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

Statistical mechanics of interacting fiber bundles

We consider quasistatic fiber bundle models with interactions. Classical load sharing rules are considered, i.e. local, global or decaying as a power-law of distance. All fibers are identically elastic, initially intact, and break at a random threshold picked from a quenched disorder (q.d.) distribution. We are interested in the probability distribution of configurations of broken fibers at a given elongation, averaged over all possible realizations of the underlying q.d.. This distribution is accessed by mapping the threshold set space onto the configurational space, each path corresponding to the evolution of a bundle corresponding to a realized q.d.. Using a perturbative approach allows to obtain this distribution to leading order in the interactions. This maps this system onto classical statistical mechanics models, i.e. percolation, standard or generalized Ising models depending on the range of the interactions chosen in the load sharing rule. This relates such q.d. based systems to standard classical mechanics, which allows to derive observables of the system, as e.g. correlation lengths. The thermodynamic parameters formally equivalent to temperature and chemical potential, are functions of the externally imposed deformation, depending on the load sharing rule and the choice of the q.d. distribution

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

FINANCING AGRICULTURAL ECONOMICS RESEARCH AND EXTENSION IN THE SOUTHERN REGION

Teaching/Communication/Extension/Profession,

Special features of the transmission systems of the satellite ESRO 2/IRIS

Telemetry system used by satellites to transmit observation to groun