IJCAI 2016 and ECAI 2016 Tutorial

Argument and Cognition

Antonis Kakas
Department of Computer Science
University of Cyprus
antonis@ucy.ac.cy
        
Loizos Michael
Computational Cognition Lab
Open University of Cyprus
loizos@ouc.ac.cy

 

The ubiquitous use of smart devices to assist human users in their everyday tasks has given a renewed impetus to that area of Artificial Intelligence that aims to understand and automate ordinary or common sense, natural intelligence. This requires systems, called cognitive systems, that can exhibit a behavior that is cognitively compatible with that of human users and that can, therefore, provide personalized solutions by evolving and learning how to adapt to their human users. One way to achieve this requirement of cognitive compatibility is for these systems to compute or reason in a way that is similar to that of human (common sense) reasoning.

Adopting the premise that the logical nature of human reasoning is foundationally different from that of classical logic and closer to the dialectical nature of argumentation — a position that has been advocated by Cognitive Psychology for many years and newly supported by recent studies — this tutorial will aim to show how argumentation can form the underlying theoretical and practical basis for building cognitive systems. Concentrating on the central cognitive task of comprehension, the tutorial will cover material from argumentation theory in AI and models of cognition in Psychology, whose synthesis can support computational cognition with common sense knowledge and can lead to a paradigm of Cognitive Programming. The tutorial will provide hands-on experience with prototype general systems (built within the above argumentation perspective to cognition) that can be used to develop cognitive system applications.

Download the slides for the IJCAI 2016 Tutorial.
(tutorial includes slides with selected bibliography)

Download the slides for the ECAI 2016 Tutorial.
(extended bibliography for tutorial at end of page)

Position papers on Argument and Cognition:

 • A. Kakas, and L. Michael. "Cognitive Systems: Argument and Cognition". IEEE Intelligent Informatics Bulletin, 17(1):14-20, 2016.

 L. Michael, A. Kakas, R. Miller, and G. Turan. "Cognitive Programming". In Proceedings of the 3rd International Workshop on Artificial Intelligence and Cognition (AIC), pages 3-18, 2015.

 •
A. Kakas, L. Michael, and F. Toni. "Argumentation: Reconciling Human and Automated Reasoning". In Proceedings of the 2nd Workshop on Bridging the Gap between Human and Automated Reasoning, 2016.


Presenters' Short Bio:

Antonis Kakas has been a member of faculty at the Department of Computer Science of the University of Cyprus since 1992. He has been working on Argumentation in AI since then, with numerous publications covering both theoretical and practical problems of argumentation. This includes work on cognitive agents whose design uses an argumentation-based agent architecture with cognitively inspired behaviors. He has developed the argumentation system Gorgias which has been used by various groups in real-life applications of argumentation. Over the last five years he has been working, together with other researches including colleagues from Cognitive Psychology, on narrative text comprehension through argumentation. This approach stems from a synthesis of argumentation theory in AI with models of comprehension from Cognitive Psychology.

Loizos Michael is an Assistant Professor at Open University of Cyprus, where he founded and directs the Computational Cognition Lab. He was educated at University of Cyprus, where he received a B.Sc. in Computer Science with a minor degree in Mathematics. He continued his education at Harvard University, where he received an M.Sc. and a Ph.D. in Computer Science. His research focuses on the principled study of cognitive processes associated with individual or collective intelligence — such as learning, reasoning, sensing, communication, cooperation — and how those are used by humans and other organisms in everyday life. Emphasis is placed on the development of computational models for various aspects of cognitive processes, and the analysis of the formal implications that such models have. This computational view of cognition is complemented by simulations, real‐world experiments, and psychological studies, designed to validate the proposed models and to identify features thereof that warrant further study.



ECAI 2016 Tutorial Bibliography:

