### 17 papers:

- ECIR-2015-LuoZDY #design #using
- Designing States, Actions, and Rewards for Using POMDP in Session Search (JL, SZ, XD, HY), pp. 526–537.
- ICML-c1-2014-ZhangHL #heuristic #performance
- Covering Number for Efficient Heuristic-based POMDP Planning (ZZ, DH, WSL), pp. 28–36.
- SIGIR-2014-ZhangLY #documentation #ranking
- A POMDP model for content-free document re-ranking (SZ, JL, HY), pp. 1139–1142.
- ICML-c3-2013-BrechtelGD #incremental #learning #performance #representation
- Solving Continuous POMDPs: Value Iteration with Incremental Learning of an Efficient Space Representation (SB, TG, RD), pp. 370–378.
- CIKM-2012-YuanW #correlation
- Sequential selection of correlated ads by POMDPs (SY, JW), pp. 515–524.
- ICML-2012-FoxT #bound
- Bounded Planning in Passive POMDPs (RF, NT), p. 15.
- SAC-2010-BaffaC #generative #modelling #policy #simulation
- Modeling POMDPs for generating and simulating stock investment policies (ACEB, AEMC), pp. 2394–2399.
- CASE-2009-HariharanB #markov #process #using
- Misplaced item search in a warehouse using an RFID-based Partially Observable Markov Decision Process (POMDP) model (SH, STSB), pp. 443–448.
- ICML-2009-BoulariasC #policy #predict
- Predictive representations for policy gradient in POMDPs (AB, BCd), pp. 65–72.
- ICML-2008-DoshiPR #learning #using
- Reinforcement learning with limited reinforcement: using Bayes risk for active learning in POMDPs (FD, JP, NR), pp. 256–263.
- ICML-2007-LiCLW #novel #orthogonal
- A novel orthogonal NMF-based belief compression for POMDPs (XL, WKWC, JL, ZW), pp. 537–544.
- ICML-2002-AberdeenB #scalability
- Scalable Internal-State Policy-Gradient Methods for POMDPs (DA, JB), pp. 3–10.
- SAC-2002-ChadesSC #approach #assessment #heuristic #problem
- A heuristic approach for solving decentralized-POMDP: assessment on the pursuit problem (IC, BS, FC), pp. 57–62.
- ICML-2000-BaxterB #learning
- Reinforcement Learning in POMDP’s via Direct Gradient Ascent (JB, PLB), pp. 41–48.
- ICML-1998-BonetG #learning #sorting
- Learning Sorting and Decision Trees with POMDPs (BB, HG), pp. 73–81.
- ICML-1997-KimuraMK #approximate #learning
- Reinforcement Learning in POMDPs with Function Approximation (HK, KM, SK), pp. 152–160.
- ICML-1996-WieringS
- Solving POMDPs with Levin Search and EIRA (MW, JS), pp. 534–542.