Comparative Analysis of Frameworks for Knowledge-Intensive Intelligent Agents

maintain awareness of its environment for a long period of time. Additionally, knowledge-intensive agents must be engineered such that their knowledge can be easily updated as environment and task requirements change during deployment. This paper discusses representations and processes for agents and behavior models that encode large knowledge bases, are long-lived, and exhibit high degrees of competence and flexibility while interacting with complex environments. There are many different approaches to building such agents, and understanding the important commonalities and differences between approaches is often difficult. We introduce a new approach to comparing approaches based on the notions of deliberate commitment, reconsideration, and a categorization of representations. We review three agent frameworks, concentrating on the major representations and processes each directly supports. By organizing the approaches according to a common nomenclature, the analysis highlights points o...

• Type: Journal
• Year: 2006
• Where: AIM
• Authors: Randolph M. Jones, Robert E. Wray III
• Keywords: Agent Frameworks, AIM 2006, Large Knowledge Bases, Long Period, Software Engineering