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Learning from Inconsistencies in an Integrated Cognitive Architecture

Learning from Inconsistencies in an Integrated Cognitive Architecture. The First Conference on Artificial General Intelligence (AGI-08) March 1st, 2008 Kai-Uwe Kühnberger (with Peter Geibel, Helmar Gust, Ulf Krumnack, Ekaterina Ovchinnikova, Angela Schwering, Tonio Wandmacher).

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Learning from Inconsistencies in an Integrated Cognitive Architecture

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  1. Learning from Inconsistencies in an Integrated Cognitive Architecture The First Conference on Artificial General Intelligence (AGI-08) March 1st, 2008 Kai-Uwe Kühnberger (with Peter Geibel, Helmar Gust, Ulf Krumnack, Ekaterina Ovchinnikova, Angela Schwering, Tonio Wandmacher) Kai-Uwe Kühnberger et al. Universität Osnabrück The First Conference on Artificial General Intelligence (AGI-08) Memphis, March 1st, 2008

  2. Overview • Introduction • Learning in Cognitive Systems • The I-Cog Architecture • General Overview of the System • Learning from Inconsistencies • General Remarks • Learning from Inconsistencies in Analogy Making and the Overall System • Conclusions Kai-Uwe Kühnberger et al. Universität Osnabrück The First Conference on Artificial General Intelligence (AGI-08) Memphis, March 1st, 2008

  3. Introduction Learning in Cognitive Systems Kai-Uwe Kühnberger et al. Universität Osnabrück The First Conference on Artificial General Intelligence (AGI-08) Memphis, March 1st, 2008

  4. Learning • Usually cognitive architectures are based on a number of different modules. • Example: Hybrid System • Obviously, coherence problems and consistency clashes can occur, in particular, in hybrid systems. • In hybrid architectures, two main questions can be asked: • On which level should learning be implemented? • What are plausible strategies in order to resolve inconsistencies? • Idea of this talk: Use occurring inconsistencies as a mechanism (trigger) of learning. Kai-Uwe Kühnberger et al. Universität Osnabrück The First Conference on Artificial General Intelligence (AGI-08) Memphis, March 1st, 2008

  5. The I-Cog Architecture General Overview Kai-Uwe Kühnberger et al. Universität Osnabrück The First Conference on Artificial General Intelligence (AGI-08) Memphis, March 1st, 2008

  6. A Proposal: I-Cog • I-Cog is a modular system consisting of three main modules: • Analogy Engine (AE): • Claim: AE is able to cover a variety of different reasoning abilities. • Ontology Rewriting Device (ORD): • Claim: Ontological background knowledge needs to be implemented in a way, such that dynamic updates are possible. • Neuro-Symbolic Learning Device (NSLD): • Claim: The neuro-symbolic learning device enables robust learning of symbolic theories form noisy data. • Finally: these three modules interact in a non-trivial way and are governed by a heuristic-driven Control Device (CD). • Kühnberger, K.-U. et al. (2007): I-Cog: A Computational Framework for Integrated Cognition of Higher Cognitive Abilities, in Proceedings of MICAI 2007, LNAI 4827, pp. 203-214, Springer. Kai-Uwe Kühnberger et al. Universität Osnabrück The First Conference on Artificial General Intelligence (AGI-08) Memphis, March 1st, 2008

  7. The Overall I-Cog Architecture Kai-Uwe Kühnberger et al. Universität Osnabrück The First Conference on Artificial General Intelligence (AGI-08) Memphis, March 1st, 2008

  8. Learning in I-Cog • Learning is based on occurring inconsistencies • In the case of ORD, rewriting algorithms make sure that inconsistencies are resolved (where this is possible). • Ovchinnikova, E. & Kühnberger, K.-U. (2007). Debugging Automatically Extended Ontologies, GLDV-Journal for Computational Linguistics and Language Technology, 23(2):19-33 . • NSLD is a learning device, where weights are adjusted based on backpropagation of errors. • Gust, H., Kühnberger, K.-U. & Geibel, P. (2007). Learning Models of Predicate Logical Theories with Neural Networks Based on Topos Theory, in P. Hitzler & B. Hammer (eds.): Perspectives of Neural-Symbolic Integration, Series “Computational Intelligence”, Springer, pp. 209-240. • In the case of AE, it is possible to reduce many adaptation processes to occurring inconsistencies. • Claim 1: Learning is distributed over the whole system. • Claim 2: Learning takes place because errors / inconsistencies occur triggering an adaptation process. Kai-Uwe Kühnberger et al. Universität Osnabrück The First Conference on Artificial General Intelligence (AGI-08) Memphis, March 1st, 2008

