Enhancing Intelligent Agents with Episodic Memory.

In this dissertation, we explore the effects of adding an episodic memory to an intelligent agent. First, we define the design space for episodic memory systems and the properties that any implementation must have in order to be integrated into a cognitive architecture. We then describe our exploration of this space including two major implementations of an architectural episodic memory as well as several refinements to those implementations and their impact on agent performance. We also present a series of cognitive capabilities that are facilitated by virtue of an agent possessing an episodic memory. We hypothesize that these capabilities improve an agent’s ability to effectively sense its environment, reason and learn. We then demonstrate five of these cognitive capabilities using a specific task in one of two different virtual environments.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/57720/2/anuxoll_1.pd

Hash Functions for Episodic Recognition and Retrieval

Episodic memory systems for artificially intelligent agents must cope with an ever-growing episodic memory store. This paper presents an approach for minimizing the size of the store by using specialized hash functions to convert each memory into a relatively short binary code. A set of desiderata for such hash functions are presented including locale sensitivity and reversibility. The paper then introduces multiple approaches for such functions and compares their effectiveness