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» Planning with predictive state representations
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RECOMB
2004
Springer
14 years 10 months ago
Learning Regulatory Network Models that Represent Regulator States and Roles
Abstract. We present an approach to inferring probabilistic models of generegulatory networks that is intended to provide a more mechanistic representation of transcriptional regul...
Keith Noto, Mark Craven
ICML
2008
IEEE
14 years 10 months ago
On-line discovery of temporal-difference networks
We present an algorithm for on-line, incremental discovery of temporal-difference (TD) networks. The key contribution is the establishment of three criteria to expand a node in TD...
Takaki Makino, Toshihisa Takagi
NECO
2007
87views more  NECO 2007»
13 years 9 months ago
Reinforcement Learning State Estimator
cal networks in the learning of abstract and effector-specific representations of motor sequences. Neuroimage. 32, 714-727. (Neuroimage Editor’s Choice Award, 2006) Daw, N. D. Do...
Jun Morimoto, Kenji Doya
AIPS
2007
14 years 4 days ago
Learning to Plan Using Harmonic Analysis of Diffusion Models
This paper summarizes research on a new emerging framework for learning to plan using the Markov decision process model (MDP). In this paradigm, two approaches to learning to plan...
Sridhar Mahadevan, Sarah Osentoski, Jeffrey Johns,...
AAAI
2006
13 years 11 months ago
Planning with First-Order Temporally Extended Goals using Heuristic Search
Temporally extended goals (TEGs) refer to properties that must hold over intermediate and/or final states of a plan. The problem of planning with TEGs is of renewed interest becau...
Jorge A. Baier, Sheila A. McIlraith