Probabilistic abstract argumentation frameworks, a possible world view

Abstract After Dung's founding work in Abstract Argumentation Frameworks there has been a growing interest in extending the Dung's semantics in order to describe more complex or real life situations. Several of these approaches take the direction of weighted or probabilistic extensions. One of the most prominent probabilistic approaches is that of constellation Probabilistic Abstract Argumentation Frameworks. In this paper, we first make the connection of possible worlds and constellation semantics; we then introduce the probabilistic attack normal form for the constellation semantics; we furthermore prove that the probabilistic attack normal form is sufficient to represent any Probabilistic Abstract Argumentation Framework of the constellation semantics; then we illustrate its connection with Probabilistic Logic Programming and briefly present an existing implementation. The paper continues by also discussing the probabilistic argument normal form for the constellation semantics and proves its equivalent properties. Finally, this paper introduces a new probabilistic structure for the constellation semantics, namely probabilistic cliques.

[1]  Henry Prakken,et al.  A general account of argumentation with preferences , 2013, Artif. Intell..

[2]  Luc De Raedt,et al.  Inference and learning in probabilistic logic programs using weighted Boolean formulas , 2013, Theory and Practice of Logic Programming.

[3]  Sergio Flesca,et al.  On the Complexity of Probabilistic Abstract Argumentation , 2013, IJCAI.

[4]  Nir Oren,et al.  Semantics for Evidence-Based Argumentation , 2008, COMMA.

[5]  Huaxin Huang,et al.  Formulating Semantics of Probabilistic Argumentation by Characterizing Subgraphs: Theory and Empirical Results , 2018, J. Log. Comput..

[6]  Pierpaolo Dondio TOWARD A COMPUTATIONAL ANALYSIS OF PROBABILISTIC ARGUMENTATION FRAMEWORKS , 2014, Cybern. Syst..

[7]  Taisuke Sato,et al.  A Statistical Learning Method for Logic Programs with Distribution Semantics , 1995, ICLP.

[8]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[9]  Anthony Hunter,et al.  A probabilistic approach to modelling uncertain logical arguments , 2013, Int. J. Approx. Reason..

[10]  Stefano Bistarelli,et al.  ConArg: A Constraint-Based Computational Framework for Argumentation Systems , 2011, 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence.

[11]  Paul E. Dunne,et al.  Semi-stable semantics , 2006, J. Log. Comput..

[12]  Pietro Baroni,et al.  An introduction to argumentation semantics , 2011, The Knowledge Engineering Review.

[13]  Anthony Hunter,et al.  Probabilistic Reasoning with Abstract Argumentation Frameworks , 2017, J. Artif. Intell. Res..

[14]  Anthony Hunter Some Foundations for Probabilistic Abstract Argumentation , 2012, COMMA.

[15]  Stefano Bistarelli,et al.  Probabilistic Argumentation Frameworks with MetaProbLog and ConArg , 2018, 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI).

[16]  Bernd Gutmann,et al.  On Continuous Distributions and Parameter Estimation in Probabilistic Logic Programs (Over continue verdelingen en het schatten van parameters in probabilistische logische programma's) , 2011 .

[17]  Stefano Bistarelli,et al.  A novel weighted defence and its relaxation in abstract argumentation , 2018, Int. J. Approx. Reason..

[18]  Mikolaj Podlaszewski,et al.  A Labelling-Based Justification Status of Arguments , 2010, NMR 2010.

[19]  Ricardo Rocha,et al.  Using Iterative Deepening for Probabilistic Logic Inference , 2017, PADL.

[20]  Phan Minh Dung,et al.  An Argumentation-Theoretic Foundations for Logic Programming , 1995, J. Log. Program..

[21]  Francesca Toni,et al.  An Assumption-Based Framework for Non-Monotonic Reasoning , 1993, LPNMR.

[22]  Pietro Baroni,et al.  AFRA: Argumentation framework with recursive attacks , 2011, Int. J. Approx. Reason..

[23]  Gerda Janssens,et al.  Dedicated Tabling for a Probabilistic Setting , 2010, ICLP.

[24]  Gerda Janssens,et al.  Implementation and Performance of Probabilistic Inference Pipelines , 2015, PADL.

[25]  Fabrizio Riguzzi,et al.  The PITA system: Tabling and answer subsumption for reasoning under uncertainty , 2011, Theory Pract. Log. Program..

[26]  Hannes Strass,et al.  Abstract Dialectical Frameworks. An Overview. , 2017 .

[27]  Nils J. Nilsson,et al.  Probabilistic Logic * , 2022 .

[28]  Anthony Hunter,et al.  Optimization of dialectical outcomes in dialogical argumentation , 2016, Int. J. Approx. Reason..

[29]  Luc De Raedt,et al.  Probabilistic (logic) programming concepts , 2015, Machine Learning.

[30]  Fabrizio Riguzzi,et al.  Approximate Inference for Logic Programs with Annotated Disjunctions , 2010, ILP.

[31]  Claudette Cayrol,et al.  On bipolarity in argumentation frameworks , 2008, NMR.

[32]  Maurice Bruynooghe,et al.  CP-logic: A language of causal probabilistic events and its relation to logic programming , 2009, Theory and Practice of Logic Programming.

[33]  Trevor J. M. Bench-Capon Persuasion in Practical Argument Using Value-based Argumentation Frameworks , 2003, J. Log. Comput..

[34]  Luc De Raedt,et al.  Anytime Inference in Probabilistic Logic Programs with Tp-Compilation , 2015, IJCAI.

[35]  Stefan Woltran,et al.  Probabilistic Argumentation Frameworks - A Logical Approach , 2014, SUM.

[36]  Nir Oren,et al.  Probabilistic Argumentation Frameworks , 2011, TAFA.

[37]  Gerda Janssens,et al.  Nesting Probabilistic Inference , 2011, ArXiv.

[38]  David Poole,et al.  The Independent Choice Logic and Beyond , 2008, Probabilistic Inductive Logic Programming.

[39]  Stefano Bistarelli,et al.  Coalitions of Arguments: An Approach with Constraint Programming , 2013, Fundam. Informaticae.

[40]  Stefan Woltran,et al.  cf2 Semantics Revisited , 2010, COMMA.

[41]  Bernd Gutmann,et al.  Trading memory for answers: Towards tabling ProbLog , 2009 .

[42]  Stefan Woltran,et al.  Characterizing Strong Equivalence for Argumentation Frameworks , 2010, KR.

[43]  Luc De Raedt,et al.  On the implementation of the probabilistic logic programming language ProbLog , 2010, Theory and Practice of Logic Programming.

[44]  Filippo Furfaro,et al.  Complexity of Fundamental Problems in Probabilistic Abstract Argumentation: Beyond Independence (Extended Abstract) , 2019, IJCAI.

[45]  Dov M. Gabbay,et al.  Fibring Argumentation Frames , 2009, Stud Logica.

[46]  Matthias Thimm,et al.  A Probabilistic Semantics for abstract Argumentation , 2012, ECAI.