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Abstract
The human acquisition of human languages is is based on the analysis of signal and context. To study how this might work, a simplified robotic setting is described in which the problem is divided into two basic steps: an analysis of the linguistic events in context that yields dependency structures, and the identification of grammars that generate those structures. A learnability result that generalizes (Kanazawa 1994) has been obtained, showing that non-CF, and even non-TAG languages can be identified in this setting, and more realistic assessments of the learning problem are under study.

Biography
Stabler is Professor of Linguistics at UCLA. He specializes in theories of human language processing and formal learnability theory, with interests in automated theorem proving, philosophy of logic and language, and artificial life.