TR announcement: reflective agent teams

[Frank Smieja]

The file beyer.teams.ps.Z is available for
copying from the Neuroprose repository:
	
Learning from Examples, Agent Teams and the Concept of Reflection (22 pages)
Uwe Beyer and Frank Smieja
GMD, Germany

ABSTRACT. Learning from examples has a number of distinct algebraic forms,
depending on what is to be learned from which available information.
One of these forms is $x \stackrel{G}{\rightarrow} y$, where the
input--output tuple $(x,y)$ is the available information, and $G$
represents the process determining the mapping from $x$ to $y$.
Various models, $y = f(x)$, of $G$ can be constructed using the
information from the $(x,y)$ tuples.  In general, and for real-world
problems, it is not reasonable to expect the exact representation of
$G$ to be found (i.e.\ a formula that is correct for all possible
$(x,y)$).  The modeling procedure involves finding a
satisfactory set of basis functions, their combination, a coding
for $(x,y)$ and then to adjust all free parameters in an approximation
process, to construct a final model.  The approximation process can
bring the accuracy of the model to a certain level, after which it
becomes increasingly expensive to improve further.  Further
improvement may be gained through constructing a number of agents
$\{\alpha\}$, each of which develops its own model $f_\alpha$. These 
may then be combined in a second modeling phase to synthesize a
{\it team\/} model.  If each agent has the ability of internal {\it
reflection\/} the combination in a team framework becomes more
profitable.  We describe reflection and the generation of a {\it
confidence\/} function: the agent's estimate of the correctness of
each of its predictions.  The presence of reflective information is
shown to increase significantly the performance of a team.


	-Frank Smieja

Gesellschaft fuer Mathematik und Datenverarbeitung (GMD)
GMD-FIT.KI.AS, Schloss Birlinghoven, 5205 St Augustin 1, Germany.
Tel: +49 2241-142214		email: smieja at gmd.de