The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Intelligent Behavior in Animals and Robots
Abstract
This exciting study explores the novel insight, based on well-established ethological principles, that animals, humans, and autonomous robots can all be analyzed as multi-task autonomous control systems. Intelligence takes many forms. This exciting study explores the novel insight, based on well-established ethological principles, 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. In this technically sophisticated, clearly written investigation of robot-animal analogies, McFarland and Bösser show that a bee's accuracy in navigating on a cloudy day and a moth's simple but effective hearing mechanisms have as much to teach us about intelligent behavior as human models. In defining intelligent behavior, what matters is the behavioral outcome, not the nature of the mechanism by which the outcome is achieved. Similarly, in designing robots capable of intelligent behavior, what matters is the behavioral outcome. McFarland and Bösser address the problem of how to assess the consequences of robot behavior in a way that is meaningful in terms of the robot's intended role, comparing animal and robot in relation to rational behavior, goal seeking, task accomplishment, learning, and other important theoretical issues. Bradford Books imprint
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Cites
The Embodied Mind: Cognitive Science and Human Experience
Varela, F. J., Thompson, E., & Rosch, E. (2017) · doi:10.7551/mitpress/9780262529365.001.0001 · Cited by 2,449
Principles of Biological Autonomy
Varela, F. J. (2025) · The MIT Press eBooks · doi:10.7551/mitpress/14875.001.0001 · Cited by 1,885
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Studying artificial life with cellular automata
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Collective patterns and decision-making
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Modeling Adaptive Autonomous Agents
Maes, P. (1993) · Artificial Life · doi:10.1162/artl.1993.1.135 · Cited by 575
Co-evolving parasites improve simulated evolution as an optimization procedure
Hillis, W. D. (1991) · Cited by 447
Fundamentals of measurement and representation of natural systems
Rosén, R. (1978) · Cited by 353
Physics and social science — The approach of synergetics
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Cited by
Emotion and sociable humanoid robots
Breazeal, C. (2003) · International Journal of Human-Computer Studies · doi:10.1016/s1071-5819(03)00018-1 · Cited by 1,279
Artificial Intelligence: A New Synthesis
(1998) · Elsevier eBooks · doi:10.1016/c2009-0-27773-7 · Cited by 1,163
What is intrinsic motivation? A typology of computational approaches
Oudeyer, P.-Y. (2007) · Frontiers in Neurorobotics · doi:10.3389/neuro.12.006.2007 · Cited by 811
Animal cognition and animal behaviour
Shettleworth, S. J. (2001) · Animal Behaviour · doi:10.1006/anbe.2000.1606 · Cited by 438
Metadata
- Authors
- Mcfarland, D., & Bösser, T.
- Venue
- The MIT Press eBooks
- Publisher
- The MIT Press
- Published
- 1993-09-28
- OpenAlex
- W1518224635
- Cited by
- 321
- References
- 22
- FWCI
- 4.54
- Type
- book
- Language
- en
- Open access
- closed
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