PUBLICATION LIST


Akihiro Yamamoto

Department of Intelligence Science and Technology
Graduate School of Informatics
Kyoto University

1. Academic Papres (Reviewed and Published)

  1. Yamamoto, A.: Hypothesis Finding based on Upward Refinement of Residue Hypotheses, Theoretical Computer Science, 298, 5-19 (2003).
  2. Kikuchi, T. and Yamamoto, A. : Unifying Various Knowledge Discovery Systems in Logic of Discovery, Information Modelling and Knowledge Bases XIV, 118-127, IOS Press (2003).
  3. Fronhoefer, B. and Yamamoto, A. : Minimised Residue Hypotheses in  Relevant Logic, Proceedings of the 13th International Workshop on Algorithmic Learning Theory (Lecture Notes in Artificial Intelligence 2533), 278-292, Springer (2002).
  4. Kikuchi, T. and Yamamoto, A. : A Software Environment for Operating Various Discovery Systems based on the Logic of Discovery, Journal of JSAI , 17(5), 576-584  (2002) (in Japanese).
  5. Yamamoto, A., Ito, K., Ishino, A., and Arimura, H. : Deductive and Inductive Reasoning on Semi-Structured Documents Modelled with Hedges, Proceedings of the 11th International Workshop on Inductive Logic Programming(Lecture Notes in Artificial Intelligence), 240-247, Springer (2001).
  6. Yamamoto, A.: Using Abduction for Induction based on Bottom Generalization, in P.A. Flach and A.C. Kakas (eds.)  Abductive and Inductive Reasoning : Essays on their Relation and Integration, 267-280, Kluwer Academic Press (2000).
  7. Yamamoto, A. and Fronhoefer, B. : Hypotheses Finding via Residue Hypotheses with the Resolution Principle, Proceedings of the 11th International Workshop on Algorithmic Learning Theory  (Lecture Notes in Artificial Intelligence 1968), 156-165, Springer (2000).
  8. Yamamoto, A. : New Conditions for the Existence of a Least Generalization under Relative Subsumption, Proceedings of the 10th International Workshop on Inductive Logic Programming , (Lecture Notes in Artificial Intelligence 1866), 253-264, Springer (2000).
  9. Ito, K. and Yamamoto, A. : Finding Hypotheses from Examples by Computing the Least Generalization of Bottom Clauses, Journal of JSAI , 14(4), 709-716 (1999) (in Japanese).
  10. Ito, K. and Yamamoto, A. :A Constructive Learning Algorithm which Invents New Predicates Based on Schema and Queries, Journal of JSAI, 14(4), 679-688 (1999) (in Japanese).
  11. Ishino, A. and Yamamoto, A. :  Inference of Equations by Inverse of Reduction, Journal of JSAI , 14(3), 512-519 (1999) (in Japanese).
  12. Yamamoto, A. : Revising the Logical Foundations of Inductive Logic Programming Systems with Ground Reduced Program, New Generation Computing, 17(1): 119-127 (1999).
  13. Yamamoto, A. : An Inference Method for the Complete Inverse of Relative Subsumption, New Generation Computing, 17(1): 99-117 (1999).
  14. Ito, K. and Yamamoto, A. :  Finding Hypotheses from Examples by Computing the Least Generalization of Bottom Clauses, Proceedings of the First International Conference on Discovery Science (Lecture Notes in Artificial Intelligence 1532), 303-314, Springer (1998). 
  15. Yamamoto A. : Logical Aspects of Several  Bottom-up Fittings, Proceedings of the 9th International Workshop on Algorithmic Learning Theory (Lecture Notes in Artificial Intelligence 1501), 158-168,Springer(1998).
