Katsumi Inoue, Gauvain Bourgne, Takayuki Okamoto


When knowledge is physically distributed, information and knowledge of individual agents may not be collected to one agent because they should not be known to others for security and privacy reasons. We thus assume the situation that individual agents cooperate with each other to find useful information from a distributed system to which they belong, without supposing any master or mediate agent who collects all necessary information from the agents. Then we propose two complete algorithms for distributed consequence finding. The first one extends a technique of theorem proving in partition-based knowledge bases. The second one is a more cooperative method than the first one. We compare these two methods on a sample problem showing that both can improve efficiency over a centrlized approach, and then discuss other related approaches in the literature.


  1. Adjiman, P., Chatalic, P., Goasdoué, F., Rousset, M.-C. and Simon, L. (2005). Scalability study of peer-to-peer consequence finding. In Proc. IJCAI-05, pp.351-356.
  2. Adjiman, P., Chatalic, P., Goasdoué, F., Rousset, M.-C. and Simon, L. (2006). Distributed reasoning in a peer-topeer setting: Application to the semantic web. In J. Artif. Intell. Res., 25:269-314.
  3. Amir, A. and McIlraith, S. (2005). Partition-based logical reasoning for first-order and propositional theories. In Artif. Intell., 162:49-88.
  4. Bourgne, G., Maudet, N., and Inoue, K. (2010). Abduction of distributed theories through local interactions. In Proc. ECAI'10, 901-906.
  5. Ciampolini, A., Lamma, E., Mello, P., Toni, F., and Torroni, P. (2003). Cooperation and competition in ALIAS: A logic framework for agents that negotiate. Ann. Math. Artif. Intell., 37(1-2):65-91.
  6. Craig, W. (1957). Linear reasoning: A new form of the Herbrand-Gentzen theorem. J. Symbolic Logic, 22:250-268.
  7. Dechter, R. and Pearl, J. (1989). Tree clustering for constraint networks. Artif. Intell., 38:353-366.
  8. del Val, A. (1999). A new method for consequence finding and compilation in restricted languages. In Proc. AAAI-99, pp.259-264.
  9. Fisher, M. (2000) Characterizing simple negotiation as distributed agent-based theorem-proving-a preliminary report. in: Proc. 4th ICMAS, pp. 127-134.
  10. Greco, G. (2007). Solving abduction by computing joint explanations. Ann. Math. Art. Intel., 50(1-2):143-194.
  11. Hirayama, K. and Yokoo, M. (2005). The distributed breakout algorithms. Artif. Intell., 161:89-115.
  12. Inoue, K. (1992). Linear resolution for consequence finding. Artif. Intell, 56:301-353.
  13. Inoue, K. (2004). Induction as consequence finding. Machine Learning, 55:109-135.
  14. Inoue, K. and Iwanuma, K. (2004). Speculative computation through consequence-finding in multi-agent environments, Ann. Math. Artif. Intell., 42(1-3):255-291.
  15. Inoue, K., Iwanuma, K. and Nabeshima, H. (2006). Consequence finding and computing answers with defaults. J. Intell. Inform. Systems, 26:41-58.
  16. Inoue, K., Sato, T., Ishihata, M., Kameya, Y. and Nabeshima, H. (2009). Evaluating abductive hypotheses using an EM algorithm on BDDs. IJCAI, 810-815.
  17. Iwanuma, K. and Inoue, K. (2002). Minimal answer computation and SOL. JELIA 7802, LNAI 2424, 245-257, Springer.
  18. Lee, C.T. (1967). A completeness theorem and computer program for finding theorems derivable from given axioms. Ph.D. thesis, Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA.
  19. Ma, J., Russo, A., Broda, K., and Clark, K. (2008). DARE: A system for distributed abductive reasoning. AAMAS'08, 16(3):271-297.
  20. Marquis, P. (2000). Consequence finding algorithms. in: Handbook for Defeasible Reasoning and Uncertain Management Systems, Vol. 5, pp.41-145, Kluwer.
  21. Nabeshima, H., Iwanuma, K. and Inoue, K. (2003). SOLAR: A consequence finding system for advanced reasoning. TABLEAUX, LNAI 2796, 257-263, Springer.
  22. Nabeshima, H., Iwanuma, K., Inoue, K. and Ray, O. (2010). SOLAR: An automated deduction system for consequence finding. AI Communic., 23(2-3):183-203.
  23. Nienhuys-Cheng, S.-H. and de Wolf, R. (1997). Foundations of Inductive Logic Programming. LNAI 1228, Springer.
  24. Okamoto, T., Inoue, K. (2005). Distributed consequence finding with message communication. IPSJ-SIG Tech. Rep., 24:25-30, (in Japanese).
  25. Slagle, J.R. (1970). Interpolation theorems for resolution in lower predicate calculus. J. ACM, 17(3):535-542.
  26. Yokoo, M., Durfee, E.H., Ishida, T., and Kuwabara, K. (1998). The distributed constraint satisfaction problem: Formalization and algorithms. IEEE Trans. Know. & Data Eng., 10(5):673-685.

Paper Citation

in Harvard Style

Inoue K., Bourgne G. and Okamoto T. (2011). COMPLETE DISTRIBUTED CONSEQUENCE FINDING WITH MESSAGE PASSING . In Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-8425-41-6, pages 134-143. DOI: 10.5220/0003190001340143

in Bibtex Style

author={Katsumi Inoue and Gauvain Bourgne and Takayuki Okamoto},
booktitle={Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},

in EndNote Style

JO - Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
SN - 978-989-8425-41-6
AU - Inoue K.
AU - Bourgne G.
AU - Okamoto T.
PY - 2011
SP - 134
EP - 143
DO - 10.5220/0003190001340143