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Titlebook: Symbol Grounding and Beyond; Third International Paul Vogt,Yuuya Sugita,Chrystopher Nehaniv Conference proceedings 2006 Springer-Verlag Be

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樓主: Tyler
21#
發(fā)表于 2025-3-25 03:36:10 | 只看該作者
Evolving Distributed Representations for Language with Self-Organizing Maps,g. Both symbol sequences and propositional meanings are represented by high-dimensional vectors of real numbers. A neural network learns to map between the distributed representations of the symbol sequences and the distributed representations of the propositions. Unlike previous neural network mode
22#
發(fā)表于 2025-3-25 10:40:55 | 只看該作者
23#
發(fā)表于 2025-3-25 15:12:08 | 只看該作者
How Grammar Emerges to Dampen Combinatorial Search in Parsing,ssues in communication among autonomous agents, particularly maximisation of communicative success and expressive power and minimisation of cognitive effort. Experiments in the emergence of grammar should hence start from a simulation of communicative exchanges between embodied agents, and then show
24#
發(fā)表于 2025-3-25 19:54:15 | 只看該作者
25#
發(fā)表于 2025-3-25 21:23:11 | 只看該作者
26#
發(fā)表于 2025-3-26 00:38:05 | 只看該作者
Operational Aspects of the Evolved Signalling Behaviour in a Group of Cooperating and Communicatingcts of the evolved behaviour of a group of robots equipped with a different set of sensors, that navigates towards a target in a walled arena. In particular, analysis of the sound signalling behaviour shows that the robots employ the sound to remain close to each other at a safe distance with respec
27#
發(fā)表于 2025-3-26 05:53:47 | 只看該作者
Propositional Logic Syntax Acquisition,ch as invention, adoption, parsing, generation and induction is proposed. Self-organisation principles are used to show how a shared set of preferred lexical entries and grammatical constructions, i.e., a ., can emerge in a population of autonomous agents which do not have any initial linguistic kno
28#
發(fā)表于 2025-3-26 09:23:44 | 只看該作者
Robots That Learn Language: Developmental Approach to Human-Machine Conversations, nonverbal interaction with users. The method focuses on two major problems that should be pursued to realize natural human-machine conversation: a scalable grounded symbol system and belief sharing. The learning is performed in the process of joint perception and joint action with a user. The metho
29#
發(fā)表于 2025-3-26 15:44:18 | 只看該作者
30#
發(fā)表于 2025-3-26 17:30:57 | 只看該作者
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