Connectionistic Problem Solving

Hampson, Steven E.

doi:10.1007/978-1-4684-6770-3

Cited by 16 publications

(2 citation statements)

References 301 publications

(388 reference statements)

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“…However, as we have stated earlier, rats use more than SR behavior. Rats tend to have &dquo;place learning&dquo; rather than the &dquo;response learning&dquo; of the SR-style models (Tolman, 1932;Hampson, 1990;Redish, 1997). Krose and Eecen (1994) show that topological relationships may be generated in a SOM by using richer sensory input.…”

Section: Schema Levelmentioning

confidence: 98%

Cognitive Models of Spatial Navigation from a Robot Builder's Perspective

Wyeth

Browning

1998

Adaptive Behavior

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Complete physically embodied agents present a powerful medium for the investigation of cognitive models for spatial navigation. This article presents a maze-solving robot, called a micromouse, that parallels many of the behaviors found in its biological counterpart, the rat. A cognitive model of the robot is presented, and its limits are investigated. Limits are found to exist with respect to biological plausibility and robot applicability. It is proposed that the fundamental representations used to store and process information are the limiting factor. A review of the literature of current cognitive models reveals a lack of models suitable for implementation in real agents and proposes that available models fail as they have not been developed with real agents in mind. A solution to this conundrum is proposed in a list of guidelines for the development of future spatial models.

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Section: Schema Levelmentioning

confidence: 98%

Cognitive Models of Spatial Navigation from a Robot Builder's Perspective

Wyeth

Browning

1998

Adaptive Behavior

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“…Cooperative learning by Ant Colony System for solving TSP referring to Figure 9, which adapted from [1], the difference between communication levels among agents (ants) develops different outputs average speed to optimum solution. The changes of communication level are analogues to different values of λ in sigmoid function as shown at Equation (13) in below. This analogy seems to be illustrated well as referring to Figure 4 where the output salivation signal is increased depending upon the value of no of training cycles.…”

Section: Cooperative Learning By Acs For Solving Tspmentioning

confidence: 99%

On Analysis and Evaluation of Comparative Performance for Selected Behavioral Neural Learning Models versus One Bio-Inspired Non-Neural Clever Model (Neural Networks Approach)

Mustafa¹,

Tourkia²,

Ramadan³

2016

OALib

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This piece of research addresses an interesting comparative analytical study, which considers two concepts of diverse algorithmic computational intelligent paradigms related tightly with Neural and Non-Neural Systems' modeling. The first computational paradigm was concerned with practically obtained psycho-learning behavioral results after three animals' neural modeling. These are namely: Pavlov's, and Thorndike's experimental work. In addition, the third model is concerned with optimal solution of reconstruction problem reached by a mouse's movement inside Figure 8 maze. Conversely, second algorithmic intelligent paradigm was originated from observed activities' results after Non-Neural bio-inspired clever modeling namely Ant Colony System (ACS). These results were obtained after attaining optimal solution while solving Traveling Sales-man Problem (TSP). Interestingly, the effect of increasing number of agents (either neurons or ants) on learning performance was shown to be similar for both introduced systems. Finally, performances of both intelligent learning paradigms have been shown to be in agreement with learning convergence process searching for least mean square error LMS algorithm. While its application was for training some Artificial Neural Network (ANN) models. Accordingly, adopted ANN modeling is a relevant and realistic tool to investigate observations and analyze performance for both selected computational intelligence (biological behavioral learning) systems.

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The Convergence of TD(λ) for General λ

Dayan

1992

Reinforcement Learning

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The method of temporal differences (TD) is one way of making consistent predictions about the future. This paper uses some analysis of Watkins (1989) to extend a convergence theorem due to Sutton (1988) from the case which only uses information from adjacent time steps to that involving information from arbitrary ones. It also considers how this version of TD behaves in the face of linearly dependent representations for statesdemonstrating that it still converges, but to a different answer from the least mean squares algorithm. Finally it adapts Watkins' theorem that Q-learning, his closely related prediction and action learning method, converges with probability one, to demonstrate this strong form of convergence for a slightly modified version of TD.

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Connectionistic Problem Solving

Cited by 16 publications

References 301 publications

Cognitive Models of Spatial Navigation from a Robot Builder's Perspective

Cognitive Models of Spatial Navigation from a Robot Builder's Perspective

On Analysis and Evaluation of Comparative Performance for Selected Behavioral Neural Learning Models versus One Bio-Inspired Non-Neural Clever Model (Neural Networks Approach)

The Convergence of TD(λ) for General λ

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