Discovering Relational Domain Features for Probabilistic Planning

By Jia-Hong Wu, Robert Givan. 2007

15 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Sriraam Natarajan
2. Martijn van Otterlo
3. Srijita Das
4. Scott Sanner
5. Kristian Kersting
6. Ronald E. Parr
7. Kaushik Roy
8. Craig Boutilier

Citations in the corpus

• Fitted Q-Learning for Relational Domains (2020, background): “Wu and Givan (2007) approximate the value function by using beam search with relational features that are iteratively learned from sampled trajectories.”
• Solving Relational and First-Order Logical Markov Decision Processes: A Survey (2012, methodology): “method representation type algorithm applications IDP (van Otterlo, 2009b) any E IDP — SDP (Boutilier et al, 2001) FOL E IDP logistics ReBel (Kersting et al, 2004) Conj E IDP BW, logistics FOVI (Hölldobler and Skvortsova, 2004) Conj E IDP BW FODD (Wang et al, 2008a) FO-ADD E IDP BW FODD (Wang and Kh”
• Practical solution techniques for first-order MDPs (2009, result): “Empirically, recent results ( Wu and Givan, 2007 ) show that inductive first-order approaches outperform FOALP.”