Learning from inconsistencies in an integrated cognitive architecture

Frontiers in Artificial Intelligence and Applications

Volumn 171, Issue 1, 2008, Pages 212-223

  Kühnberger, Kai Uwe ^a   Geibel, Peter ^a   Gust, Helmar ^a   Krumnack, Ulf ^a   Ovchinnikova, Ekaterina ^a   Schwering, Angela ^a   Wandmacher, Tonio ^a  

^a UNIVERSITY OF OSNABRÜCK   (Germany)

Author keywords

Hybrid Systems; Inconsistencies; Integrated Cognition; Learning

Indexed keywords

COGNITIVE SYSTEMS; CONSTRAINT SATISFACTION PROBLEMS;

COGNITIVE ARCHITECTURES; CONSTRAINT-SATISFACTION PROBLEMS; DEDUCTIVE REASONING; INCONSISTENCY; INTEGRATED COGNITION; LEARNING; PLANNING SYSTEMS; PLANNING TOOLS; REAL-WORLD; SYMBOL-BASED SYSTEM;

HYBRID SYSTEMS;

EID: 84875918017     PISSN: 09226389     EISSN: 18798314     Source Type: Book Series    
DOI: None     Document Type: Conference Paper

References (34)

1. K.-U. Kühnberger, T. Wandmacher, A. Schwering, E. Ovchinnikova, U. Krumnack, H. Gust and P. Geibel, I-Cog: A Computational Framework for Integrated Cognition of Higher Cognitive Abilities, Proceedings of 6th Mexican International Conference on Artificial Intelligence, LNAI 4827, Springer (2007) pp. 203-214.
2. K.-U. Kühnberger, T. Wandmacher, E. Ovchinnikova, U. Krumnack, H. Gust and P. Geibel, Modeling Human-Level Intelligence by Integrated Cognition in a Hybrid Architecture, in P. Hitzler, T. Roth-Berghofer, S. Rudolph (eds): Foundations of Artificial Intelligence (FAInt-07), Workshop at KI 2007, CEUR-WS 277 (2007), 1-15.
3. H. Gust, K.-U. Kühnberger and U. Schmid, Metaphors and Heuristic-Driven Theory Projection (HDTP), Theoretical Computer Science 354 (2006) 98-117.
4. G. Plotkin, A note of inductive generalization, Machine Intelligence 5 (1970), 153-163.
5. U. Krumnack, A. Schwering, H. Gust, K.-U. Kühnberger, Restricted Higher-Order Anti-Unification for Analogy Making, in Proceedings of Twenties Australian Joint Conference on Artificial Intelligence, LNAI 4830, Springer (2007) pp. 273-282.
6. H. Gust, K.-U. Kühnberger and U. Schmid, Solving Predictive Analogies with Anti-Unification, in: P. Slezak (eds.): Proceedings of the Joint International Conference on Cognitive Science, 2003, pp. 145-150.
7. A. Schwering, U. Krumnack, K.-U. Kühnberger and H. Gust, Using Gestalt Principles to Compute Analogies of Geometric Figures, in: D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th Annual Conference of the Cognitive Science Society, Cognitive Science Society, 2007, pp. 1485-1490.
8. E. Ovchinnikova and K.-U. Kühnberger, Adaptive ALE-TBox for Extending Terminological Knowledge. In A. Sattar, B. H. Kang (eds.): Proceedings of the 19th ACS Australian Joint Conference on Artificial Intelligence, LNAI 4304, Springer, 2006, pp. 1111-1115.
9. E. Ovchinnikova, T. Wandmacher and K.-U. Kühnberger, Solving Terminological Inconsistency Problems in Ontology Design. International Journal of Interoperability in Business Information Systems, 2(1) (2007), 65-80.
10. R. Goldblatt, Topoi: The Categorial Analysis of Logic. Studies in Logic and the Foundations of Mathematics, 98, North-Holland, Amsterdam (1979).
11. H. Gust, K.-U. Kühnberger and P. Geibel, Learning Models of Predicate Logical Theories with Neural Networks based on Topos Theory. In P. Hitzler and B. Hammer (eds.): Perspectives of Neuro-Symbolic Integration, Studies in Computational Intelligence (SCI) 77, Springer, (2007) pp. 233-264.
12. H. Gust, K.-U. Kühnberger and U. Schmid, Ontological Aspects of Computing Analogies. Proceedings of the Sixth International Conference on Cognitive Modeling, Mahwah, NJ: Lawrence Erlbaum, (2004) pp. 350-351.
