@inproceedings{8b2ec4f750c94dc691aedc6326f7e724,

title = "Example-guided optimization of recursive domain theories",

abstract = "The authors investigate the utility of explanation-based learning in recursive domain theories and examine the cost of using macro-rules in these theories. As a first step in producing effective explanation-based generalization (EBG) algorithms, the authors present a new algorithm for performing source optimization of recursive domain theories. The algorithm, RSG (recursive-structure generalizer), uses a training example as bias and generalizes the control knowledge encoded in the example's derivation tree to produce a more efficient formulation of the original domain theory. The control knowledge involves control of both clause and binding selection. The authors demonstrate the effectiveness of the method of planning problems in situation calculus. The authors show that in most cases one must know the future problem distribution a priori to produce an optimal reformulation.",

author = "Ronen Feldman and Devika Subramanian",

year = "1990",

month = feb,

language = "American English",

isbn = "0818621354",

series = "Proceedings of the Conference on Artificial Intelligence Applications",

publisher = "Publ by IEEE",

pages = "240--244",

booktitle = "Proceedings of the Conference on Artificial Intelligence Applications",

note = "Proceedings of the 7th IEEE Conference on Artificial Intelligence Applications ; Conference date: 24-02-1991 Through 28-02-1991",

}