Ádám Rotaru-Varga

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A Hebbian-inspired, competitive network is presented which learns to predict the typical semantic features of denoting terms in simple and moderately complex sentences. In addition, the network learns to predict the appearance of syntactically key words, such as prepositions and relative pronouns. Importantly , as a by-product of the network's semantic(More)
We analyze the effect of different genetic encodings used for evolving three-dimensional agents with physical morphologies. The complex phenotypes used in such systems often require nontrivial encodings. Different encodings used in Framsticks--a system for evolving three-dimensional agents--are presented. These include a low-level direct mapping and two(More)
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