on Cognitive Computing / Systems
 • AAAI Special Track on Cognitive Systems, 2015 and 2016.
 • IBM Watson / Cognitive Computing. D. Ferrucci, et al., “Building Watson: An overview of the DeepQAProject”, AI Magazine, Fall 2010. http://www.research.ibm.com/cognitive-computing/
 • Cognitive Computing, Cognitive Systems and Other applications Knowledge Repository. http://www.aee.odu.edu/cognitivecomp/index.php
 • J. McCarthy, “From Here to Human-Level AI”, Artificial Intelligence, 171:1174-1182, 2007.
 • P. Langley, “The Cognitive Systems Paradigm”, Advances in Cognitive Systems, 1:3-13, 2012.
 • P. Langley, J. E. Laird, and S. Rogers. "Cognitive Architectures: Research Issues and Challenges". Cognitive Systems Research, 10(2):141-160, 2009.
 • A. Lieto, A. Minieri, A. Piana, and D. P. Radicioni. "A Knowledge-Based System for Prototypical Reasoning". Connection Science, 27(2):137-152, 2015.
 • A. Lieto and D. P. Radicioni. "From Human to Artificial Cognition and Back: New Perspectives on Cognitively Inspired AI Systems". Cognitive Systems Research, Elsevier, 2016.
 • L. Michael, A. Kakas, R. Miller, and G. Turan. "Cognitive Programming". In Proceedings of the 3rd International Workshop on Artificial Intelligence and Cognition (AIC), pages 3-18, 2015.
 • J. E. Laird. The Soar Cognitive Architecture. The MIT Press, 2012.
 • R. Kowalski. Computational Logic and Human Thinking: How to Be Artificially Intelligent. Cambridge University Press, New York, NY, USA, 2011.
 • J. R. Anderson, B. E. John, M. A. Just, P. A. Carpenter, D. E. Kieras, and D. E. Meyer. Production System Models of Complex Cognition. In Proceedings of the 17th Annual Conference of the Cognitive Science Society, pages 9-12, 1995.
 • D. Vernon, G. Metta, and G. Sandini. "A Survey of Artificial Cognitive Systems: Implications for the Autonomous Development of Mental Capabilities in Computational Agents". Transactions on Evolutionary Computation, 11(2):151-180, 2007.

on Psychology of Reasoning
 • J. S. Evans. "Logic and Human Reasoning: An Assessment of the Deduction Paradigm". Psychological Bulletin, 128(6):978-96, 2002.
 • P. N. Johnson-Laird. Mental Models. Cambridge University Press, 1983.
 • P. N. Johnson-Laird, S.S. Khemlani, and G.P. Goodwin. "Logic, Probability and Human Reasoning". Trends in Cognitive Sciences, 19(4):201-212, 2015.
 • P. N. Johnson-Laird and M. Steedman. "The Psychology of Syllogisms". Cognitive Psychology, 10:64-99, 1978.
 • H. Mercier. "The Argumentative Theory: Predictions and Empirical Evidence". Trends in Cognitive Sciences, to appear, 2016.
 • H. Mercier and D. Sperber. "Why do Humans Reason? Arguments for an Argumentative Theory". Behavioral and Brain Sciences, 34(2):57-74, 2011.
 • J. Hornikx and U. Hahn. "Reasoning and Argumentation: Towards an Integrated Psychology of Argumentation". Thinking & Reasoning, 18(3):225-243, 2012.
 • L. J. Rip. The Psychology of Proof: Deductive Reasoning in Human Thinking. Cambridge MA: The MIT Press, 1994.
 • K. Stenning and M. van Lambalgen. Human Reasoning and Cognitive Science. The MIT Press, 2008.
 • K. Stenning and M. van Lambalgen. "Reasoning, Logic, and Psychology". WIREs Cognitive Science, 2(5):555-567, 2010.
 • I.-A. Diakidoy, S. A. Christodoulou, G. Floros, K. Ioardanou and P. V. Kargopoulos. "Forming a Belief: The Contribution of Comprehension to the Evaluation and Persuasive Impact of Argumentative Text. British Journal of Educational Psychology, l 85:300-315, 2015.
 • P. Cheng and K. Holyoak. "Pragmatic Reasoning Schemas". Cognitive Psychology, 17:391-416, 1985.
 • J. L. Pollock. Cognitive Carpentry: A Blueprint for How to Build a Person. The MIT Press, 1995.
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on Computational Argumentation
 • A. Bondarenko, F. Toni, and R. Kowalski. "An Assumption-based Framework for Non-monotonic Reasoning". In Proceedings of the 2nd International Workshop on Logic Programming and Non-monotonic Reasoning (LPNMR), pages 171-189, 1993.
 • T. J. M. Bench-Capon and P. E. Dunne. "Argumentation in Artificial Intelligence". Artificial Intelligence, 171(10-15):619-641, 2007.
 • P. Besnard and A. Hunter. Elements of Argumentation. The MIT Press, 2008.
 • P. M. Dung. "On the Acceptability of Arguments and its Fundamental Role in Nonmonotonic Reasoning, Logic Programming and n-person Games". Artificial Intelligence, 77:321-357, 1995.
 • P. M. Dung and P. Mancarella. "Production Systems with Negation as Failure". IEEE Transactions on Knowledge and Data Engineering, 14(2):336-352, 2002.
 • A. Hunter. "Modelling the Persuadee in Asymmetric Argumentation Dialogues for Persuasion". In Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI), pages 3055-3061, 2015.
 • I.-A. Diakidoy, A. Kakas, L. Michael, and R. Miller. "Story Comprehension through Argumentation". In Proceedings of the 5th International Conference on Computational Models of Argument (COMMA), pages 31-42, 2014.
 • I.-A. Diakidoy, A. Kakas, L. Michael, and R. Miller. "STAR: A System of Argumentation for Story Comprehension and Beyond". In Proceedings of the 12th International Symposium on Logical Formalizations of Commonsense Reasoning, pages 64-70, 2015.
 • H. Prakken and G. Sartor. “Argument-Based Extended Logic Programming with Defeasible Priorities”. Journal of Applied Non-Classical Logics 7(1):25-75, 1997.
 • S. Modgil and H. Prakken. “The ASPIC+ Framework for Structured Argumentation: A Tutorial”. Argument & Computation 5(1):31-62, 2014.
 • D. Nute. “Defeasible Logic”. In Handbook of Logic in Artificial Intelligence and Logic Programming, Volume 3: Nonmonotonic Reasoning and Uncertain Reasoning, p.353-395. Clarendon Press, 1994.
 • T. J. M. Bench-Capon. “Value-Based Argumentation Frameworks”. In Proceedings of the 9th International Workshop on Non-Monotonic Reasoning (NMR), pages 443-454, 2002.
 • A. J. Garcıa and L. Simari, “Defeasible Logic Programming: An Argumentative Approach”. Journal Theory and Practice of Logic Programming, 4(2):95-138, 2004.
 • A. Kakas, P. Mancarella. “On the Semantics of Abstract Argumentation”. Journal of Logic and Computation, 23(5):991-1015, 2013.
 • A. Kakas, P. Mancarella, P.M. Dung. “The Acceptability Semantics for Logic Programs”. In Proceedings of the 11th International Conference on Logic Programming (ICLP), pages 504-519, 1994.
 • A. Kakas, F. Toni. “Computing Argumentation in Logic Programming”. Journal of Logic and Computation, 9:515-562, 1999.
 • A. Kakas, F. Toni, and P. Mancarella. “Argumentation Logic”. In Proceedings of the 5th International Conference on Computational Models of Argument (COMMA), pages 12-27, 2014.

