Multi-Agent Knowledge Allocation

  • Sebastian Rudolph
  • Madalina Croitoru
Conference paper


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.


Allocation Problem Optimal Allocation Valuation Function Combinatorial Auction Conjunctive Query 
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  1. 1.
    S. Abiteboul, R. Hull, and V. Vianu. Foundations of Databases. Addison-Wesley, 1995.Google Scholar
  2. 2.
    Ravindra K. Ahuja, Thomas L. Magnanti, and James B. Orlin. Network Flows: Theory, Algorithms, and Applications. Prentice Hall, 1993.Google Scholar
  3. 3.
    C. Boutilier and H. Hoos. Bidding languages for combinatorial auctions. In Proc. IJCAI01, pages 1211–1217, 2001.Google Scholar
  4. 4.
    S. J. Brams. On envy-free cake division. J. Comb. Theory, Ser. A, 70(1):170–173, 1995.Google Scholar
  5. 5.
    Dan Brickley and Ramanathan V. Guha, editors. RDF Vocabulary Description Language 1.0: RDF Schema. W3C Recommendation, 2004.Google Scholar
  6. 6.
    A. Cal`ı, G. Gottlob, and M. Kifer. Taming the infinite chase: Query answering under expressive relational constraints. In Proc. KR08, pages 70–80, 2008.Google Scholar
  7. 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.MATHCrossRefGoogle Scholar
  8. 8.
    A. K. Chandra and P. M. Merlin. Optimal implementation of conjunctive queries in relational data bases. In Proc. STOC77, pages 77–90, 1977.Google Scholar
  9. 9.
    P. Cramton, Y. Shoham, and R. Steinberg. Combinatorial Auctions. MIT Press, 2006.Google Scholar
  10. 10.
    M. Croitoru and S. Rudolph. Exclusivity-based allocation of knowledge. In Proc. AAMAS 2012, pages 1249–1250, 2012.Google Scholar
  11. 11.
    A. Giovannucci, J. Rodriguez-Aguilar, J. Cerquides, and U. Endriss. Winner determination for mixed multi-unit combinatorial auctions via Petri nets. In Proc. AAMAS07, 2007.Google Scholar
  12. 12.
    N. Nisan. Bidding and allocations in combinatorial auctions. In Proc. EC-2000, 2000.Google Scholar
  13. 13.
    D. Porello and U. Endriss. Modelling combinatorial auctions in linear logic. In Proc. KR10,2010.Google Scholar
  14. 14.
    Eric Prud’hommeaux and Andy Seaborne, editors. SPARQL Query Language for RDF. W3C Recommendation, 2008.Google Scholar
  15. 15.
    M. Rothkopf, A. Pekec, and R. Harstad. Computationally manageable combinational auctions. Management Science, 44:1131–1147, 1998.MATHCrossRefGoogle Scholar
  16. 16.
    W3C OWL Working Group. OWL 2 Web Ontology Language Recommendation, 2009.: Document Overview. W3CGoogle Scholar

Copyright information

© Springer-Verlag London 2012

Authors and Affiliations

  1. 1.KSRI/AIFB, Karlsruhe Institute of TechnologyKarlsruheGermany
  2. 2.LIRMMMontpellierFrance

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