Abstract
The paper describes the prototype HAP - heuristic assembly planner and tries to explain current ideas about how to extend the present search algorithms using principles of AI - machine learning. HAP planner uses AO* based heuristic search through AND/OR graph representing all possible assembly sequences. The main problem of this approach was shown to be its computational complexity, which was somehow limited by developing efficient algorithm for generating feasible assembly sequences. Still, when the number of such sequences are to big, the search may not be efficient. To cope with this, we present the idea about how the system can learn the "assembly rules" from previous planning and how such a knowledge can be used when searching for the optimal assembly sequence of a new assembly.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
