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  Coherent Inference on Optimal Play in Game Trees

Hennig, P., Stern, D., & Graepel, T. (2010). Coherent Inference on Optimal Play in Game Trees. In Y. Teh, & M. Titterington (Eds.), JMLR Workshop and Conference Proceedings (pp. 326-333). Cambridge, MA, USA: JMLR.

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 Creators:
Hennig, P1, Author           
Stern, D, Author
Graepel, T, Author
Affiliations:
1Cavendish Laboratory, Cambridge UK, ou_persistent22              

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 Abstract: Round-based games are an instance of discrete planning problems. Some of the best contemporary game tree search algorithms use random roll-outs as data. Relying on a good policy, they learn on-policy values by propagating information upwards in the tree, but not between sibling nodes. Here, we present a generative model and a corresponding approximate message passing scheme for inference on the optimal, off-policy value of nodes in smooth AND/OR trees, given random roll-outs. The crucial insight is that the distribution of values in game trees is not completely arbitrary. We define a generative model of the on-policy values using a latent score for each state, representing the value under the random roll-out policy. Inference on the values under the optimal policy separates into an inductive, pre-data step and a deductive, post-data part. Both can be solved approximately with Expectation Propagation, allowing off-policy value inference for any node in the (exponentially big) tree in linear time.

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 Dates: 2010-05
 Publication Status: Issued
 Pages: -
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 Rev. Type: -
 Identifiers: BibTex Citekey: HennigSG2010
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Title: Thirteenth International Conference on Artificial Intelligence and Statistics (AI & Statistics 2010)
Place of Event: Chia Laguna Resort, Italy
Start-/End Date: 2010-05-13 - 2010-05-15

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Title: JMLR Workshop and Conference Proceedings
Source Genre: Proceedings
 Creator(s):
Teh, YW, Editor
Titterington, M, Editor
Affiliations:
-
Publ. Info: Cambridge, MA, USA : JMLR
Pages: - Volume / Issue: 9 Sequence Number: - Start / End Page: 326 - 333 Identifier: -