A Machine with Short-Term, Episodic, and Semantic Memory Systems

Inspired by the cognitive science theory of the explicit human memory systems, we have modeled an agent with short-term, episodic, and semantic memory systems, each of which is modeled with a knowledge graph. To evaluate this system and analyze the behavior of this agent, we designed and released ou...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2024-11
Main Authors: Kim, Taewoon, Cochez, Michael, François-Lavet, Vincent, Neerincx, Mark, Vossen, Piek
Format: Article
Language:English
Subjects:
Knowledge representation
Machine learning
Semantics
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Inspired by the cognitive science theory of the explicit human memory systems, we have modeled an agent with short-term, episodic, and semantic memory systems, each of which is modeled with a knowledge graph. To evaluate this system and analyze the behavior of this agent, we designed and released our own reinforcement learning agent environment, "the Room", where an agent has to learn how to encode, store, and retrieve memories to maximize its return by answering questions. We show that our deep Q-learning based agent successfully learns whether a short-term memory should be forgotten, or rather be stored in the episodic or semantic memory systems. Our experiments indicate that an agent with human-like memory systems can outperform an agent without this memory structure in the environment.
ISSN:2331-8422
DOI:10.48550/arxiv.2212.02098