The selectively attentive environmental learning system

Johnson, _______; Grogan,

doi:10.1109/icsyse.1991.161107

Cited by 2 publications

References 2 publications

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The decision process in selective attention

Johnson

Grogan

Conference Proceedings 1991 IEEE International Conference on Systems, Man, and Cybernetics

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A learning system that can be trained to generate t h e necessary sequential decision policy for maneuvering through a multiple T-maze, is proposed. The .Selectively Attentive Environmental Learning System (SAELS) has a n architecture with two types of neurally-inspired learning mechanisms to model and act on its environment. The first mechanism, which has been shown to model 19 forms of classically conditioned behavior [l], generates an internal reinforcement signal that anticipates the external reinforcement signal that SAELS will receive. The second learning mechanism is a single-layer associative neural network with competitive learning. This mechanism uses the internal reinforcement of the first learning mechanism to learn the association between the state of SAELS and the decision that must b e generated in order to successfully complete the maze. It is shown that SAELS solves the temporal credit assignment problem that arises when reinforcement is only available at the end of a sequence of actions. The general requirements of a system of selective attention, and how SAELS meets these requirements is discussed.

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The decision process in selective attention

Johnson

Grogan

Conference Proceedings 1991 IEEE International Conference on Systems, Man, and Cybernetics

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A Hierarchical Network of Control Systems that Learn: Modeling Nervous System Function During Classical and Instrumental Conditioning

Klopf¹,

Weaver²,

Morgan³

1993

Adaptive Behavior

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A computational model of nervous system function during classical and instrumental conditioning is proposed. The model assumes the form of a hierarchical network of control systems. Each control system is capable of learning and is referred to as an associative control process (ACP). Learning systems consisting of ACP networks, employing the drive-reinforcement learning mechanism (Klopf, 1988) and engaging in real-time, closed-loop, goal-seeking interactions with environments, are capable of being classically and instrumentally conditioned, as demonstrated by means of computer simulations. In multiple-T mazes, the systems learn to chain responses that avoid punishment and that lead eventually to reward. The temporal order in which the responses are learned and extinguished during instrumental conditioning is consistent with that observed in animal learning. Also consistent with animal learning experimental evidence, the ACP network model accounts for a wide range of classical conditioning phenomena. ACP networks, at their current stage of development, are intended to model sensorimotor, limbic, and hypothalamic nervous system function, suggesting a relationship between classical and instrumental conditioning that extends Mowrer's (1956, 1960a/1973) two-factor theory of learning. In conjunction with consideration of limbic system and hypothalamic function, the role of emotion in natural intelligence is modeled and discussed. ACP networks constitute solutions to temporal and structural credit assignment problems, suggesting a theoretical approach for the synthesis of machine intelligence.

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The selectively attentive environmental learning system

Cited by 2 publications

References 2 publications

The decision process in selective attention

The decision process in selective attention

A Hierarchical Network of Control Systems that Learn: Modeling Nervous System Function During Classical and Instrumental Conditioning

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