An Approach Employing Polysemous Rules to Complement Legacy Rules for the Elevator Operation

In this paper, an approach to complement legacy rules for the elevator operation is proposed. The approach is derived from the analysis that the elevator operation in the real world often obeys a heuristic rule, and such a rule can be divided into a legacy rule and ad-hoc rules. In the approach, ad-hoc rules are represented as polysemous rules, and a Genetics-Based Machine Learning (GBML) method is applied to acquire such rules. Here, a polysemous rule encodes, not a set of environments’ states as the well-known if-then rule does, but a relative attribute vector of an arbitrary elevator. The elevator selection rule based on polysemous rules is simple: if there is a polysemous rule which matches one of attribute vectors of the elevators, select the elevator which corresponds to the matching vector; otherwise select an elevator according to a legacy rule. In computer illustrations, the GBML method is applied to 3 traffic patterns formed by the system’s users. It is shown that the resultant polysemous rules seem to complement an existing (legacy) operational rule. Furthermore, polysemous rules, which are selected among those acquired by the GBML method, are successfully applied to harder problems with more elevators than those used in learning.