Algorithmic Learning Theory: 12th International Conference, by Naoki Abe, Roni Khardon, Thomas Zeugmann

By Naoki Abe, Roni Khardon, Thomas Zeugmann

This quantity comprises the papers awarded on the twelfth Annual convention on Algorithmic studying thought (ALT 2001), which used to be held in Washington DC, united states, in the course of November 25–28, 2001. the most goal of the convention is to supply an inter-disciplinary discussion board for the dialogue of theoretical foundations of computer studying, in addition to their relevance to useful purposes. The convention used to be co-located with the Fourth foreign convention on Discovery technology (DS 2001). the amount contains 21 contributed papers. those papers have been chosen by means of this system committee from forty two submissions in line with readability, signi?cance, o- ginality, and relevance to idea and perform of computing device studying. also, the amount includes the invited talks of ALT 2001 awarded through Dana Angluin of Yale collage, united states, Paul R. Cohen of the college of Massachusetts at Amherst, united states, and the joint invited speak for ALT 2001 and DS 2001 provided through Setsuo Arikawa of Kyushu collage, Japan. in addition, this quantity contains abstracts of the invited talks for DS 2001 provided via Lindley Darden and Ben Shneiderman either one of the collage of Maryland in school Park, united states. the full models of those papers are released within the DS 2001 court cases (Lecture Notes in Arti?cial Intelligence Vol. 2226).

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Extra info for Algorithmic Learning Theory: 12th International Conference, ALT 2001 Washington, DC, USA, November 25–28, 2001 Proceedings

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Robot Baby 2001 37 1. Learn representations s ∼ S 2. Learn functions f (s, a) → S This strategy is almost certainly wrong because, as we said, s is supposed to inform actions a, so should be learned while trying to act. Researchers do not always follow the best strategy (we don’t, anyway) and the divide and conquer strategy we did follow has produced an unexpected dividend: We now think it is possible that some kinds of structural abstraction are generally useful, that is, they inform large classes of actions.

We want to do away with the domain expert and the en1 An early effort in our laboratory to automate applied statistics was Rob St. , exploring high leverage points in regression analysis). R. Cohen et al. gineers/statistical consultants, and have programs learn representions and their meanings, autonomously. One impediment to learning the meanings of representations is the fuzziness of commonsense notions of meaning. 0001. One meaning of this representation is provided by classical inferential statistics: x and y appear linearly related and the relationship between these random variables is very unlikely to be accidental.

New Generation Computing, 8(4):337–348, 1991. 19. L. G. Valiant. A theory of the learnable. Commun. ACM, 27:1134–1142, 1984. Robot Baby 2001 Paul R. Cohen1 , Tim Oates2 , Niall Adams3 , and Carole R. edu Abstract. In this paper we claim that meaningful representations can be learned by programs, although today they are almost always designed by skilled engineers. We discuss several kinds of meaning that representations might have, and focus on a functional notion of meaning as appropriate for programs to learn.

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