Jason Riggle

Center for Applied Linguistics, University of California, Los Angeles

Papers

Total Citations

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About

Jason Riggle’s research lies at the intersection of computational linguistics, cognitive science, and artificial intelligence, with a particular focus on how language and concepts emerge from adaptive, interactive systems. His most influential work, “The Learning and Emergence of Mildly Context Sensitive Languages” (2003, 11 citations), explores how complex syntactic structures can be learned and arise naturally in artificial agents, offering insights into the computational foundations of human language. In a series of studies on adaptive communication among collaborative agents (2004, 7 and 2 citations), Riggle investigates symbol grounding—how agents develop shared meanings through interaction, a fundamental challenge for AI and robotics. His work on “Induction of Prototypes in a Robotic Setting Using Local Search MDL” (2004, 3 citations) applies Minimum Description Length (MDL) learning to enable robots to categorize objects, forming prototype-based concepts that support more sophisticated environmental interaction. Though his citation counts are modest, Riggle’s contributions are notable for bridging formal language theory, machine learning, and embodied cognition, providing a framework for understanding how intelligent systems can autonomously develop linguistic and conceptual structures. His research remains relevant for scholars exploring emergent communication, cognitive robotics, and the learning of hierarchical grammars.