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.

本文介绍了知识工程的原理和方法，使得开发跨领域和应用的语言能力智能体成为可能。我们以对话行为建模为例，描述了基于 OntoAgent 知识中心的认知架构的综合方法，并突出了过去方法的局限性，这些方法将对话与其他代理功能隔离开来。

人工智能长期游戏中的知识工程：基于言语行为的案例研究

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

This paper describes recent progress on natural language generation (NLG) for
language-endowed intelligent agents (LEIAs) developed within the OntoAgent
cognitive architecture. The approach draws heavily from past work on natural
language understanding in this paradigm: it uses the same knowledge bases,
theory of computational linguistics, agent architecture, and methodology of
developing broad-coverage capabilities over time while still supporting
near-term applications.

本文描述了 OntoAgent 认知架构内开发的面向智能代理的自然语言生成（NLG）的最新进展，该方法在计算语言学的知识库、代理架构和方法上深受过去自然语言理解研究的启发，以实现长期面向广泛覆盖，同时支持近期应用。