  9. Learning from Inconsistencies The Example of Analogical Reasoning Kai-Uwe Kühnberger et al. Universität Osnabrück The First Conference on Artificial General Intelligence (AGI-08) Memphis, March 1st, 2008

  10. General Remarks • Inconsistencies are classically connected to logic • If for a set of axioms  (relative to a language L)  can be entailed and  can be entailed, then  is inconsistent. • We use the term “inconsistency” rather loosely and do not restrict this concept to logic. Here are some examples: • Every analogy establishes a relation that resolves a clash of concepts, information, interpretations etc. • Gust, H. & Kühnberger, K.-U. (2006). Explaining Effective Learning by Analogical Reasoning, 28th Annual Conference of the Cognitive Science Society, pp. 1417-1422. • Ontology generation / learning • Ovchinnikova, E., Wandmacher, T. & Kühnberger, K.-U. (2007). Solving Terminological Inconsistency Problems in Ontology Design, IBIS 4:65-80. • Non-monotonicity effects in reasoning. • Ovchinnikova, E. & Kühnberger, K.-U. (2006). Adaptive ALE-TBox for Extending Terminological Knowledge, in Proceedings of AI’06, LNAI 4304, Springer, pp. 1111-1115. Kai-Uwe Kühnberger et al. Universität Osnabrück The First Conference on Artificial General Intelligence (AGI-08) Memphis, March 1st, 2008

  11. The Analogy Engine • The Analogy Engine is based on Heuristic-Driven Theory Projection (HDTP). • HDTP is a mathematically sound theory of computing analogies. • It is based on anti-unification of a source theory ThS and a target theory ThT. • It was applied to various domains like naïve physics, metaphors, geometric figures etc. • Some features: • Complex formulas can be anti-unified. • A theorem prover allows the re-representation of formulas. • Whole theories can be generalized. • The involved processes are governed by heuristics. • Gust, H., Kühnberger, K.-U. & Schmid, U. (2006). Metaphors and Heuristic-Driven Theory Projection (HDTP), Theoretical Computer Science, 354:98-117. Kai-Uwe Kühnberger et al. Universität Osnabrück The First Conference on Artificial General Intelligence (AGI-08) Memphis, March 1st, 2008

  12. Recursion Example I For the generalized theory, the following substitutions need to be established: 1: E  0, Op1  add, Op2  s 2: E  s(0), Op1  mult, Op2  z.add(x,z) Kai-Uwe Kühnberger et al. Universität Osnabrück The First Conference on Artificial General Intelligence (AGI-08) Memphis, March 1st, 2008

  13. Recursion Example II Trying to anti-unify 1 and 1 is not possible. But by using axioms 1 and 2 we can derive mult(s(0),x) = add(x,mult(0,x)) = add(x,0) = … = add(0,x)Hence we can derive: 3: x: mult(s(0),x) = xFor the generalized theory, the following substitutions can be established: 1: E  0, Op  add and 2: E  s(0), Op  mult Kai-Uwe Kühnberger et al. Universität Osnabrück The First Conference on Artificial General Intelligence (AGI-08) Memphis, March 1st, 2008

  14. Conclusion • Main claims: • In cognitive architectures “inconsistencies” (as used in the broad sense here) should be considered as a trigger for learning and adaptation. • These adaptation processes can be relevant for: • Adapting background knowledge, • Reasoning processes of various types, • Neuro-based learning approaches. • Learning in the systems is therefore distributed and continuously realized. Kai-Uwe Kühnberger et al. Universität Osnabrück The First Conference on Artificial General Intelligence (AGI-08) Memphis, March 1st, 2008

  15. Thank you very much!! Questions? Kai-Uwe Kühnberger et al. Universität Osnabrück The First Conference on Artificial General Intelligence (AGI-08) Memphis, March 1st, 2008

