Remembering A Conversation - A Conversational Memory Architecture For Embodied Conversational Agents

Keywords

Chatbots; chatterbots; conversational agents; conversational memory; Embodied Conversational Agents; episodic memory; episodic memory architecture

Abstract

This paper addresses the role of conversational memory in Embodied Conversational Agents (ECAs). It describes an investigation into developing such a memory architecture and integrating it into an ECA. ECAs are virtual agents whose purpose is to engage in conversations with human users, typically through natural language speech. While several works in the literature seek to produce viable ECA dialog architectures, only a few authors have addressed the episodic memory architectures in conversational agents and their role in enhancing their intelligence. In this work, we propose, implement, and test a unified episodic memory architecture for ECAs. We describe a process that determines the prevalent contexts in the conversations obtained from the interactions. The process presented demonstrates the use of multiple techniques to extract and store relevant snippets from long conversations, most of whose contents are unremarkable and need not be remembered. The mechanisms used to store, retrieve, and recall episodes from previous conversations are presented and discussed. Finally, we test our episodic memory architecture to assess its effectiveness. The results indicate moderate success in some aspects of the memory-enhanced ECAs, as well as some work still to be done in other aspects.

Publication Date

1-1-2017

Publication Title

Journal of Intelligent Systems

Volume

26

Issue

1

Number of Pages

1-21

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1515/jisys-2015-0094

Socpus ID

85008173430 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85008173430

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