Adaptive Control of Ill-Defined SystemsOliver G. Selfridge, Edwina L. Rissland, Michael A. Arbib There are some types of complex systems that are built like clockwork, with well-defined parts that interact in well-defined ways, so that the action of the whole can be precisely analyzed and anticipated with accuracy and precision. Some systems are not themselves so well-defined, but they can be modeled in ways that are like trained pilots in well-built planes, or electrolyte balance in healthy humans. But there are many systems for which that is not true; and among them are many whose understanding and control we would value. For example, the model for the trained pilot above fails exactly where the pilot is being most human; that is, where he is exercising the highest levels of judgment, or where he is learning and adapting to new conditions. Again, sometimes the kinds of complexity do not lead to easily analyzable models at all; here we might include most economic systems, in all forms of societies. There are several factors that seem to contribute to systems being hard to model, understand, or control. The human participants may act in ways that are so variable or so rich or so interactive that the only adequate model of the system would be the entire system itself, so to speak. This is probably the case in true long term systems involving people learning and growing up in a changing society. |
Contents
| 1 | |
| 11 | |
Some Themes and Primitives in IllDefined Systems | 21 |
The Use of Optimal Control in Economics | 27 |
Biological Views of Adaptation | 39 |
Adaptive Behavior in Manual Control and the Optimal | 51 |
Regulation Feedback and Internal Models | 75 |
The Dynamics of Adaptation in Living Systems | 89 |
The Concepts of Adaptation and Attunement | 187 |
From Neural Nets to Schema | 207 |
Richly Specified Input to Language Learning | 227 |
Adaptation as a Positive and | 251 |
Piaget and Education | 277 |
Implications and Applications of Piagets Sensorimotor | 289 |
Failure is Not the Spur | 305 |
Genetic Algorithms and Adaptation | 317 |
ARTIFICIAL INTELLIGENCE | 115 |
The Role of the Critic in Learning Systems | 127 |
Examples and Learning Systems | 149 |
Conceptual Models of IllDefined Systems | 165 |
Creativity in Skilled Performance | 177 |
CONTRIBUTORS | 335 |
| 337 | |
| 343 | |
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Adaptive Control of Ill-Defined Systems Oliver G. Selfridge,Edwina L. Rissland,Michael A. Arbib No preview available - 2012 |
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acquisition action activity adaptive control alleles analysis apraxia Arbib Artificial Intelligence behavior Beranek and Newman biological brain calculator child closed class items cognitive complex concepts constraints construct control system control theory coordination Cybernetics described domain dynamics effects environment evaluation example experiments factors feedback function genetic algorithm Gleitman global performance standard hemisphere heuristics ill-defined systems input instance interaction internal knowledge language Lavorel learner learning systems lesions linguistic machine mathematical motor learning movement Nespoulous neural objects open class optic flow optimal optimal control organism output parameters parse trees perception physical Piaget Piagetian predict problem processes properties provides Psychology representation rules schemata Science semantic sensorimotor sequence Séron signals simulation skill specific speech stability strategies strings structure studies surrogate models syntactic task tion Tok Pisin Tyldesley University variables vector verb visual visual perception York