@article {18912,
	title = {Learning preconditions for planning from plan traces and HTN structure},
	journal = {Computational Intelligence},
	volume = {21},
	year = {2005},
	month = {2005///},
	pages = {388 - 413},
	abstract = {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{\textquoteright}s soundness, completeness, and convergence properties. (3) We present empirical results about CaMeL{\textquoteright}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{\textquoteright}s output can be useful even before it has fully converged.},
	keywords = {candidate elimination, HTN planning, learning, version spaces},
	isbn = {1467-8640},
	doi = {10.1111/j.1467-8640.2005.00279.x},
	url = {http://onlinelibrary.wiley.com/doi/10.1111/j.1467-8640.2005.00279.x/abstract},
	author = {Ilghami,Okhtay and Nau, Dana S. and Mu{\~n}oz-Avila,H{\'e}ctor and Aha,David W.}
}
