Intelligent Behavior in Animals and RobotsIntelligence takes many forms. This study explores an insight that animals, humans, and autonomous robots can all be analyzed as multi-task autonomous control systems. Biological adaptive systems, the authors argue, can in fact provide a better understanding of intelligence and rationality than that provided by traditional AI. |
Contents
Intelligent Behavior | 1 |
Rational Behavior | 25 |
Utility | 41 |
State and Cost | 73 |
Design and Decision | 109 |
Motivation and Autonomy | 141 |
Goals and Behavior | 173 |
Accomplishing Tasks | 191 |
Prerequisites for an Autonomous Robot | 215 |
The Goal Function in Robot Architecture | 237 |
Animal and Robot Learning | 257 |
Conclusions | 281 |
| 295 | |
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Common terms and phrases
achieved action theory activity adapted alternative animal behavior animal intelligence animal's approach architecture artificial intelligence asymptotically stable automaton autonomous agent autonomous robot battery calibration chapter choice classical conditioning cognitive cost function cost of changing criteria cupboard David McFarland decision decision-maker defined dishes driver economic emergent functionality energy environment environmental ethology evaluation example factors floor foraging fuel goal function goal-directed havior housekeeping robot human illustrated in figure indifference curves input intelligent behavior involved knowledge learning machine Madison Square Garden maximized McFarland 1976 McFarland and Houston mechanisms mental motivational nest neural notional costs operations optimal option outcome particular perform physiological planning possible predators principle problem procedures rational reinforcement relevant representation result risk sensor sequence shown in figure Sibly and McFarland simple situation space specified stimulus task temperature tion tool tradeoff trajectory utility function variables