Abstract of FKI-198-94

Document-Name:  fki-200-94.ps.gz
Title:		On Learning How to Learn Learning Strategies
Authors:	Juergen Schmidhuber 
Revision-Dates:	1994/11/24, 1995/01/31 
Category:	Technical Report (Forschungsberichte Künstliche Intelligenz)
Abstract:  This paper introduces the ``incremental self-improvement paradigm''.
           Unlike previous methods,  incremental self-improvement  encourages a
           reinforcement learning  system to improve  the way it learns, and to
           improve the way  it improves the way it learns,  without significant
           theoretical limitations -- the system  is able to ``shift its induc-
           tive bias'' in  a universal way.  Its major features are:  (1) There
           is no  explicit difference  between ``learning'', ``meta-learning'',
           and other kinds of  information  processing.  Using a Turing machine
           equivalent  programming  language,  the system  itself  occasionally
           executes  self-delimiting,  initially  highly  random ``self-modifi-
           cation programs'' which modify  the context-dependent  probabilities
           of  future  programs (including future  self-modification programs).
           (2) The system keeps  only those probability  modifications computed
           by ``useful'' self-modification  programs:  those which  bring about
           more payoff per time  than all previous  self-modification programs.
           (3) The computation  of payoff per time  takes into account  all the
           computation time required for learning -- the entire system  life is
           considered: boundaries between learning trials are ignored (if there
           are any). A particular implementation based on the novel paradigm is
           presented.  It is  designed to  exploit  what  conventional  digital
           machines are good at: fast storage addressing, arithmetic operations
           etc. Experiments illustrate the system's mode of operation.
Keywords:	self-improvement, self-reference, machine learning,
                reinforcement learning, introspection
Size:		20 pages
Language:	English
ISSN:		0941-6358
Copyright:	The ``Forschungsberichte Künstliche Intelligenz''
		series includes primarily preliminary publications,
		specialized partial results, and supplementary
		material. In the interest of a subsequent final
		publication these reports should not be copied. All
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		critical comments.

Gerhard Weiss