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Implementation of 3-Valued Paraconsistent Logic Programming Towards Decision Making System of Agents

  • Yuki Goto
  • Megumi Fujita
  • Naoyuki Nide
Article
  • 39 Downloads

Abstract

Due to the rapid development of applications of artificial intelligence and robotics in recent years, the necessity of reasoning and decision making with uncertain and inaccurate information is increasing. Since robots in the real world are always exposed to behavioral inaccuracies and uncertainty arising from recognition methods, they may occasionally encounter contradictory facts during reasoning on action decision.

Paraconsistent logic programming is promising to make appropriate action decisions even when an agent is exposed to such uncertain information or contradictory facts, but there has been no implementation of this programming to the best of our knowledge. We propose a resolution algorithm for the 3-valued paraconsistent logic programming system QMPT0 and its implementation on SWI-Prolog. We also describe an application of the 3-valued paraconsistent logic programming regarding agent decision making.

Keywords

Agent-based modelling for complex systems paraconsistent logic programming solver implementation declarative programming application for agents 

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References

  1. [1]
    Angelotti, E. S., Scalabrin, E. E. & Ávila, B. C. (2001). PANDORA: a multi-agent system using paraconsistent logic, Proc. of ICCIMA 2001, pp. 352–356.Google Scholar
  2. [2]
    Beirlaen, M. & Straßer, C. (2011). A paraconsistent multi-agent framework for dealing with normative conflicts, of CLIMA XII, pp. 312–329.MATHGoogle Scholar
  3. [3]
    Blair, H. A. & Subrahmanian, V.S. (1989). Paraconsistent logic programming, Theoretical Computer Science 68(2): 135–154.MathSciNetCrossRefMATHGoogle Scholar
  4. [4]
    Bordini, R. H., Hübner, J. F. & Wooldridge, M. (2007). Programming Multi-Agent Systems in AgentSpeak Using Jason, John Wiley & Sons.CrossRefMATHGoogle Scholar
  5. [5]
    Coniglio, M. E. & Oliveira, K. E. (2016). On 3-valued paraconsistent logic programming, Syntax Meets Semantics (SYSMICS) 2016.Google Scholar
  6. [6]
    Coniglio, M. E. & Silvestrini, L. H. D. C. (2013). An alternative approach for quasi-truth. Logic Journal of IGPL, 22(2): 387–410.MathSciNetCrossRefMATHGoogle Scholar
  7. [7]
    Da Costa, N. C., & Bueno, O. (1999). Quasi-truth, supervaluations and free logic. History and Philosophy of Logic, 20(3–4): 215–226.MathSciNetCrossRefMATHGoogle Scholar
  8. [8]
    de Amo, S. & Pais, M. S. (2007). A paraconsistent logic programming approach for querying inconsistent databases, International Journal of Approximate Reasoning 46(2): 366–386.MathSciNetCrossRefMATHGoogle Scholar
  9. [9]
    Dries, A., Kimmig, A., Meert, W., Renkens, J., den Broeck, G. V., Vlasselaer, J. & Raedt, L. D. (2015). Problog2: Probabilistic logic programming, Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2015, LNCS, Vol.9286, 312–315.Google Scholar
  10. [10]
    Fujita, M., Goto, Y., Nide, N., Satoh, K. & Hosobe, H. (2013). An architecture for autonomously controlling robot with embodiment in real world. Proc. of Knowledge Representation and Reasoning in Robotics (workshop at ICLP 2013), 59–71.Google Scholar
  11. [11]
    Fujita, M., Goto, Y., Nide, N., Satoh, K. & Hosobe, H. (2014). Logic-based and robust decision making for robots in real world. Proc. of AAMAS’ 14, 1685–1686.Google Scholar
  12. [12]
    Fujita, M., Goto, Y., Nide, N., Satoh, K. & Hosobe, H. (2016). Autonomous control of mobile robots using logical representation of map and inference of location. Proc. of IEEE ICA 2016, 78–81.Google Scholar
  13. [13]
    Mares, E. D. (1997). Paraconsistent probability theory and paraconsistent Bayesianism. Logique et Analyse 40(160): 375–384.MathSciNetMATHGoogle Scholar
  14. [14]
    Rao, A. S. & Georgeff, M. P. (1997). Modeling rational agents within a BDI-architecture. in M. N. Huhns & M. P. Singh (eds), Readings in Agents, Morgan Kaufmann, 317–328.Google Scholar
  15. [15]
    Su, C.P. (2014). Paraconsistent justification logic: a starting point. Advances in Modal Logic 10: 513–532.MathSciNetMATHGoogle Scholar
  16. [16]
    Vojtáš, P. (2001). Fuzzy logic programming. Fuzzy Sets and Systems, 124(3): 361–370.MathSciNetCrossRefMATHGoogle Scholar
  17. [17]
    Zhang, X., Zhang, Z. & Lin, Z. (2009). An argumentative semantics for paraconsistent reasoning in description logic. Proc. of 22nd International Workshop on Description Logics.Google Scholar

Copyright information

© Systems Engineering Society of China and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Graduate School of System InformaticsKobe University (Until Mar. 2018)KobeJapan
  2. 2.Faculty of Science and EngineeringKindai UniversityHigashi-osakaJapan
  3. 3.Faculty, Division of Human Life and Environmental SciencesNara Women’s UniversityNaraJapan

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