Event Memory for Intelligent Agents

Abstract

This dissertation presents a novel theory of event memory along with an associated computational model that embodies the claims of view which is integrated within a cognitive architecture. Event memory is a general-purpose storage for personal past experience. Literature on event memory reveals that people can remember events by both the successful retrieval of specific representations from memory and the reconstruction of events via schematic representations. Prominent philosophical theories of event memory, i.e., causal and simulationist theories, fail to capture both capabilities because of their reliance on a single representational format. Consequently, they also struggle with accounting for the full range of human event memory phenomena. In response, we propose a novel view that remedies these issues by unifying the representational commitments of the causal and simulation theories, thus making it a hybrid theory. We also describe an associated computational implementation of the proposed theory and conduct experiments showing the remembering capabilities of our system and its coverage of event memory phenomena. Lastly, we discuss our initial efforts to integrate our implemented event memory system into a cognitive architecture, and situate a tool-building agent with this extended architecture in the Minecraft domain in preparation for future event memory research.