523 research outputs found
Learning a world model and planning with a self-organizing, dynamic neural system
We present a connectionist architecture that can learn a model of the
relations between perceptions and actions and use this model for behavior
planning. State representations are learned with a growing self-organizing
layer which is directly coupled to a perception and a motor layer. Knowledge
about possible state transitions is encoded in the lateral connectivity. Motor
signals modulate this lateral connectivity and a dynamic field on the layer
organizes a planning process. All mechanisms are local and adaptation is based
on Hebbian ideas. The model is continuous in the action, perception, and time
domain.Comment: 9 pages, see http://www.marc-toussaint.net
Self-adaptive exploration in evolutionary search
We address a primary question of computational as well as biological research
on evolution: How can an exploration strategy adapt in such a way as to exploit
the information gained about the problem at hand? We first introduce an
integrated formalism of evolutionary search which provides a unified view on
different specific approaches. On this basis we discuss the implications of
indirect modeling (via a ``genotype-phenotype mapping'') on the exploration
strategy. Notions such as modularity, pleiotropy and functional phenotypic
complex are discussed as implications. Then, rigorously reflecting the notion
of self-adaptability, we introduce a new definition that captures
self-adaptability of exploration: different genotypes that map to the same
phenotype may represent (also topologically) different exploration strategies;
self-adaptability requires a variation of exploration strategies along such a
``neutral space''. By this definition, the concept of neutrality becomes a
central concern of this paper. Finally, we present examples of these concepts:
For a specific grammar-type encoding, we observe a large variability of
exploration strategies for a fixed phenotype, and a self-adaptive drift towards
short representations with highly structured exploration strategy that matches
the ``problem's structure''.Comment: 24 pages, 5 figure
Neutrality: A Necessity for Self-Adaptation
Self-adaptation is used in all main paradigms of evolutionary computation to
increase efficiency. We claim that the basis of self-adaptation is the use of
neutrality. In the absence of external control neutrality allows a variation of
the search distribution without the risk of fitness loss.Comment: 6 pages, 3 figures, LaTe
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