on Decision Making in Argumentation
 • N. I. Spanoudakis, A. Kakas, and P. Moraitis. “Applications of Argumentation: The SoDA Methodology”. In Proceedings of the 22nd European Conference on Artificial Intelligence (ECAI), 2016.
 • L. Amgoud and H. Prade. "Using Arguments for Making and Explaining Decisions". Artificial Intelligence, 173(3):413-436, 2009.
 • N. I. Karacapilidis and D. Papadias. "Computer Supported Argumentation and Collaborative Decision Making: The HERMES System". Information Systems 26(4):259-277, 2001.
 • T. J. M. Bench-Capon. "Persuasion in Practical Argument using Value-Based Argumentation Frameworks". Journal of Logic and Computation, 13(3):429-448, 2003.
 • W. Ouerdane, N. Maudet, and A. Tsoukias. "Argument Schemes and Critical Questions for Decision Aiding Process". In Proceedings of the 2nd International Conference on Computational Models of Argument (COMMA), pages 285-296, 2008.
 • J. Fox, D. Glasspool, D. Grecu, S. Modgil, M. South, and Vivek Patkar. "Argumentation-Based Inference and Decision Making — A Medical Perspective". IEEE Intelligent Systems, 22(6):34-41, 2007.
 • A. Hunter and M.-A. Williams. "Aggregation of Clinical Evidence using Argumentation: A Tutorial Introduction". In: Foundations of Biomedical Knowledge Representation: Methods and Applications, pages 317–337, 2015.
 • A. C. Kakas and P. Moraitis. "Argumentation-Based Decision Making for Autonomous Agents". In Proceedings of the 2nd International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), pages 883-890, 2003.
 • S. Kraus, K. Sycara, and A. Evenchik. "Reaching Agreements through Argumentation: A Logical Model and Implementation". Artificial Intelligence, 104(1):1–69, 1998.
 • A. Rosenfeld and S. Kraus. "Strategical Argumentative Agent for Human Persuasion". In Proceedings of the 22nd European Conference on Artificial Intelligence (ECAI), 2016.