  16. References • Analogical Reasoning (Selection) • Gust, H., Kühnberger, K.-U. & Schmid, U. (2006). Metaphors and Heuristic-Driven Theory Projection (HDTP), Theoretical Computer Science, 354:98-117. • Gust, H. & Kühnberger, K.-U. (2006). Explaining Effective Learning by Analogical Reasoning, in: R. Sun & N. Miyake (eds.): 28th Annual Conference of the Cognitive Science Society, Lawrence Erlbaum, pp. 1417-1422. • Gust, H., Krumnack, U., Kühnberger, K.-U. & Schwering, A. (2007). An Approach to the Semantics of Analogical Relations, in S. Vosniadou et al. (eds.): Proceedings of EuroCogSci 2007, Lawrence Erlbaum, pp. 640-645. • Krumnack, U., Schwering, A., Gust, H. & Kühnberger, K.-U. (2007). Restricted Higher-Order Anti-Unification for Analogy Making, to appear in Proceedings of AI’07, Springer. • Gust, H., Krumnack, U., Kühnberger, K.-U. & Schwering, A. (2008). Analogical Reasoning: A Core of Cognition, to appear in Künstliche Intelligenz 1/2008. Kai-Uwe Kühnberger et al. Universität Osnabrück The First Conference on Artificial General Intelligence (AGI-08) Memphis, March 1st, 2008

  17. References • Neuro-Symbolic Integration (Selection) • Gust, H., Kühnberger, K.-U. & Geibel, P. (2007).Learning and Memorizing Models of Logical Theories in a Hybrid Learning Device, to appear in Proceedings of ICONIP 2007, Springer. • Gust, H., Kühnberger, K.-U. & Geibel, P. (2007). Learning Models of Predicate Logical Theories with Neural Networks Based on Topos Theory, in P. Hitzler & B. Hammer (eds.): Perspectives of Neural-Symbolic Integration, Series “Computational Intelligence”, Springer, pp. 209-240. • Ontology Rewriting (Selection) • Ovchinnikova, E. & Kühnberger, K.-U. (2007). Debugging Automatically Extended Ontologies, GLDV-Journal for Computational Linguistics and Language Technology, volume 23(2). • Ovchinnikova, E., Wandmacher, T. & Kühnberger, K.-U. (2007). Solving Terminological Inconsistency Problems in Ontology Design, International Journal of Interoperability in Business Information Systems, 4:65-80. • Ovchinnikova, E. & Kühnberger, K.-U. (2006). Adaptive ALE-TBox for Extending Terminological Knowledge, in A. Sattar & B. H. Kang (eds.): Proceedings of AI’06, LNAI 4304, Springer, pp. 1111-1115. Kai-Uwe Kühnberger et al. Universität Osnabrück The First Conference on Artificial General Intelligence (AGI-08) Memphis, March 1st, 2008

  18. References • I-Cog • Kühnberger, K.-U., Geibel, P., Gust, H., Krumnack, U., Ovchinnikova, E., Schwering, A. & Wandmacher, T. (2008): Learning from Inconsistencies in an Integrated Cognitive Architecture, to appear in Proceedings of AGI 2008, IOS Press. • Kühnberger, K.-U. (2007): Principles for the Foundation of Integrated Higher Cognition (Abstract). In: D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the CogSci 2007, (p. 1796). Austin, TX: Cognitive Science Society. • Kühnberger, K.-U., Wandmacher T., Schwering, A., Ovchinnikova, E., Krumnack, U., Gust, H. & Geibel, P. (2007): I-Cog: A Computational Framework for Integrated Cognition of Higher Cognitive Abilities, in Proceedings of MICAI 2007, LNAI 4827, pp. 203-214, Springer. • Kühnberger, K.-U., Wandmacher, T., Schwering, A., Ovchinnikova, E., Krumnack, U., Gust, H. & Geibel, P. (2007): Modeling Human-Level Intelligence by Integrated Cognition in a Hybrid Architecture, in P. Hitzler, T. Roth-Berghofer, S. Rudolph: FAInt-07, Workshop at KI 2007, CEUR-WS, vol. 277, pp. 1-15. Kai-Uwe Kühnberger et al. Universität Osnabrück The First Conference on Artificial General Intelligence (AGI-08) Memphis, March 1st, 2008

  19. Peter Geibel Karl Gerhards Helmar Gust Ulf Krumnack Kai-Uwe Kühnberger Jens Michaelis Ekaterina Ovchinnikova Angela Schwering Konstantin Todorov Ulas Türkmen Tonio Wandmacher Members of the AI group Kai-Uwe Kühnberger et al. Universität Osnabrück The First Conference on Artificial General Intelligence (AGI-08) Memphis, March 1st, 2008

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