Machine learning for cognitive networks : technology assessment and research challenges

The field of machine learning has made major strides over the last 20 years. This document summarizes the major problem formulations that the discipline has studied, then reviews three tasks in cognitive networking and briefly discusses how aspects of those tasks fit these formulations. After this, it discusses challenges for machine learning research raised by Knowledge Plane applications and closes with proposals for the evaluation of learning systems developed for these problems.

[1]  Fred M. Tonge,et al.  Summary of a Heuristic Line Balancing Procedure , 1960 .

[2]  Tom M. Mitchell,et al.  Learning from Solution Paths: An Approach to the Credit Assignment Problem , 1982, AI Mag..

[3]  Tom M. Mitchell,et al.  LEAP: A Learning Apprentice for VLSI Design , 1985, IJCAI.

[4]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[5]  D. Rumelhart.  Learning Internal Representations by Error Propagation, Parallel Distributed Processing , 1986 .

[6]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[7]  Matthew Self,et al.  Bayesian Classification , 1988, AAAI.

[8]  George Drastal,et al.  Error Correction in Constructive Induction , 1989, ML.

[9]  Raymond J. Mooney,et al.  Changing the Rules: A Comprehensive Approach to Theory Refinement , 1990, AAAI.

[10]  Vipin Kumar,et al.  Algorithms for Constraint-Satisfaction Problems: A Survey , 1992, AI Mag..

[11]  Henry Lieberman,et al.  Watch what I do: programming by demonstration , 1993 .

[12]  Bart Selman,et al.  Local search strategies for satisfiability testing , 1993, Cliques, Coloring, and Satisfiability.

[13]  David J. Marchette,et al.  Adaptive mixture density estimation , 1993, Pattern Recognit..

[14]  Monte Zweben,et al.  Scheduling and rescheduling with iterative repair , 1993, IEEE Trans. Syst. Man Cybern..

[15]  Michael L. Littman,et al.  Packet Routing in Dynamically Changing Networks: A Reinforcement Learning Approach , 1993, NIPS.

[16]  Andreas Stolcke,et al.  Inducing Probabilistic Grammars by Bayesian Model Merging , 1994, ICGI.

[17]  Michael J. Maher,et al.  Constraint Logic Programming: A Survey , 1994, J. Log. Program..

[18]  Pat Langley,et al.  Elements of Machine Learning , 1995 .

[19]  Douglas C. Montgomery,et al.  Response Surface Methodology: Process and Product Optimization Using Designed Experiments , 1995 .

[20]  Wei Zhang,et al.  A Reinforcement Learning Approach to job-shop Scheduling , 1995, IJCAI.

[21]  Herbert A. Simon,et al.  Applications of machine learning and rule induction , 1995, CACM.

[22]  Rich Caruana,et al.  Removing the Genetics from the Standard Genetic Algorithm , 1995, ICML.

[23]  Wray L. Buntine.  A Guide to the Literature on Learning Probabilistic Networks from Data , 1996, IEEE Trans. Knowl. Data Eng..

[24]  Andrew W. Moore,et al.  Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..

[25]  Pat Langley.  Relevance and insight in experimental studies , 1996 .

[26]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[27]  Avrim Blum,et al.  The Bottleneck , 2021, Monopsony Capitalism.

[28]  S. Pattinson,et al.  Learning to fly. , 1998 .

[29]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[30]  John J. Grefenstette,et al.  Evolutionary Algorithms for Reinforcement Learning , 1999, J. Artif. Intell. Res..

[31]  Pat Langley,et al.  User modeling in adaptive interfaces , 1999 .

[32]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[33]  Andrew W. Moore,et al.  Learning Evaluation Functions to Improve Optimization by Local Search , 2001, J. Mach. Learn. Res..

[34]  Andrew W. Moore,et al.  Q2: memory-based active learning for optimizing noisy continuous functions , 1998, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[35]  David D. Clark,et al.  A knowledge plane for the internet , 2003, SIGCOMM '03.

[36]  Pat Langley,et al.  Machine learning as an experimental science , 2004, Machine Learning.

[37]  Ronald J. Williams,et al.  Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning , 2004, Machine Learning.

[38]  Peter Clark,et al.  The CN2 Induction Algorithm , 1989, Machine Learning.

[39]  Thomas G. Dietterich,et al.  A Study of Explanation-Based Methods for Inductive Learning , 1989, Machine Learning.

[40]  D. Kibler,et al.  Instance-based learning algorithms , 2004, Machine Learning.

[41]  Douglas H. Fisher,et al.  Knowledge Acquisition Via Incremental Conceptual Clustering , 1987, Machine Learning.

[42]  D. Clark.  A New vision for network architecture , .