Learning Hierarchical Problem Networks for Knowledge-Based Planning

Author:

Langley Pat

Publisher

Springer Nature Switzerland

Reference19 articles.

1. Cropper, A., Muggleton, S.H.: Learning efficient logical robot strategies involving composable objects. In: Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, Buenos Aires, Argentina, pp. 3423–3429. AAAI Press (2015)

2. Fine-Morris, M., et al.: Learning hierarchical task networks with landmarks and numeric fluents by combining symbolic and numeric regression. In: Proceedings of the Eighth Annual Conference on Advances in Cognitive Systems. Cognitive Systems Foundation (2020)

3. Hogg, C., Muñoz-Avila, H., Aha, D.W.: HTN-Maker: learning HTNs with minimal additional knowledge engineering required. In: Proceedings of the Twenty-Third National Conference on Artificial Intelligence. AAAI Press (2008)

4. Iba, G.A.: A heuristic approach to the discovery of macro-operators. Mach. Learn. 3, 285–317 (1989)

5. Ilghami, O., Nau, D.S., Muñoz-Avila, H., Aha, D.W.: CaMeL: learning method preconditions for HTN planning. In: Proceedings of the Sixth International Conference on AI Planning and Scheduling, Toulouse, France, pp. 131–141. AAAI Press (2002)

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