CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
Combining Symbolic and Neural Learning
Machine Learning
Extraction of rules from discrete-time recurrent neural networks
Neural Networks
Machine Learning - Special issue on inductive transfer
Computer science as empirical inquiry: symbols and search
Communications of the ACM
CloseGraph: mining closed frequent graph patterns
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Iterated learning: a framework for the emergence of language
Artificial Life
Cross-domain knowledge transfer using structured representations
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
The evolution of communication systems by adaptive agents
Adaptive agents and multi-agent systems
The Iterated Classification Game: A New Model of the Cultural Transmission of Language
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
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We suggest that the primary motivation for an agent to construct a symbol-meaning mapping is to solve a task. The meaning space of an agent should be derived from the tasks that it faces during the course of its lifetime. We outline a process in which agents learn to solve multiple tasks and extract a store of “cumulative knowledge” that helps them to solve each new task more quickly and accurately. This cumulative knowledge then forms the ontology or meaning space of the agent. We suggest that by grounding symbols to this extracted cumulative knowledge agents can gain a further performance benefit because they can guide each others' learning process. In this version of the symbol grounding problem meanings cannot be directly communicated because they are internal to the agents, and they will be different for each agent. Also, the meanings may not correspond directly to objects in the environment. The communication process can also allow a symbol meaning mapping that is dynamic. We posit that these properties make this version of the symbol grounding problem realistic and natural. Finally, we discuss how symbols could be grounded to cumulative knowledge via a situation where a teacher selects tasks for a student to perform.