Finding Diverse High-Quality Plans for Hypothesis Generation
Shirin Sohrabi, Anton Riabov, Octavian Udrea, Oktie Hassanzadeh · Frontiers in artificial intelligence and applications · 2016
In this paper, we address the problem of finding diverse high-quality plans motivated by the hypothesis generation problem. To this end, we present a planner called TK*that first efficiently solves the “top-k” cost-optimal planning problem to find k best plans, followed by clustering to produce diverse plans as cluster representatives.