Oren Etzioni, Neal Lesh, and Richard Segal
AI is moving away from "toy tasks" such as block stacking towards real-world problems. This trend is positive, but the amount of preliminary groundwork required to tackle a real-world task can be staggering, particularly when developing an integrated agent architecture. To address this problem, we advocate real-world software environments, such as operating systems or databases, as domains for agent research. The cost, effort, and expertise required to develop and experiment with software agents is relatively low. Furthermore, software environments circumvent many thorny, but peripheral, research issues that are inescapable in other environments. Thus, software environments enable us to test agents in a real world yet focus on core AI research issues. To support this claim, we describe our project to develop UNIX1 softbots (software robots)--complete intelligent agents that interact with UNIX. Our fully-implemented softbot is able to accept a diverse set of high-level goals, generate and execute plans to achieve these goals in real time, and recover from errors when necessary.