Koehler, Jana, Ottiger, Daniel
Not widely known by the AI community, elevator control has become a major field of application for AI technologies. Techniques such as neural networks, genetic algorithms, fuzzy rules and, recently, multiagent systems and AI planning have been adopted by leading elevator companies not only to improve the transportation capacity of conventional elevator systems but also to revolutionize the way in which elevators interact with and serve passengers. In this article, we begin with an overview of AI techniques adopted by this industry and explain the motivations behind the continuous interest in AI. In the second part, we present in more detail a recent development project to apply AI planning and multiagent systems to elevator control problems.
Long, Derek, Kautz, Henry, Selman, Bart, Bonet, Blai, Geffner, Hector, Koehler, Jana, Brenner, Michael, Hoffmann, Joerg, Rittinger, Frank, Anderson, Corin R., Weld, Daniel S., Smith, David E., Fox, Maria, Long, Derek
In 1998, the international planning community was invited to take part in the first planning competition, hosted by the Artificial Intelligence Planning Systems Conference, to provide a new impetus for empirical evaluation and direct comparison of automatic domain-independent planning systems. This article describes the systems that competed in the event, examines the results, and considers some of the implications for the future of the field.