning about Situated Cyber-Physical Systems , 2015 .

[14]  Michael Gelfond,et al.  Logic Programs with Consistency-Restoring Rules , 2003 .

[15]  Maurice Bruynooghe,et al.  Temporal Reasoning with Abductive Event Calculus , 1992, ECAI.

[16]  Michalis Faloutsos,et al.  TrueView: Harnessing the Power of Multiple Review Sites , 2015, WWW.

[17]  Gerhard Brewka.  Preferences in Answer Set Programming , 2005, CAEPIA.

[18]  Michael Gelfond,et al.  Classical negation in logic programs and disjunctive databases , 1991, New Generation Computing.

[19]  Michael Gelfond,et al.  Diagnostic reasoning with A-Prolog , 2003, Theory Pract. Log. Program..

[20]  Hans Tompits,et al.  A framework for compiling preferences in logic programs , 2002, Theory and Practice of Logic Programming.

[21]  Elizabeth Sklar,et al.  Using argumentation to reason about trust and belief , 2012, J. Log. Comput..

[22]  Thomas Eiter,et al.  Preferred Answer Sets for Extended Logic Programs , 1999, Artif. Intell..

[23]  Alex M. Andrew,et al.  Knowledge in Action: Logical Foundations for Specifying and Implementing Dynamical Systems , 2002 .

[24]  Michael Gelfond,et al.  Action Languages , 1998, Electron. Trans. Artif. Intell..

[25]  Thomas Eiter,et al.  Prioritizing Default Logic , 2000, Intellectics and Computational Logic.

[26]  John McCarthy,et al.  SOME PHILOSOPHICAL PROBLEMS FROM THE STANDPOINT OF ARTI CIAL INTELLIGENCE , 1987 .

[27]  Torsten Schaub,et al.  asprin: Customizing Answer Set Preferences without a Headache , 2015, AAAI.