Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Argument Ranking with Categoriser Function

  • Conference paper
Knowledge Science, Engineering and Management (KSEM 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8793))

Abstract

Recently, ranking-based semantics is proposed to rank-order arguments from the most acceptable to the weakest one(s), which provides a graded assessment to arguments. In general, the ranking on arguments is derived from the strength values of the arguments. Categoriser function is a common approach that assigns a strength value to a tree of arguments. When it encounters an argument system with cycles, then the categoriser strength is the solution of the non-linear equations. However, there is no detail about the existence and uniqueness of the solution, and how to find the solution (if exists). In this paper, we will cope with these issues via fixed point technique. In addition, we define the categoriser-based ranking semantics in light of categoriser strength, and investigate some general properties of it. Finally, the semantics is shown to satisfy some of the axioms that a ranking-based semantics should satisfy.

This work is supported by the Funds NSFC61171121, NSFC60973049, and the Science Foundation of Chinese Ministry of Education-China Mobile 2012.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Rahwan, I., Simari, G.R.: Argumentation in artificial intelligence. Springer (2009)

    Google Scholar 

  2. Dung, P.M.: On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Journal of Artificial Intelligence 77(2), 321–357 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  3. Baroni, P., Giacomin, M., Guida, G.: SCC-recursiveness: A general schema for argumentation semantics. Artificial Intelligence 168(1), 162–210 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  4. Dung, P.M., Mancarella, P., Toni, F.: A dialectic procedure for sceptical, assumption-based argumentation. COMMA 144, 145–156 (2006)

    Google Scholar 

  5. Baroni, P., Cerutti, F., Giacomin, M., Simari, G.R.: Computational Models of Argument, vol. 216. Ios Press (2010)

    Google Scholar 

  6. Bench-Capon, T.J., Dunne, P.E.: Argumentation in artificial intelligence. Artificial Intelligence 171(10), 619–641 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  7. Amgoud, L., Ben-Naim, J.: Ranking-based semantics for argumentation frameworks. In: Liu, W., Subrahmanian, V.S., Wijsen, J. (eds.) SUM 2013. LNCS (LNAI), vol. 8078, pp. 134–147. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  8. Cayrol, C., Lagasquie-Schiex, M.C.: Graduality in argumentation. J. Artif. Intell. Res (JAIR) 23, 245–297 (2005)

    MathSciNet  MATH  Google Scholar 

  9. Besnard, P., Hunter, A.: A logic-based theory of deductive arguments. Artificial Intelligence 128(1), 203–235 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  10. Altman, A., Tennenholtz, M.: Axiomatic foundations for ranking systems. J. Artif. Intell. Res (JAIR) 31, 473–495 (2008)

    MathSciNet  MATH  Google Scholar 

  11. Burden, R.L., Faires, J.D.: Numerical analysis. Brooks/Cole, USA (2001)

    Google Scholar 

  12. Teschl, G.: Nonlinear functional analysis. Lecture notes in Math, Vienna Univ., Austria (2001)

    Google Scholar 

  13. Li, K., Liang, J., Xiao, T.J.: Positive fixed points for nonlinear operators. Computers & Mathematics with Applications 50(10), 1569–1578 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  14. Thimm, M., Kern-Isberner, G.: Stratified labelings for abstract argumentation. arXiv preprint arXiv:1308.0807 (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Pu, F., Luo, J., Zhang, Y., Luo, G. (2014). Argument Ranking with Categoriser Function. In: Buchmann, R., Kifor, C.V., Yu, J. (eds) Knowledge Science, Engineering and Management. KSEM 2014. Lecture Notes in Computer Science(), vol 8793. Springer, Cham. https://doi.org/10.1007/978-3-319-12096-6_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-12096-6_26

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12095-9

  • Online ISBN: 978-3-319-12096-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics