@Article{koehler02a,
  author =	 {Jana Koehler and Daniel Ottiger},
  title =	 {An {AI}-Based Approach to Destination Control in
                  Elevators},
  googleid =	 {FCYZLWsSQ_0J:scholar.google.com/},
  journal =	 {{AI} Magazine},
  year =	 2002,
  volume =	 23,
  number =	 3,
  pages =	 {59--78},
  month =	 {Fall},
  abstract =	 {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. We
                  review and summarize publications that are not
                  easily accessible from the common AI sources. In the
                  second part, we present in more detail a recent
                  development project to apply AI planning and
                  multiagent systems to elevator control problems.},
  keywords =     {multiagent application},
  url =		 {http://jmvidal.cse.sc.edu/library/koehler02a.pdf},
  comment =	 {masrg},
  cluster = 	 {18249450366562805268}
}