on Human Comprehension
 • J. E. Albrecht and E. J. O'Brien. "Updating a Mental Model: Maintaining Both Local and Global Coherence". Journal of Experimental Psychology: Learning, Memory, and Cognition, 19(5):1061-1070, 1993.
 • S. L. Frank, M. Koppen, L. G. M. Noordman, and W. Vonk. "Computational Models of Discourse Comprehension". Discourse Processes, 45(6):429-463, 2008.
 • A. C. Graesser, K. K. Millis, and R. A. Zwaan. "Discourse Comprehension". Annual Review of Psychology, 48:163-189, 1997.
 • W. Kintsch. "The Role of Knowledge in Discourse Comprehension: A Construction-Integration Model". Psychological Review, 95:163-182, 1988.
 • W. Kintsch. Comprehension: A Paradigm of Cognition. Cambridge University Press, 1998.
 • W. Kintsch and P. Mangalath. "The Construction of Meaning". Topics in Cognitive Science, 3:346-370, 2011.
 • D. S. McNamara and J. Magliano. "Toward a Comprehensive Model of Comprehension". The Psychology of Learning and Motivation, 51:297-384, 2009.
 • D. N. Rapp and P. Van den Broek. "Dynamic Text Comprehension: An Integrative View of Reading". Current Directions in Psychological Science, 14:297-384, 2005.
 • A. J. Sanford and S. C. Garrod. "The Role of Scenario Mapping in Text Comprehension". Discourse Processes, 26(2-3):159-190, 1998.
 • P. Thagard. Coherence in Thought and Action. The MIT Press, 2002.
 • P. Van den Broek. "Comprehension and Memory of Narrative Texts: Inferences and Coherence". In M. A. Gernsbacher, editor, Handbook of Psycholinguistics, Academic Press, pages 539-588, 1994.
 • R. M. Young. "Production Systems in Cognitive Psychology". In N. J. Smelser and P. B. Baltes, editors, International Encyclopedia of the Social & Behavioral Sciences, Elsevier, 2001.
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on Knowledge Acquisition
  • Workshops on Cognitive Knowledge Acquisition and Applications (Cognitum), 2015 and 2016.
 • L. Michael. "Cognitive Reasoning and Learning Mechanisms". In Proceedings of the 4th International Workshop on Artificial Intelligence and Cognition (AIC), 2016.
 • L. Michael. Causal Learnability. In Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI), pages 1014-1020, 2011.
 • L. Michael. Machines with WebSense. In Proceedings of the 11th International Symposium on Logical Formalizations of Commonsense Reasoning, 2013.
 • L. Michael. Simultaneous Learning and Prediction. In Proceedings of the 14th International Conference on Principles of Knowledge Representation and Reasoning (KR), pages 348-357, 2014.
 • L. Michael. Introspective Forecasting. In Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI), pages 3714-3720, 2015.
 • T. Mitchell et al. "Never-Ending Learning". In Proceedings of the 29th AAAI Conference on Artificial Intelligence, pages 2302-2310, 2015.
 • L. G. Valiant. "A Theory of the Learnable". Communications of the ACM, 27(11):1134-1142, 1984.
 • L. G. Valiant. Robust Logics. Artificial Intelligence, 117(2):231-253, 2000.
 • Y. Dimopoulos, L. Michael, and F. Athienitou. “Ceteris Paribus Preference Elicitation with Predictive Guarantees”. In Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI), pages 1890-1895, 2009.
 • L. Michael and E. Papageorgiou. “An Empirical Investigation of Ceteris Paribus Learnability”. In Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI), pages 1537-1543, 2013.
 • L. Michael, L.G. Valiant. “A First Experimental Demonstration of Massive Knowledge Infusion”. In Proceedings of the 11th International Conference on Principles of Knowledge Representation and Reasoning (KR), pages 378-389, 2008.

on Argument Mining
 • Workshops on Argument Mining, 2014, 2015, and 2016.
 • A. Peldszus and M. Stede. "From Argument Diagrams to Argumentation Mining in Texts: A Survey". International Journal of Cognitive Informatics and Natural Intelligence, 7(1):1-31, 2013.
 • F. Cerutti, N. Tintarev, and N. Oren, "Formal Arguments, Preferences, and Natural Language Interfaces to Humans: An Empirical Evaluation". In Proceedings of the 21st European Conference on Artificial Intelligence (ECAI), pages 207-212, 2014.
 • F. Toni and P. Torroni. "Bottom-Up Argumentation". In Proceedings of the 1st International Workshop on the Theory and Applications of Formal Argumentation (TAFA), LNAI 7132, pages 249-262, 2012.