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ORCIDJournal of Experimental and Theoretical Artificial Intelligence, Volume 1
Volume 1, Number 1, 1989
- Eric Dietrich, Chris A. Fields
:
Experimental and theoretical artificial intelligence. 1-4 - Stevan Harnad:
Minds, machines and Searle. 5-25 - Brent J. Krawchuk, Ian H. Witten:
Explanation-based learning: its role in problem solving. 27-49 - Fernando Gomez, Carlos Segami:
The recognition and classification of concepts in understanding scientific texts. 51-77 - Anne M. Keuneke, Dean Allemang:
Exploring the No-Function-In-Structure principle. 79-89
Volume 1, Number 2, 1989
- Jonathan Wald, Martin Farach, Malle A. Tagamets, James A. Reggia:
Generating plausible diagnostic hypotheses with self-processing causal networks. 91-112 - Kenzo Iwama, Anthony S. Maida:
Organizing and integrating edge segments for texture discrimination. 113-132 - John A. Barnden:
Towards a paradigm shift in belief representation methodology. 133-161 - Frank Harary:
The graph of a boolean function. 163-169
Volume 1, Number 3, 1989
- Chris A. Fields:
Consequences of nonclassical measurement for the algorithmic description of continuous dynamical systems. 171-178 - Thomas C. Eskridge:
Principles of continuous analogical reasoning. 179-194 - R. J. Nelson:
Philosophical issues in Edelman's Neural Darwinism. 195-208
- John L. Pollock:
OSCAR: a general theory of rationality. 209-226
- Joseph S. Fulda, Kevin de Fontes:
The a priori meaningfulness measure and resolution theorem proving. 227-230
Volume 1, Number 4, 1989
- Jon Sticklen:
Problem-solving architecture at the knowledge level. 233-247 - William J. Clancey:
Commentary on Jon Sticklen's 'Problem-solving architecture at the knowledge level'. 249-253 - James A. Hendler:
Below the knowledge level architecture. 255-258 - Brian M. Slator:
Decomposing meat: a commentary on Sticklen's 'problem-solving architecture at the knowledge level'. 259-262 - Jon Stecklen:
Reply to commentaries on 'problem-solving architecture at the knowledge level'. 263-269 - Geoffrey Sampson, Robin Haigh, Eric Atwell:
Natural language analysis by stochastic optimization: a progress report on Project APRIL. 271-287 - Aaron Sloman:
On designing a visual system (Towards a Gibsonian computational model of vision). 289-337
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