A Proposal for Intelligent Agents with Episodic Memory
  In the future we can expect that artificial intelligent agents, once
deployed, will be required to learn continually from their experience during
their operational lifetime. Such agents will also need to communicate with
humans and other agents regarding the content of their experience, in the
context of passing along their learnings, for the purpose of explaining their
actions in specific circumstances or simply to relate more naturally to humans
concerning experiences the agent acquires that are not necessarily related to
their assigned tasks. We argue that to support these goals, an agent would
benefit from an episodic memory; that is, a memory that encodes the agent's
experience in such a way that the agent can relive the experience, communicate
about it and use its past experience, inclusive of the agents own past actions,
to learn more effective models and policies. In this short paper, we propose
one potential approach to provide an AI agent with such capabilities. We draw
upon the ever-growing body of work examining the function and operation of the
Medial Temporal Lobe (MTL) in mammals to guide us in adding an episodic memory
capability to an AI agent composed of artificial neural networks (ANNs). Based
on that, we highlight important aspects to be considered in the memory
organization and we propose an architecture combining ANNs and standard
Computer Science techniques for supporting storage and retrieval of episodic
memories. Despite being initial work, we hope this short paper can spark
discussions around the creation of intelligent agents with memory or, at least,
provide a different point of view on the subject.
