One of AI’s grand challenges consists in the development of autonomous agents with communication systems offering the robustness, flexibility and adaptivity found in human languages. While the processes that drive language acquisition in children have been extensively studied, faithful computational operationalisations of the underlying mechanisms are still lacking. Recently, a mechanistic model of the cognitive capacities of intention reading and pattern finding has shown, on a limited scale, how autonomous agents can self-organise a linguistic system through communicative interactions. In this project, we tackle fundamental challenges that remain to extend this mechanistic model to a larger scale. This involves the development of more efficient algorithms for overcoming the intractability of intention reading, and more generally applicable pattern finding algorithms for computing generalisations over formal representations of utterances and meanings. The outcome of this project will have a major impact in construction grammar, usage-based linguistics, and artificial intelligence. Concretely, a computational model of how an autonomous agent can learn a sophisticated communication system, in the form of a construction grammar, completely bottom-up in a usage-based fashion, will constitute a major breakthrough that is applicable in a wide range of language technology applications and meaning-based AI systems, such as human-robot interfaces and conversational agents.
|Effective start/end date
|1/10/23 → 30/09/24
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