Abstract

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 advicate real-world software environments, such as operating systems or databases, as domains for agent research. The cost, effort, and expertise required to develop and systematically 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 ina real world yet focus on core AI research issues. To support this claim, we describe our project to develop UNIX softbots (software robots) -- intelligent agnets that interact with UNIX. Existing softbots 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.

Citations