Knowledge Engineering in the Long Game of Artificial Intelligence: The Case of Speech Acts

TL;DR

This paper discusses principles of knowledge engineering for creating versatile, lifelong learning intelligent agents with a focus on dialog act modeling within a comprehensive cognitive architecture.

Contribution

It introduces an integrative approach based on OntoAgent, emphasizing holistic agent functionalities and lifelong learning in dialog modeling.

Findings

Highlights limitations of isolated dialog systems

Proposes an integrated, knowledge-centric architecture

Supports lifelong learning and domain versatility

Abstract

This paper describes principles and practices of knowledge engineering that enable the development of holistic language-endowed intelligent agents that can function across domains and applications, as well as expand their ontological and lexical knowledge through lifelong learning. For illustration, we focus on dialog act modeling, a task that has been widely pursued in linguistics, cognitive modeling, and statistical natural language processing. We describe an integrative approach grounded in the OntoAgent knowledge-centric cognitive architecture and highlight the limitations of past approaches that isolate dialog from other agent functionalities.