  16. Ishino, A. and Yamamoto, A. : Generalizations in Typed Equational Programming and Their Application to Learning Functions, New Generation Computing, 15:87-103(1997).
  17. Yamamoto, A. : Which Hypotheses Can Be Found with Inverse Entailment? Proceedings of the 7th International Workshop on Inductive Logic Programming (Lecture Notes in Artificial Intelligence 1297), 296-308, Springer (1997).
  18. Yamamoto, A. : Learning Logic Programs Using Definite Equality Theories as Background Knowledge, IEICE Trans. Inf. and Syst. , E78-D(5), 539--544, (1995).
  19. Yamamoto, A. : Programming by First Order Formulas for Object and Relation Definition, in Information Modelling and Knowledge Bases VI, The IOS Press (1995).
  20. Ishino, A. and Yamamoto, A. : Learning from Examples with Typed Equational Programming, Proceedings of the 5th International Workshop on Algorithmic Learning Theory (Lecture Notes in Artificial Intelligence 872), 301-316, Springer-Verlag(1994).
  21. Yamamoto A. : Generalized Unification as Background Knowledge in Learning Logic Programs, Proceedings of the 4th International Workshop on Algorithmic  Learning Theory (Lecture Notes in Artificial Intelligence 744), 111-122, Springer-Verlag(1993).
  22.  Ito, K. and Yamamoto, A.: Polynomial-Time MAT Learning of Multilinear Logic Programs, Proceedings of the Third Workshop on Algorithmic  Learning Theory (Lecture Notes in Artificial Intelligence 743), 63-74, Springer-Verlag(1992).
  23. Arikawa, S., Miyano, S., Shinohara, A., Shinohara, T., and Yamamoto, A. : Algorithmic Learning Theory  with Elementary Formal Systems, IEICE Trans. Inf. & Syst., E75-D(4), 405-414 (1992).
  24. Yamamoto, A. : Procedural Semantics and Negative Information of Elementary Formal System, the Journal of Logic Programming, 13(1), 89-97 (1992).
  25. Yamamoto, A. : Generalization of Weakly Reducing EFS with Abstraction,  in Advances in Information Modelling and Knowledge Bases, 110-123, The IOS Press (1991).
  26. Arikawa, S., Shinohara, T., and Yamamoto, A. : Elementary Formal System as a Unifying Framework for Language Learning, Proceedings of the Second Annual Workshop on Computational Learning Theory, 312-327 (1989).
  27. Arikawa, S., Shinohara, T., and Yamamoto, A. : Elementary Formal System as a Unifying Framework for Language Learning, Proceedings of the Second Annual Workshop on Computational Learning Theory, 312-327 (1989).
  28. Yamamoto, A. : Elementary Formal System as a Logic Programming Language,  Proceedings of the 8th Logic Programming Conference (Lecture Notes in Artificial Intelligence 485), 73-86, Springer-Verlag(1989).
  29. Yamamoto, A :Completeness Problems of Extended Unification based on Basic Narrowing ,Computer Software, 6(3), 35-45(1989) (in Japanese).
  30.  Yamamoto, A.: Completeness of Extended Unification Based on Basic Narrowing, Proceedings of the 7th Logic Programming Conference (Lecture Notes in Artificial Intelligence 383), 1-10, Springer-Verlag(1988).
  31. Yamamoto, A.: A Theoretical Combination of SLD-Resolution and Narrowing, J.-L. Lassez (ed.) Logic Programming, 470-487, The MIT Press (1987).
  32. Yamamoto, A. : An Anatomy of Abstraction, Bulletin of Informatics and Cybernetics, 22(3-4), 179-188 (1987).

