Reflecting inductive inference machines and its improvement by therapy
In a special sense, reflection means to think about its own capabilities. This phenomenon is studied in the field of Inductive Inference of Recursive Functions.
The main statement in [Jan95] was, that reflection is possible iff it is not necessary at all. In the present paper this is weakened. Several notions of reflection are generalized and formalized. Here reflection is seen as a process in the limit too. Prototypical investigations are done to show some effects of additional requirements: finiteness and consistency. For finite learning each of the resulting reflective identification types has its own peculiarities, even two of these are incomparable. While for LIM and FIN reflection means restriction of learning power for total consistent learning this is not the case.
The competence assessment should be used to improve the learning behaviour. Thus the idea of introducing a therapy in case of failure by changing the requirements is introduced and formalized. Based on this, some choice points while investigating therapy are worked out. So as reaction to incompetence it may be possible to use another strategy, change the space of hypothesis or to present the information in another way. Fixing such a choice, a couple of other questions is still open.
Unable to display preview. Download preview PDF.
- [Ang92]Dana Angluin. Computational learning theory: Survey and selected bibliography. In ACM Symposium on Theory of Computing, STOC'92, pages 351–368. ACM Press, 1992.Google Scholar
- [Bār72]Jānis Bārzdinš. Prognostication of automata and functions. Information Processing '71, 1972.Google Scholar
- [Gri96]Gunter Grieser. Reflexion in der Induktiven Inferenz. Diplomarbeit, Technische Hochschule Leipzig, FB Mathematik & Informatik, Februar 1996.Google Scholar
- [Jan95]Klaus P. Jantke. Reflecting and self-confident inductive inference machines. In K.P. Jantke, T. Shinohara, and T. Zeugmann, editors, Proc. 6th International Workshop on Algorithmic Learning Theory, (ALT'95), volume 997 of Lecture Notes in Artificial Intelligence. Springer-Verlag, 1995.Google Scholar
- [JL89]Klaus P. Jantke and Steffen Lange. Algorithmisches Lernen. In J. Grabowski, K. P. Jantke, and H. Thiele, editors, Grundlagen der Künstlichen Intelligenz, pages 246–277. Akademie-Verlag Berlin, 1989.Google Scholar
- [KZ85]Efim Kinber and Thomas Zeugmann. Inductive inference of almost everywhere correct programs by reliably working strategies. Elektronische Informationsverarbeitung und Kybernetik, 21(3):91–100, 1985.Google Scholar
- [LW94]Steffen Lange and Phil R. Watson. Machine discovery in the presence of incomplete or ambiguous data. In S. Arikawa and K.P. Jantke, editors, Algorithmic Learning Theory, Proc. 4th International Workshop on Analogical and Inductive Inference (AII'94) and the 5th International Workshop on Algorithmic Learning Theory (ALT'94), volume 872 of Lecture Notes in Artificial Intelligence, pages 439–453. Springer-Verlag, 1994.Google Scholar
- [MA93]Yasuhito Mukouchi and Setsuo Arikawa. Inductive inference machines that can refute hypothesis spaces. In K.P. Jantke, S. Kobayashi, E. Tomita, and T. Yokomori, editors, Proc. 4th Workshop on Algorithmic Learning Theory, (ALT'93), volume 744 of Lecture Notes in Artificial Intelligence, pages 123–136. Springer-Verlag, 1993.Google Scholar
- [Rog67]Hartley Rogers jr. Theory of Recursive Functions and Effective Computability. McGraw-Hill, 1967.Google Scholar
- [Wie76]Rolf Wiehagen. Limes-Erkennung rekursiver Funktionen durch spezielle Strategien. Elektronische Informationsverarbeitung und Kybernetik, 12(1/2):93–99, 1976.Google Scholar
- [WL76]Rolf Wiehagen and Walter Liepe. Charakteristische Eigenschaften von erkennbaren Klassen rekursiver Funktionen. Elektronische Informationsverarbeitung und Kybernetik, 12(8/9):421–438, 1976.Google Scholar
- [WZ95]Rolf Wiehagen and Thomas Zeugmann. Learning and consistency. In K.P. Jantke and S. Lange, editors, Algorithmic Learning for Knowledge-Based Systems, volume 961 of Lecture Notes in Artificial Intelligence, pages 1–24. Springer-Verlag, 1995.Google Scholar