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Inconsistency in Multi-Agent Systems

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Foundations of Intelligent Systems

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 122))

Abstract

Multi-agent systems are distributed problem solving systems involving multiple collaborating intelligent agents that are capable of interacting with their environments. Toward the goal of developing multi-agent systems of bounded rationality, an important issue is how to manage and handle inconsistent or conflicting knowledge and information an agent may possess or has to reason with. The focus of this paper is on inconsistency in multi-agent systems. We describe the occurrence of inconsistency in the depth of knowledge, define nine different types of inconsistent phenomena, and discuss possible ways to utilize inconsistency as useful heuristics toward developing multi-agent systems of bounded rationality. The main contribution is that we shed some new light on inconsistency in multi-agent systems.

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References

  1. Al-Said, M.: Identifying, Analyzing, and Avoiding Software Model Clashes, Ph.D. Dissertation. University of Southern California (2003)

    Google Scholar 

  2. Antoniou, G., Billington, D., Governatori, G., Maher, M.: Representation results for defeasible logic. ACM Trans. On Computational Logic 2(2), 255–286 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  3. Bertossi, L., Hunter, A., Schaub, T.: Introduction to inconsistency tolerance. In: Bertossi, L., Hunter, A., Schaub, T. (eds.) Inconsistency Tolerance. LNCS, vol. 3300, pp. 1–14. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  4. Besnard, P., Hunter, A.: Elements of Argumentation. MIT Press (2008)

    Google Scholar 

  5. Boehm, B.: Requirements that handle IKIWISI, COTS, and rapid change. IEEE Computer 33(7), 99–102 (2000)

    Article  Google Scholar 

  6. Brachman, R.J., Levesque, H.J.: Knowledge Representation and Reasoning. Morgan Kaufmann, San Francisco (2004)

    Google Scholar 

  7. Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Computing Surveys 41(3) (2009)

    Google Scholar 

  8. Dung, P.M.: On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artificial Intelligence 77, 321–357 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  9. Evans, J.A., Foster, J.G.: Metaknowledge. Science 331, 721–725 (2011)

    Article  MathSciNet  Google Scholar 

  10. Fan, W., Geerts, F., Lakshmanan, L., Xiong, M.: Discovering conditional functional dependencies. In: Proc. of the International Conference on Data Engineering (ICDE 2009), pp. 1231–1234 (2009)

    Google Scholar 

  11. Farid, H.: Seeing is not believing. IEEE Spectrum 46(8), 44–51 (2009)

    Article  Google Scholar 

  12. Ferrucci, D., et al.: Building Watson: an overview of the DeepQA project. AI Magazine, 59–79 (Fall 2010)

    Google Scholar 

  13. Gotesky, R.: The uses of inconsistency. Philosophy and Phenomenological Research 28(4), 471–500 (1968)

    Article  Google Scholar 

  14. Martinez, M.V., Pugliese, A., Simari, G.I., Subrahmanian, V.S., Prade, H.: How Dirty Is Your Relational Database? An Axiomatic Approach. In: Mellouli, K. (ed.) ECSQARU 2007. LNCS (LNAI), vol. 4724, pp. 103–114. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  15. Mahdian, B., Saic, S.: Using noise inconsistencies for blind image forensics. Image and Vision Computing 27, 1497–1503 (2009)

    Article  Google Scholar 

  16. Neagoe, V., Bishop, M.: Inconsistency in deception for defense. In: Proc. of the 2006 Workshop on New Security Paradigms, Schloss Dagstuhl, German, September 19-22, pp. 31–38 (2006)

    Google Scholar 

  17. Popescu, A.C.: Statistical Tools for Digital Image Forensics, Ph.D. thesis, Department of Computer Science, Dartmouth College (December 2004)

    Google Scholar 

  18. Pylyshyn, Z.: Computing in cognitive science. In: Posner, M.I. (ed.) Foundations of Cognitive Science, pp. 49–92. MIT Press, Cambridge, MA (1989)

    Google Scholar 

  19. Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice-Hall (2010)

    Google Scholar 

  20. Simon, H.A.: Models of Bounded Rationality. MIT Press (1982)

    Google Scholar 

  21. Wang, X., Xuan, B., Peng, S.: Digital image forgery detection based on the consistency of defocus blur. In: Proc. of International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 192–195 (2008)

    Google Scholar 

  22. Ye, S., Sun, Q., Chang, E.C.: Detecting digital image forgeries by measuring inconsistencies of blocking artifacts. In: Proc. of the IEEE International Conference on Multimedia and Expo, pp. 12–15 (2007)

    Google Scholar 

  23. Yeh, P., Puri, C.: An efficient and robust approach for discovering data quality rules. In: Proc. of the 22nd IEEE International Conference on Tools with Artificial Intelligence, Arras, France, pp. 248–255 (2010)

    Google Scholar 

  24. Zhang, D.: Taming Inconsistency in value-based software development. In: Proceedings of 21st International Conference on Software Engineering and Knowledge Engineering, Boston, Mass., pp. 450–455 (July 2009)

    Google Scholar 

  25. Zhang, D.: Capturing antagonistic stakeholders value propositions in value-based software development. In: Proc. of 22nd International Conference on Software Engineering and Knowledge Engineering, San Francisco, pp. 12–17 (July 2010)

    Google Scholar 

  26. Zhang, D.: Toward a classification of antagonistic manifestations of knowledge. In: Proc. of the 22nd IEEE International Conference on Tools with Artificial Intelligence, Arras, France, pp. 375–382 (October 2010)

    Google Scholar 

  27. Zhang, D.: Inconsistency-induced heuristics for problem solving. In: Proc. of the 23rd International Conference on Software Engineering and Knowledge Engineering, Miami, FL, pp. 137–142 (July 2011)

    Google Scholar 

  28. Zhang, D., Lu, M.: Inconsistency-induced learning: a step toward perpetual learners. In: Proc. of 10th IEEE International Conference on Cognitive Informatics, Banff, Canada, pp. 59–66 (August 2011)

    Google Scholar 

  29. Zhang, D., Grégoire, É.: The landscape of inconsistency: a perspective. To appear in International Journal of Semantic Computing

    Google Scholar 

  30. Zhang, D.: The utility of inconsistencies in information security and digital forensics. In: Özyer, T., et al. (eds.) Recent Trends in Information Reuse and Integration, pp. 381–397. Springer, Heidelberg (2011)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, California State Univeristy, Sacramento, CA, 95819-6021, USA

    Du Zhang

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  1. Du Zhang
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, 200240, Shanghai, China

    Yinglin Wang

  2. School of Information Science and Technology, Southwest Jiaotong University, 610031, Chengdu, Sichuan Province, China

    Tianrui Li

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© 2011 Springer-Verlag Berlin Heidelberg

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Zhang, D. (2011). Inconsistency in Multi-Agent Systems. In: Wang, Y., Li, T. (eds) Foundations of Intelligent Systems. Advances in Intelligent and Soft Computing, vol 122. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25664-6_46

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  • DOI: https://doi.org/10.1007/978-3-642-25664-6_46

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25663-9

  • Online ISBN: 978-3-642-25664-6

  • eBook Packages: EngineeringEngineering (R0)

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