TY - JOUR
T1 - Learning preconditions for planning from plan traces and HTN structure
JF - Computational Intelligence
Y1 - 2005
A1 - Ilghami,Okhtay
A1 - Nau, Dana S.
A1 - Muñoz-Avila,Héctor
A1 - Aha,David W.
KW - candidate elimination
KW - HTN planning
KW - learning
KW - version spaces
AB - A great challenge in developing planning systems for practical applications is the difficulty of acquiring the domain information needed to guide such systems. This paper describes a way to learn some of that knowledge. More specifically, the following points are discussed. (1) We introduce a theoretical basis for formally defining algorithms that learn preconditions for Hierarchical Task Network (HTN) methods. (2) We describe Candidate Elimination Method Learner (CaMeL), a supervised, eager, and incremental learning process for preconditions of HTN methods. We state and prove theorems about CaMeL's soundness, completeness, and convergence properties. (3) We present empirical results about CaMeL's convergence under various conditions. Among other things, CaMeL converges the fastest on the preconditions of the HTN methods that are needed the most often. Thus CaMeL's output can be useful even before it has fully converged.
VL - 21
SN - 1467-8640
UR - http://onlinelibrary.wiley.com/doi/10.1111/j.1467-8640.2005.00279.x/abstract
CP - 4
M3 - 10.1111/j.1467-8640.2005.00279.x
ER -