Towards an Indexical Model of Situated Language Comprehension for Real-World Cognitive Agents

We propose a computational model of situated language comprehension based on the Indexical Hypothesis that generates meaning representations by translating amodal linguistic symbols to modal representations of beliefs, knowledge, and experience external to the linguistic system. The model incorporates multiple information sources including perceptions, domain knowledge, and short and long-term experiences during comprehension. We show that exploiting diverse information sources can alleviate ambiguities that arise from contextual use of under-specific referring expressions and unexpressed argument alternations of verbs. The model is being used for supporting linguistic interactions in Rosie, an agent implemented in Soar that learns from instruction.