Foundations of learning in autonomous agents☆
References (28)
- et al.
Towards and mathematical theory of inductive inference
Information and Control
(1975) Language identification in the limit
Information and Control
(1967)Complexity of automaton identification from given data
Information and Control
(1978)Incremental learning of control strategies with genetic algorithsms
Generalization as search
Artificial Intelligence
(1982)- et al.
A new approach to unsupervised learning in deterministic environments
- et al.
Noise tolerant instance-based learning algorithms
- et al.
Learning from noisy examples
Machine Learning
(1988) - et al.
Pattern recognizing stochastic learning automata
IEEE Transactions on Systems, Man and Cybernetics
(1985) - et al.
Bandit Problems: Sequential Allocation of Experiments
(1985)
A critique of the Valiant model
New theoretical directions in machine learning
Machine Learning
(1988)
Finite Markov Chains
(1976)
Learning quickly when irrelevant attributes abound: A new linear threshold algorithm
Machine Learning
(1988)
Cited by (12)
Self-Organization and Autonomous Robots
2012, Neural Systems for RoboticsAdaptive neurofuzzy control of a robotic gripper with on-line machine learning
2004, Robotics and Autonomous SystemsARBIB: An autonomous robot based on inspirations from biology
2000, Robotics and Autonomous SystemsCitation Excerpt :Learning then acts to maximize the reward or return associated with or predicted from the reinforcement signal over time. The many variants of this form of learning (especially the Q-learning paradigm of Watkins [79] and Watkins and Dayan [80]) have been very popular in animat studies (e.g. [37,39,44,48,52,70]). Reinforcement learning is similar to the S–R theory of conditioning [35, p. 350] which posits a direct link between the conditioned stimulus and response, in contrast to Pavlovian S–S theory which posits association of two stimuli — the CS and the US.
Fuzzy logic controller design utilizing multiple contending software agents
1999, Fuzzy Sets and SystemsCase-based reactive navigation: A method for on-line selection and adaptation of reactive robotic control parameters
1997, IEEE Transactions on Systems, Man, and Cybernetics, Part B: CyberneticsTOWARDS INCREMENTAL AUTONOMY FRAMEWORK FOR ON-ORBIT VISION-BASED GRASPING
2021, Proceedings of the International Astronautical Congress, IAC
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This work was supported in a part by a gift from the System Development Foundation and in part by the Air force Office of Scientific Research under contract # F49620-89-C-0055
Copyright © 1991 Published by Elsevier B.V.