Download Algorithmic Learning Theory: 9th International Conference, by Michael M. Richter, Carl H. Smith, Rolf Wiehagen, Thomas PDF

By Michael M. Richter, Carl H. Smith, Rolf Wiehagen, Thomas Zeugmann

This quantity includes the entire papers offered on the 9th foreign Con- rence on Algorithmic studying conception (ALT’98), held on the eu schooling centre Europ¨aisches Bildungszentrum (ebz) Otzenhausen, Germany, October eight{ 10, 1998. The convention used to be backed through the japanese Society for Arti cial Intelligence (JSAI) and the college of Kaiserslautern. Thirty-four papers on all elements of algorithmic studying idea and similar parts have been submitted, all electronically. Twenty-six papers have been accredited via this system committee in line with originality, caliber, and relevance to the speculation of laptop studying. also, 3 invited talks provided via Akira Maruoka of Tohoku college, Arun Sharma of the college of recent South Wales, and Stefan Wrobel from GMD, respectively, have been featured on the convention. we wish to precise our honest gratitude to our invited audio system for sharing with us their insights on new and interesting advancements of their parts of study. This convention is the 9th in a sequence of annual conferences demonstrated in 1990. The ALT sequence makes a speciality of all parts relating to algorithmic studying thought together with (but now not restricted to): the speculation of laptop studying, the layout and research of studying algorithms, computational good judgment of/for laptop discovery, inductive inference of recursive features and recursively enumerable languages, studying through queries, studying by means of arti cial and organic neural networks, development popularity, studying by means of analogy, statistical studying, Bayesian/MDL estimation, inductive common sense programming, robotics, program of studying to databases, and gene analyses.

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Extra info for Algorithmic Learning Theory: 9th International Conference, ALT’98 Otzenhausen, Germany, October 8–10, 1998 Proceedings

Example text

Proof Sketch. This simulation proof is related to the proof of Freivalds and Wiehagen that every computable function can be learned from an upper bound of any of its indices [6,7]. Let M be an ArbMa-learner. Furthermore, for any given tree select that infinite branch which has among all computable infinite branches the smallest minimal index, say e. Now this branch f can be inferred as follows: The new machine N emulates M on f [n] and receives a sequence (en )n∈ω of hypotheses which converges to an index e of some branch of the tree.

Thus, to identify a master is a proper restriction for both learning from arbitrary and learning from selected masters. This shows that the advantage in watching one master (rather than none) comes from ones creating ones own winning strategy, and not from being a copycat. This result is not as surprising for ArbMa since one can imagine masters who go out of their way to avoid being figured out. But for the selective version of master learning this result is much more interesting. It says that regardless of how skilled pedagogically is the selected master you are watching, if one can learn a winning strategy from him/her/it, then this is, in general, only possible by creating a new strategy which differs from that of the master.

ALT’98, LNAI 1501, pp. 31–45, 1998. c Springer-Verlag Berlin Heidelberg 1998 32 J. Case et al. 1 We do not have much access to the masters’ actual strategic programs mostly stored in their subconscious wetware. We have, instead, access to their game-playing behavior. It is also apparently useful to study the (gameplaying) behavior of masters who play with very different styles. For example, it is likely better to study the behavior of both Kasparov and Deep Blue2 than to study only one of them.

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