13. H. Gust, K.-U. Kühnberger and U. Schmid, Ontologies as a Cue for the Metaphorical Meaning of Technical Concepts, in A. Schalley, D. Khlentzos (eds.): Mental States: Evolution, Function, Nature, Volume I, John Benjamins Publishing Company, Amsterdam, Philadelphia, (2007) pp. 191-212.
14. B. Indurkhya, Metaphor and Cognition, Dordrecht, the Netherlands, Kluver (1992).
15. H. Gust, U. Krumnack, K.-U. Kühnberger and A. Schwering, An Approach to the Semantics of Analogical Relations, in S. Vosniadou, D. Kayser, A. Protopapas (eds.): Proceedings of EuroCogSci 2007, Lawrence Erlbaum, pp. 640-645.
16. P. Haase, F. van Harmelen, Z. Huang, H. Stuckenschmidt and Y. Sure, A framework for handling inconsistency in changing ontologies. Proc.of the Fourth Internation Semantic Web Conference, LNCS, Springer (2005).
17. F. Baader, D. Calvanese, D. McGuinness, D. Nardi and P. Patel-Schneider (eds.), Description Logic Handbook: Theory, Implementation and Applications. Cambridge University Press (2003).
18. E. Ovchinnikova and K.-U. Kühnberger, Automatic Ontology Extension: Resolving Inconsistencies, GLDV Journal for Computational Linguistics and Language Technology (to appear).
19. D. Hofstadter and The Fluid Analogies Research Group, Fluid concepts and creative analogies. New York: Basic Books (1995).
20. M. Dastani, Languages of Perception, ILLC Disseration Series 1998-05, 1998, http://www.illc.uva.nl/Publications/Dissertations/DS-1998-05.text.ps.gz.
21. J. Anderson and R. Thompson, Use of Analogy in a Production System Architecture, in Vosniadou, Ortony (eds): Similarity and analogical reasoning, Cambridge (1989) 267-297.
22. B. Falkenhainer, K. Forbus and D. Gentner, The structure-mapping engine: Algorithm and example, Artificial Intelligence 41 (1989) 1-63.
23. D. Gentner, TheMechanisms of Analogical Learning, in: S. Vosniadou & A. Ortony (editors): Similarity and Analogical Reasoning, New York, Cambridge University Press.
24. S. Heymans and D. Vermeir, A Defeasible Ontology Language, In Meersman, R. et al. (eds): On the Move to Meaningful Internet Systems, 2002-Confederated International Conferences: CoopIS, DOA, and ODBASE 2002, Springer, pp. 1033-1046.
25. G. Flouris, D. Plexousakis and G. Antoniou, Updating DLs Using the AGM Theory: A Preliminary Study, in: Description Logics, 2006.
26. F. Baader and B. Hollunder, Embedding defaults into terminological knowledge representation formalisms. J. Autom. Reasoning, 14(1) (1995) 149-180.
27. A. Kalyanpur, Debugging and Repair of OWL Ontologies. Ph.D. Dissertation, (2006).
28. S. Bader, P. Hitzler, S. Hölldobler and A. Witzel, A Fully Connectionist Model Generator for Covered First-Order Logic Programs. In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence (2007), pp. 666-671.
29. M. Healy and T. Caudell, Neural Networks, Knowledge and Cognition: AMathematical Semantic Model Based upon Category Theory. University of New Mexico, (2004), EECE-TR-04-020.
30. A. D'Avila Garcez, K. Broda and D. Gabbay, Neural-Symbolic Learning Systems. Foundations and Applications. Springer (2002).
31. N. Cassimatis, A Cognitive Substrate for Achieving Human-Level Intelligence, AI Magazine 27(2) (2006) 45-56.
32. B. Kokinov and A. Petrov, Integrating Memory and Reasoning in Analogy-Making: The AMBRModel, in D. Gentner, K. Holyoak, B. Kokinov (eds.): The Analogical Mind. Perspectives from Cognitive Science, Cambridge Mass. (2001).
33. P. Langley, Cognitive Architectures and General Intelligent Systems, AI Magazine, 27(2) (2006), 33-44.
34. P. Wang, Rigid Flexibility: The Logic of Intelligence, Springer, 2006.