By Jürgen Schmidhuber (auth.), Daniel Kudenko, Dimitar Kazakov, Eduardo Alonso (eds.)
Adaptive brokers and multi-agent platforms is an rising and fascinating interdisciplinary sector of analysis and improvement regarding man made intelligence, software program engineering, and developmental biology, in addition to cognitive and social science.
This booklet provides 17 revised and punctiliously reviewed papers taken from workshops at the subject in addition to 2 invited papers via top researchers within the zone. The papers care for numerous facets of computer studying, model, and evolution within the context of agent structures and self sustaining agents.
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Extra info for Adaptive Agents and Multi-Agent Systems II: Adaptation and Multi-Agent Learning
Yet, if we understand the language (sometimes, in the case of scientiﬁc discourse, a language full of specialized words and jargon), we often become blind to the extent in which this contextual frame qualiﬁes the entire message. This is no secret to computer scientists, who have been struggling with natural language processing for decades. One could say that context is “the stuﬀ meaning is made of”. Thoughts are also an object of pure context. They are never abstract - You always think of something, a certain object.
Our multi-agent system has been designed to test the theory that language could potentially have evolved from the neural mechanisms our ancestors used to navigate [4, 5, 6, 7]. Speciﬁcally we wish to explore the feasibility of the hypothesis that the original task of speech in humans was to inform others about the D. Kudenko et al. ): Adaptive Agents and MAS II, LNAI 3394, pp. 41–54, 2005. c Springer-Verlag Berlin Heidelberg 2005 42 M. Bartlett and D. Kazakov geography of the environment. To this end, we have constructed an artiﬁcial-life environment in which a society of learning agents uses speech to direct others to resources vital to survival using the same underlying computational mechanism as they use to navigate.
This is done by simulating a “mechanical system” in which the nodes are subjected to “forces”: to ensure the spread, the nodes repel each other, and to enforce the relations, the links act as rubber bands, bringing linked nodes closer together. In order to generate a view of the graph, the nodes are placed in space, and the mechanical system moves realistically across the screen until a dynamic equilibrium of nodes’ positions is reached. When the user interacts with the system, the forces of repulsion/attraction are recalculated according to the changes he introduced, and the nodes are freed to accelerate and move around as dictated by the forces.
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