Generating abstraction hierarchies an automated approach to reducing search in planning

Generating abstraction hierarchies an automated approach to reducing search in planning

Saved in:

Bibliographic Details
Main Author:	Knoblock, Craig A. 1962-
Format:	Book
Published:	Boston Kluwer Academic Publisher 1993
Series:	The Kluwer international series in engineering and computer science SECS 214
Subjects:	Artificial intelligence Computer science Machine learning Abstraction
Online Access:	Click Here to View Status and Holdings.
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Machine learning an artificial intelligence approach
Published: (1983)

Knowledge acquisition and machine learning theory, methods, and applications
Published: (1993)

Multistrategy learning
Published: (1993)

Machines that learn based on the principles of emporical control
by: Brown, Robert Alan 1938-
Published: (1994)

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow Concepts, Tools, and Techniques to Build Intelligent Systems
by: Geron, Aurelien
Published: (2019)

Self-adaptive systems for machine intelligence
by: He, Haibo 1976-
Published: (2011)

Machine learning paradigms and methods
Published: (1990)

Utility-based learning from data
by: Friedman, Craig
Published: (2011)

Advances in machine learning dedicated to the memory of Professor Ryszard S. Michalski
Published: (2010)

Rule-based evolutionary online learning systems a principled approach to LCS analysis and design
by: Butz, Martin V. 1975-
Published: (2006)

Computational learning and probabilistic reasoning
Published: (1996)

Introduction to Deep Learning
by: Charniak, Eugene
Published: (2018)

Machine learning principles and techniques
Published: (1989)

Rule extraction from support vector machines
Published: (2008)

Emerging paradigms in machine learning
Published: (2013)

Truth from trash how learning makes sense
by: Thornton, Christopher James
Published: (2002)

Ensemble machine learning methods and applications
Published: (2012)

Elements of machine learning
by: Langley, Pat
Published: (1996)

Preference learning
Published: (2010)

Introduction to machine learning
by: Alpaydin, Ethem

Machine learning neural networks, genetic algorithms, and fuzzy systems
by: Adeli, Hojjat 1950-
Published: (1995)

Foundations of Machine Learning
by: Mohri, Mehryar, et al.
Published: (2018)

The mathematics of generalization the proceedings of the SFI/CNLS Workshop on Formal Approaches to Supervised Learning
Published: (1995)

An introduction to computational learning theory
by: Kearns, Michael J
Published: (1997)

Computational learning theory and natural learning systems edited by Jose Hanson, George A. Drastal and Ronald L. Rivest
Published: (1994)

The design and analysis of efficient learning algorithms
by: Schapire, Robert E.
Published: (1992)

Algorithmic learning theory II
Published: (1992)

Computational learning theory and natural learning systems edited by Stephen Jose Hanson ... (et al.)
Published: (1994)

Learning kernel classifiers theory and algorithms
by: Herbrich, Ralf
Published: (2002)

Machine learning ECML 2002 : 13th European Conference on Machine Learning, Helsinki, Finland, August 19-23, 2002 : proceedings
Published: (2002)

Machine learning an algorithmic perspective
by: Marsland, Stephen
Published: (2009)

The mathematics of generalization proceedings of the ... held August 1992

The dynamics of concepts - a connectionist model
by: Van Loocke, Philip R
Published: (1994)

Deep Learning for Search
by: Teofili, Tommaso
Published: (2019)

Computer systems that learn Classification and prediction methods from statistics, neural nets, machine learning, and expert systems
by: Weiss, Sholom M.
Published: (1991)

Machine learning and data mining methods and applications
Published: (1998)

Cost-sensitive machine learning
Published: (2012)

Machine Learning For Absolute Beginners A Plain English Introduction
by: Theobald, Oliver
Published: (2017)

Connectionist approaches to language learning
Published: (1991)

Learning with kernels support vector machines , regularization, optimization and beyond
by: Scholkopf, Bernhard
Published: (2002)