2. International Conference Proceedings (Reviewed)

  1. Ogiso, A and Yamamoto, A : Finding Similar Stories by Using Sequences of Occurrence Vectors, the 13th European-Japanese Conference on Information Modelling and Knowledge Bases, Kokura (2003).
  2. Yamamoto, A and Fronhoefer, B : Finding Hypotheses by Generalizing Residue Hypotheses, the Work in Progress session in the 11th International Workshop on Inductive Logic Programming, Strasbourg (2001).
  3. Yamamoto, A.: Hypothesis Finding based on Upward Refinement of Residue Hypotheses –extended abstract—, In Proceedings of the Workshop on Logic and Learning affiliated with LICS 2001 (2001).
  4. Yamamoto, A. : Which Hypotheses Can Be Found with Inverse Entailment? –Extended Abstract—, Proceedings of IJCAI ’97 Workshop on Frontiers of Inductive Logic Programming, 19-23(1997).
  5. Yamamoto, A. : Representing Inductive Inference with SOLD-Resolution, Proceedings of IJCAI ’97 Workshop on Abduction and Induction in AI, 59-63(1997).

3. Talks in Conferences

  1. Yamamoto, A. : Relative Least Generalization Revisited, Second Joint Seminar on Theories and Applications of Discovery Science, The University of New South Wales, Sydney (2000).
  2. Yamamoto, A. : Hypothesis Construction and Network, Joint Seminar on Theories and Applications of Discovery Science, The University of New South Wales, Sydney (1999).
  3. Yamamoto, A. : Hypothesis Construction and Beyond it, 16th Machine Intelligence Workshop, York (1998).
  4. Yamamoto, A. : Characterization of Inductive Method based on Multiple Abduction, Impromptu Talk at 8th Workshop on Algorithmic Learning Theory, Sendai(1997).
  5. Yamamoto, A. : Extensions of Deductive Logic Programming for Inductive Logic Programming,  Seminar on Deduction, Dagstuhl Seminar Report 170, 22-23, Dagstuhl (1997).
  6. Yamamoto, A. : Procedural Semantics of Elementary Formal System and Closed World Assumption, Japanese-French Seminar on Deductive Database and Artificial Intelligence (1990).
  7. Arikawa, S., Shinohara, T., and Yamamoto, A. : Elementary Formal System as a Framework of Inductive Inference, Theoretical Foundations of Knowledge Information Processing (1989).
  8. Arikawa, S., Shinohara, T., and Yamamoto, A. : Inductive Inference  of  Formal Languag by Elementary Formal Systems, Joint Scandinavian-Japanese Seminar on Information Modelling and Knowledge Bases (1989).
  9. Arikawa, S., Haraguchi, M., Inoue, H., Kawasaki, Y., Miyahara, T, Miyano, S., Oshima., K., Sakai, H., Shinohara, T., Shiraishi, S., Takeda, M., Takeya, S., Yamamoto, A.: The Text Database Management System SIGMA : An Improvement of the Main Engine, Proceedings of the Berliner Informatik-Tage, 72-81 (1988).
  10. Yamamoto, A. : Some Deductive Approaches to Inductive Logic,  Invited Talk, The 8th Asian Logic Conference, Chongqing (2002).

4. Books

  1. Tanaka, Y., Kangassalo, H., Jaakkola, H., and , Yamamoto, A.(eds.): Information Modelling and Knowledge Bases VII, The IOS Press (1996).
  2. Arikawa, S. and Haraguchi, M. (eds.) : Predicate Logic and Logic Programming. Ohmsha, Tokyo. (1988)  (in Japanese).

5. Surveys

  1. Yamamoto, A., Arimura, H., and Hirata, K.: Inductive Logic Programming and Learning, submitted to Fundameta Infomatica.
  2. Yamamoto, A : Theoretical Foundations of Inductive Logic Programming, Journal of JSAI, 12(5), 13-22(1997).
  3. Arimura, H., Hirata, K. and Yamamoto, A. : Inductive Logic Programming and Proof Completion, “Knowledge Discovery and Data Mining”, 34-44, Kyoritsu-Shuppan (2000) (in Japanese).
  4. Arimura, H. and Yamamoto, A. : Inductive Logic Programming : From Logic of Discovery to Machine Learning, IEICE Trans. Inf. and Syst. E83-D(1), 10-18 (2000).