From theory to practice: AI planning for high performance elevator control

Abstract

Offering an individually tailored service to passengers while maintaining a high transportation capacity of an elevator group is an upcoming challenge in the elevator business, which cannot be met by software methods traditionally used in this industry. AI planning offers a novel solution to these control problems: (1) by synthesizing the optimal control for any situation occurring in a building based on fast search algorithms, (2) by implementing a domain model, which allows to easily add new features to the control software. By embedding the planner into a multi-agent system, real-time interleaved planning and execution is implemented and results in a high-performing, self-adaptive, and modular control software.