I wrote my master thesis on “Automatic Selection of Pattern Collections for Domain Independent Planning” in the field of artificial intelligence at University of Basel. Below you can find the abstract as well as download links to the thesis.
Heuristic search with admissible heuristics is the leading approach to cost-optimal, domain-independent planning. Pattern database heuristics – a type of abstraction heuristics – are state-of-the-art admissible heuristics. Two recent pattern database heuristics are the iPDB heuristic by Haslum et al. and the PhO heuristic by Pommerening et al.
The iPDB procedure performs a hill climbing search in the space of pattern collections and evaluates selected patterns using the canonical heuristic. We apply different techniques to the iPDB procedure, improving its hill climbing algorithm as well as the quality of the resulting heuristic. The second recent heuristic – the PhO heuristic – obtains strong heuristic values through linear programming. We present different techniques to influence and improve on the PhO heuristic.
We evaluate the modified iPDB and PhO heuristics on the IPC benchmark suite and show that these abstraction heuristics can compete with other state-of-the-art heuristics in cost-optimal, domain-independent planning.