Connectionist Models. Proceedings of the 1990 Summer School by David S. Touretzky, Jeffrey L. Elman, Terrence J. Sejnowski

By David S. Touretzky, Jeffrey L. Elman, Terrence J. Sejnowski

Connectionist types

Show description

Read or Download Connectionist Models. Proceedings of the 1990 Summer School PDF

Similar intelligence & semantics books

Evolutionary Computation in Practice

This booklet is loaded with examples during which desktop scientists and engineers have used evolutionary computation - courses that mimic normal evolution - to unravel genuine difficulties. They aren t summary, mathematically in depth papers, yet debts of fixing vital difficulties, together with guidance from the authors on find out how to keep away from universal pitfalls, maximize the effectiveness and potency of the quest approach, and plenty of different sensible feedback.

Feedforward Neural Network Methodology (Springer Series in Statistics)

This decade has visible an explosive progress in computational pace and reminiscence and a fast enrichment in our figuring out of synthetic neural networks. those components offer structures engineers and statisticians being able to construct versions of actual, monetary, and information-based time sequence and indications.

Artificial Intelligence for Humans, Volume 2: Nature-Inspired Algorithms

Nature could be a nice resource of proposal for synthetic intelligence algorithms simply because its expertise is significantly extra complicated than our personal. between its wonders are robust AI, nanotechnology, and complicated robotics. Nature can hence function a consultant for real-life challenge fixing. during this booklet, you'll come upon algorithms motivated via ants, bees, genomes, birds, and cells that offer useful equipment for lots of different types of AI events.

Extra info for Connectionist Models. Proceedings of the 1990 Summer School

Sample text

This approach was discussed by W e r b o s (1977) in relation to the dif­ ferential dynamic programming method of Jacobson and Mayne (1970), and Jordan and Jacobs (1990) il­ lustrated it using a version o f the pole-balancing task with continuous control actions. , El-Fattah, 1981; Sutton, 1990). Werbos (1987, 1988, 1989) discusses gradient methods that make use of system models. Although direct reinforcement learning methods d o not use state-transit ion models, they can use value function models, which we call value models in what follows.

L where a is a positive constant. T h e fractions in (3) cause the controller t o sometimes prefer over u* an action that has not been been performed for a long time. Sato et al. (1988) show that if 0 satisfies the condi­ tions given by ( 2 ) , then in the limit all actions are performed infinitely often for each state, as needed for convergence o f the state-transition m o d e l , and that the policy converges t o an optimal policy. Specifically, they define the relative frequency coefficient t o be (4) n o At each time t, an estimated optimal policy, i / * ( t ) , is c o m p u t e d using the policy iteration method o f D P based o n the current state-transition model and the payoff array.

T h e policy can then b e adjusted via gradient ascent. Using an artificial neural network to repre­ sent the value model makes this approach attractive because the value model's gradient can be computed efficiently by error back-propagation. This approach was discussed by W e r b o s (1977) in relation to the dif­ ferential dynamic programming method of Jacobson and Mayne (1970), and Jordan and Jacobs (1990) il­ lustrated it using a version o f the pole-balancing task with continuous control actions.

Download PDF sample

Rated 4.76 of 5 – based on 39 votes