AAAI Publications, Ninth Artificial Intelligence and Interactive Digital Entertainment Conference

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Modeling Autobiographical Memory for Believable Agents
Andrew Kope, Caroline Rose, Michael Katchabaw

Last modified: 2013-11-13

Abstract


We present a multi-layer hierarchical connectionist network model for simulating human autobiographical memory in believable agents. Grounded in psychological theory, this model improves on previous attempts to model agents’ event knowledge by providing a more dynamic and non-deterministic representation of autobiographical memories. From this model, a Java-based proof-of-concept prototype system was created for use as an enabling technology in video games. This prototype was leveraged in the creation of a Minecraft modification (mod) implementation of the model that is able to demonstrate context-dependent recall and the effects of recency on memory recall. Wider implications of the model in agent and game design are discussed.

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