The problem of expensive chunks and its solution by restricting expressiveness

[1]  Gerald DeJong,et al.  Explanation-Based Learning: An Alternative View , 2005, Machine Learning.

[2]  Glenn A. Iba,et al.  A Heuristic Approach to the Discovery of Macro-Operators , 1989, Machine Learning.

[3]  Allen Newell,et al.  Chunking in Soar: The anatomy of a general learning mechanism , 1985, Machine Learning.

[4]  Tom M. Mitchell,et al.  Explanation-Based Generalization: A Unifying View , 1986, Machine Learning.

[5]  Paul S. Rosenbloom,et al.  Applying problem solving and learning to diagnosis , 1993 .

[6]  Allen Newell,et al.  The chunking of goal hierarchies: a generalized model of practice , 1993 .

[7]  Allen Newell,et al.  Varieties of learning in Soar: 1987 , 1993 .

[8]  Allen Newell,et al.  Modeling human syllogistic reasoning in Soar , 1993 .

[9]  Richard Reviewer-Granger.  Unified Theories of Cognition , 1991, Journal of Cognitive Neuroscience.

[10]  Charles L. Forgy,et al.  Rete: a fast algorithm for the many pattern/many object pattern match problem , 1991 .

[11]  Milind Tambe,et al.  A Frameworkfor Investigating Production System Formulations with Polynomially Bounded Match , 1990, AAAI.

[12]  Steven Minton,et al.  Quantitative Results Concerning the Utility of Explanation-based Learning , 1988, Artif. Intell..

[13]  Krzysztof R. Apt,et al.  Logic Programming , 1990, Handbook of Theoretical Computer Science, Volume B: Formal Models and Sematics.

[14]  Paul P. Maglio,et al.  Approximating Learned Search Control Knowledge , 1989, ML.

[15]  Shaul Markovitch,et al.  Information Filters and Their Implementation in the SYLLOG System , 1989, ML.

[16]  M. Posner.  Foundations of cognitive science , 1989 .

[17]  John E. Laird,et al.  Symbolic architectures for cognition , 1989 .

[18]  Oren Etzioni,et al.  Explanation-Based Learning: A Problem Solving Perspective , 1989, Artif. Intell..

[19]  Raymond J. Mooney,et al.  The Effect of Rule Use on the Utility of Explanation-Based Learning , 1989, IJCAI.

[20]  Russell Greiner,et al.  Incorporating Redundant Learned Rules: A Preliminary Formal Analysis of EBL , 1989, IJCAI.

[21]  Milind Tambe,et al.  Eliminating Expensive Chunks by Restricting Expressiveness , 1989, IJCAI.

[22]  Jaime G. Carbonell,et al.  Towards a General Framework for Composing Disjunctive and Iterative Macro-operators , 1989, IJCAI.

[23]  Shaul Markovitch,et al.  Utilization Filtering: A Method for Reducing the Inherent Harmfulness of Deductively Learned Knowledge , 1989, IJCAI.

[24]  Allen Newell,et al.  A Problem Space Approach to Expert System Specification , 1989, IJCAI.

[25]  Jude W. Shavlik,et al.  Acquiring Recursive Concepts with Explanation-Based Learning , 1989, IJCAI.

[26]  Allen Newell,et al.  Soar/PSM-E: investigating match parallelism in a learning production sytsem , 1988, PPoPP 1988.

[27]  Anoop Gupta,et al.  Suitability of Message Passing Computers for Implementing Production Systems , 1988, AAAI.

[28]  Anoop Gupta,et al.  Comparison of the RETE and TREAT production matchers for soar (A summary) , 1988, AAAI 1988.

[29]  Toru Ishida,et al.  Optimizing Rules in Production System Programs , 1988, AAAI.

[30]  Shaul Markovitch,et al.  The Role of Forgetting in Learning , 1988, ML.

[31]  William W. Cohen.  Generalizing Number and Learning from Multiple Examples in Explanation Based Learning , 1988, ML.

[32]  Richard M. Keller,et al.  Defining Operationality for Explanation-Based Learning , 1987, Artificial Intelligence.

[33]  Jaime G. Carbonell,et al.  Learning effective search control knowledge: an explanation-based approach , 1988 .

[34]  Allen Newell,et al.  Some Chunks Are Expensive , 1988, ML.

[35]  Allen Newell,et al.  SOAR: An Architecture for General Intelligence , 1987, Artif. Intell..

[36]  David M. Steier.  CYPRESS-Soar: A Case Study in Search and Learning in Algorithm Design , 1987, IJCAI.

[37]  Gerald DeJong,et al.  An Explanation-based Approach to Generalizing Number , 1987, IJCAI.

[38]  Kemal Oflazer,et al.  Partitioning in parallel processing of production systems , 1987 .

[39]  Jack Mostow,et al.  PROLEARN: Towards a Prolog Interpreter that Learns , 1987, AAAI.

[40]  Daniel P. Miranker.  TREAT: a better match algorithm for AI production systems , 1987, AAAI 1987.

[41]  G. Weiderhold.  File organization for database design , 1987 .

[42]  Anoop Gupta.  Parallelism in production systems , 1987 .

[43]  John E. Laird,et al.  Mapping Explanation-Based Generalization onto Soar , 1986, AAAI.

[44]  Daniel J. Scales.  Efficient matching algorithms for the Soar/OPS5 production system , 1986 .

[45]  Steven Minton,et al.  Selectively Generalizing Plans for Problem-Solving , 1985, IJCAI.

[46]  H. Levesque,et al.  Readings in Knowledge Representation , 1985 .

[47]  Nancy Martin,et al.  Programming Expert Systems in OPS5 - An Introduction to Rule-Based Programming(1) , 1985, Int. CMG Conference.

[48]  David E. Smith,et al.  Ordering Conjunctive Queries , 1985, Artif. Intell..

[49]  Allen Newell,et al.  R1-Soar: An Experiment in Knowledge-Intensive Programming in a Problem-Solving Architecture , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[50]  Elaine Kant,et al.  Programming expert systems in OPS5 , 1985 .

[51]  Charles L. Forgy,et al.  The OPS83 report , 1984 .

[52]  Allen and Rosenbloom Paul S. Newell,et al.  Mechanisms of Skill Acquisition and the Law of Practice , 1993 .

[53]  J. D. Uiiman,et al.  Principles of Database Systems , 2004, PODS 2004.

[54]  M. Garey.  Johnson: computers and intractability: a guide to the theory of np- completeness (freeman , 1979 .

[55]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .