Travelled to:1 × Australia
1 × Finland
1 × Germany
1 × Slovenia
1 × United Kingdom
11 × USA
Collaborated with:∅ M.T.Gervasio J.Gratch A.Laud A.Epshteyn S.A.Chien Q.Sun M.Brodie S.Bennett S.A.Rajamoney A.Vogel R.C.Schank J.L.Kolodner K.Yotov X.Li G.Ren M.Cibulskis M.J.Garzarán D.A.Padua K.Pingali P.Stodghill P.Wu
Talks about:learn (12) reinforc (5) explan (5) approach (4) plan (4) control (3) knowledg (2) retriev (2) reactiv (2) problem (2)
Person: Gerald DeJong
DBLP: DeJong:Gerald
Contributed to:
2008200620052003
20032002200019991995199419931992199119891988
19831980Wrote 19 papers:
- ICML-2008-EpshteynVD #learning
- Active reinforcement learning (AE, AV, GD), pp. 296–303.
- ICML-2006-EpshteynD #learning
- Qualitative reinforcement learning (AE, GD), pp. 305–312.
- ICML-2005-SunD #approach #learning
- Explanation-Augmented SVM: an approach to incorporating domain knowledge into SVM learning (QS, GD), pp. 864–871.
- ICML-2003-LaudD #analysis #learning
- The Influence of Reward on the Speed of Reinforcement Learning: An Analysis of Shaping (AL, GD), pp. 440–447.
- PLDI-2003-YotovLRCDGPPSW #comparison #empirical #modelling #optimisation
- A comparison of empirical and model-driven optimization (KY, XL, GR, MC, GD, MJG, DAP, KP, PS, PW), pp. 63–76.
- ICML-2002-LaudD #behaviour #learning
- Reinforcement Learning and Shaping: Encouraging Intended Behaviors (AL, GD), pp. 355–362.
- ICML-2000-DeJong #empirical #learning
- Hidden Strengths and Limitations: An Empirical Investigation of Reinforcement Learning (GD), pp. 215–222.
- ICML-1999-BrodieD #induction #learning #using
- Learning to Ride a Bicycle using Iterated Phantom Induction (MB, GD), pp. 57–66.
- ICML-1995-DeJong #case study
- A Case Study of Explanation-Based Control (GD), pp. 167–175.
- ICML-1994-GervasioD #approach #incremental #learning
- An Incremental Learning Approach for Completable Planning (MTG, GD), pp. 78–86.
- ICML-1993-GratchCD #learning #network #scheduling
- Learning Search Control Knowledge for Deep Space Network Scheduling (JG, SAC, GD), pp. 135–142.
- ML-1992-GratchD #analysis #learning #problem
- An Analysis of Learning to Plan as a Search Problem (JG, GD), pp. 179–188.
- ML-1991-BennettD #probability
- Comparing Stochastic Planning to the Acquisition of Increasingly Permissive Plans (SB, GD), pp. 586–590.
- ML-1991-ChienGD #learning #on the
- On Becoming Decreasingly Reactive: Learning to Deliberate Minimally (SAC, MTG, GD), pp. 288–292.
- ML-1991-GratchD #approach #effectiveness #hybrid
- A Hybrid Approach to Guaranteed Effective Control Strategies (JG, GD), pp. 509–513.
- ML-1989-GervasioD #learning
- Explanation-Based Learning of Reactive Operations (MTG, GD), pp. 252–254.
- ML-1988-RajamoneyD #approach #multi #problem #reduction
- Active Explanation Reduction: An Approach to the Multiple Explanations Problem (SAR, GD), pp. 242–255.
- SIGIR-1983-DeJong #information retrieval
- Artificial Intelligence Implications for Information Retrieval (GD), pp. 10–17.
- SIGIR-1980-SchankKD #concept #information retrieval
- Conceptual Information Retrieval (RCS, JLK, GD), pp. 94–116.