Effective and Efficient Memory for Generally Intelligent Agents

Intelligent systems with access to large stores of experience, or
memory, can draw upon and reason about this knowledge in a variety of
situations, such as to improve the efficacy of their learning,
decision-making, and actions in the world. However, little research
has examined the computational challenges that arise when real-time
agents require access to large stores of knowledge over long periods
of time.

This dissertation explores the computational trade-offs involved in
enhancing intelligent agents with effective and efficient memory. We
exploit general properties of environments, tasks, and agent cues in
order to develop scalable algorithms for episodic learning
(autobiographical memory); semantic learning (context-independent
store of facts and relations); and competence-preserving retention of
learned knowledge (policies to forget memories while maintaining task
performance). We evaluate these algorithms in Soar, a general
cognitive architecture, for hours-to-days of real-time execution and
demonstrate that agents with effective and efficient memory benefit
along numerous dimensions when tasked within a variety of problem
domains, including linguistics, planning, games, and mobile robotics.