13. Machine Intelligence 1994

Historical Perspectives

• John Alan Robinson: Logic, Computers, Turing, and von Neumann. 1-35
• Stephen Muggleton: Logic and Learning: Turing's legacy. 37-56

Inductive Inference

• Hiroki Arimura, Takeshi Shinohara, Setsuko Otsuki, Hiroki Ishizaka: A Generalization of the Least General Generalization. 59-85
• Ashwin Srinivasan, Stephen Muggleton, Michael Bain: The Justification of Logical Theories based on Data Compression. 87-121
• Ken'ichi Handa, Mikiko Nishikimi, Hitoshi Matsubara: Utilizing Structure Information in Concept Formation. 123-142
• Hiroshi Tsukimoto, Chie Morita: The Discovery of Propositions in Noisy Data. 143-167
• Takashi Yokomori: Learning non-deterministic finite automata from queries and counterexamples. 169-189

Scientific Domains

• Michael J. E. Sternberg, R. A. Lewis, Ross D. King, Stephen Muggleton: Machine Learning and biomolecular modelling. 193-212
• E. J. Kehoe: More than meets the eye: animal learning and knowledge induction. 213-246
• C. Trevarthen: Regulation of human cognition and its growth. 247-268
• Enn Tyugu: Large heterogeneous knowledge bases. 269-288

Experimental Machine Learning

• Michael Bain, Stephen Muggleton: Learning optimal chess strategies. 291-309
• Ross D. King, Bob Henery, Cao Feng, A. Sutherland: A Comparative Study of Classification Algorithms: Statistical, Machine Learning and Neural Network. 311-359

Learning Control

• Claude Sammut: Recent progress with BOXES. 363-383
• Donald Michie, Rui Camacho: Building symbolic representations of intuitive real-time skills from performance data. 385-418
• Masaki Suwa, Hiroshi Motoda: Learning Perceptually Chunked Macro Operators. 419-440
• Masayuki Numao, T. Maruoka, Masamichi Shimura: Inductively Speeding Up Logic Programs. 441-458