BibSLEIGH — Pat

Travelled to:

1 × Australia
1 × Germany
1 × Slovenia
1 × United Kingdom
13 × USA

Collaborated with:

∅ W.Iba J.A.Allen K.Thompson N.Nejati T.Könik L.Todorovski D.G.Shapiro M.Schwabacher S.Sage G.H.John K.Pfleger B.Nordhausen D.Rose N.Asgharbeygi D.J.Stracuzzi M.T.Gervasio S.Rogers C.Wilson S.Handley F.A.Rauscher J.Wogulis Nishant Trivedi Matt Banister W.Bridewell N.B.Asadi D.George S.D.Bay K.Saito J.N.Sánchez S.Dzeroski A.Kalton K.Wagstaff J.P.Yoo Dongkyu Choi Chunki Park

Talks about:

learn (9) data (6) knowledg (4) concept (4) induct (4) model (4) process (3) use (3) increment (2) discoveri (2)

Person: Pat Langley

DBLP: Langley:Pat

Facilitated 1 volumes:

Contributed to:

2006

2005

2003

2002

2001

2000

1999

1998

1996

1995

1992

1991

1990

1989

1988

2007

2010

Wrote 27 papers:

ICML-2006-AsgharbeygiSL #difference #learning #relational: Relational temporal difference learning (NA, DJS, PL), pp. 49–56.
ICML-2006-NejatiLK #learning #network: Learning hierarchical task networks by observation (NN, PL, TK), pp. 665–672.
ICML-2005-BridewellALT #induction #process: Reducing overfitting in process model induction (WB, NBA, PL, LT), pp. 81–88.
ICML-2003-LangleyGBS #induction #modelling #process #robust: Robust Induction of Process Models from Time-Series Data (PL, DG, SDB, KS), pp. 432–439.
ICML-2002-LangleySTD #modelling #process: Inducing Process Models from Continuous Data (PL, JNS, LT, SD), pp. 347–354.
ICML-2002-ShapiroL #learning #using: Separating Skills from Preference: Using Learning to Program by Reward (DGS, PL), pp. 570–577.
ICML-2001-SchwabacherL: Discovering Communicable Scientific Knowledge from Spatio-Temporal Data (MS, PL), pp. 489–496.
KDD-2001-KaltonLWY #clustering #learning: Generalized clustering, supervised learning, and data assignment (AK, PL, KW, JPY), pp. 299–304.
ICML-2000-Langley #machine learning: Crafting Papers on Machine Learning (PL), pp. 1207–1216.
ICML-1999-GervasioIL #adaptation #evaluation #learning #scheduling: Learning User Evaluation Functions for Adaptive Scheduling Assistance (MTG, WI, PL), pp. 152–161.
ICML-1999-LangleyS #analysis #classification #naive bayes: Tractable Average-Case Analysis of Naive Bayesian Classifiers (PL, SS), pp. 220–228.
KDD-1999-RogersLW #mining #modelling: Mining GPS Data to Augment Road Models (SR, PL, CW), pp. 104–113.
KDD-1998-HandleyLR #learning #predict: Learning to Predict the Duration of an Automobile Trip (SH, PL, FAR), pp. 219–223.
KDD-1996-JohnL #data mining #mining: Static Versus Dynamic Sampling for Data Mining (GHJ, PL), pp. 367–370.
KDD-1996-Langley #induction: Induction of Condensed Determinations (PL), pp. 327–330.
ICML-1995-LangleyP: Case-Based Acquisition of Place Knowledge (PL, KP), pp. 344–352.
ML-1992-IbaL #induction: Induction of One-Level Decision Trees (WI, PL), pp. 233–240.
ML-1991-LangleyA: The Acquisition of Human Planning Expertise (PL, JAA), pp. 80–84.
ML-1991-ThompsonLI #concept #using: Using Background Knowledge in Concept Formation (KT, PL, WI), pp. 554–558.
ML-1990-NordhausenL #approach #robust: A Robust Approach to Numeric Discovery (BN, PL), pp. 411–418.
ML-1989-AllenL #concept #using: Using Concept Hierarchies to Organize Plan Knowledge (JAA, PL), pp. 229–231.
ML-1989-Langley #empirical #learning: Unifying Themes in Empirical and Explanation-Based Learning (PL), pp. 2–4.
ML-1989-ThompsonL #concept #incremental: Incremental Concept Formation with Composite Objects (KT, PL), pp. 371–374.
ML-1988-IbaWL #concept #incremental #learning: Trading Off Simplicity and Coverage in Incremental concept Learning (WI, JW, PL), pp. 73–79.
ML-1988-RoseL #approach: A Hill-Climbing Approach to Machine Discovery (DR, PL), pp. 367–373.
AIIDE-2007-ChoiKNPL #game studies: A Believable Agent for First-Person Shooter Games (DC, TK, NN, CP, PL), pp. 71–73.
AIIDE-2010-LangleyTB: A Command Language for Taskable Virtual Agents (PL, NT, MB).