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International Conference on Innovative Techniques and Applications of Artificial Intelligence

SGAI 2012: Research and Development in Intelligent Systems XXIX pp 165–178Cite as

Multi-Agent Knowledge Allocation

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Abstract

Classical query answering either assumes the existence of just one knowledge requester, or knowledge requests from distinct parties are treated independently. Yet, this assumption is inappropriate in practical applications where requesters are in direct competition for knowledge. We provide a formal model for such scenarios by proposing the Multi-Agent Knowledge Allocation (MAKA) setting which combines the fields of query answering in information systems and multi-agent resource allocation.We define a bidding language based on exclusivityannotated conjunctive queries and succinctly translate the allocation problem into a graph structure which allows for employing network-flow-based constraint solving techniques for optimal allocation.

Keywords

  • Allocation Problem
  • Optimal Allocation
  • Valuation Function
  • Combinatorial Auction
  • Conjunctive Query

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/978-1-4471-4739-8_12
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References

  1. S. Abiteboul, R. Hull, and V. Vianu. Foundations of Databases. Addison-Wesley, 1995.

    Google Scholar 

  2. Ravindra K. Ahuja, Thomas L. Magnanti, and James B. Orlin. Network Flows: Theory, Algorithms, and Applications. Prentice Hall, 1993.

    Google Scholar 

  3. C. Boutilier and H. Hoos. Bidding languages for combinatorial auctions. In Proc. IJCAI’01, pages 1211–1217, 2001.

    Google Scholar 

  4. S. J. Brams. On envy-free cake division. J. Comb. Theory, Ser. A, 70(1):170–173, 1995.

    Google Scholar 

  5. Dan Brickley and Ramanathan V. Guha, editors. RDF Vocabulary Description Language 1.0: RDF Schema. W3C Recommendation, 2004.

    Google Scholar 

  6. A. Cal`ı, G. Gottlob, and M. Kifer. Taming the infinite chase: Query answering under expressive relational constraints. In Proc. KR’08, pages 70–80, 2008.

    Google Scholar 

  7. D. Calvanese, G. De Giacomo, D. Lembo, M. Lenzerini, and R. Rosati. Tractable reasoning and efficient query answering in description logics: The dl-lite family. J. Autom. Reasoning, 39(3):385–429, 2007.

    MATH  CrossRef  Google Scholar 

  8. A. K. Chandra and P. M. Merlin. Optimal implementation of conjunctive queries in relational data bases. In Proc. STOC’77, pages 77–90, 1977.

    Google Scholar 

  9. P. Cramton, Y. Shoham, and R. Steinberg. Combinatorial Auctions. MIT Press, 2006.

    Google Scholar 

  10. M. Croitoru and S. Rudolph. Exclusivity-based allocation of knowledge. In Proc. AAMAS 2012, pages 1249–1250, 2012.

    Google Scholar 

  11. A. Giovannucci, J. Rodriguez-Aguilar, J. Cerquides, and U. Endriss. Winner determination for mixed multi-unit combinatorial auctions via Petri nets. In Proc. AAMAS’07, 2007.

    Google Scholar 

  12. N. Nisan. Bidding and allocations in combinatorial auctions. In Proc. EC-2000, 2000.

    Google Scholar 

  13. D. Porello and U. Endriss. Modelling combinatorial auctions in linear logic. In Proc. KR’10,2010.

    Google Scholar 

  14. Eric Prud’hommeaux and Andy Seaborne, editors. SPARQL Query Language for RDF. W3C Recommendation, 2008.

    Google Scholar 

  15. M. Rothkopf, A. Pekec, and R. Harstad. Computationally manageable combinational auctions. Management Science, 44:1131–1147, 1998.

    MATH  CrossRef  Google Scholar 

  16. W3C OWL Working Group. OWL 2 Web Ontology Language Recommendation, 2009.: Document Overview. W3C

    Google Scholar 

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Author information

Authors and Affiliations

  1. KSRI/AIFB, Karlsruhe Institute of Technology, Karlsruhe, Germany

    Sebastian Rudolph

  2. LIRMM, Montpellier, France

    Madalina Croitoru

Authors
  1. Sebastian Rudolph
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  2. Madalina Croitoru
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Corresponding author

Correspondence to Sebastian Rudolph .

Editor information

Editors and Affiliations

  1. School of Computing, University of Portsmouth, Whitepost Lane The Lilacs, Portsmouth, PO1 3AH, Hampshire, United Kingdom

    Max Bramer

  2. School of Computing, Engineering & Mathe, University of Brighton, Lewes Road, Brighton, BN2 4GJ, West Sussex, United Kingdom

    Miltos Petridis

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© 2012 Springer-Verlag London

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Rudolph, S., Croitoru, M. (2012). Multi-Agent Knowledge Allocation. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXIX. SGAI 2012. Springer, London. https://doi.org/10.1007/978-1-4471-4739-8_12

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  • DOI: https://doi.org/10.1007/978-1-4471-4739-8_12

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