the use of methods employed for estimating the strength and toughness of composites reduces the trial-and-error in their design. the strength of interface between reinforcement and matrix and the effective surface of pulled-out particles have great influences on toughness and strength of these composites. in this study, a model was proposed to estimate the interface strength and matrix strength...

We evaluated four methods for increasing the practicality of functional communication training (FCT) by decreasing the frequency of reinforcement for alternative behavior. Three participants whose problem behaviors were maintained by positive reinforcement were treated successfully with FCT in which reinforcement for alternative behavior was initially delivered on fixed-ratio (FR) 1 schedules. ...

The use of reinforcement and rewards is known to enhance memory retention. However, the impact of reinforcement on higher-order forms of memory processing, such as integration and generalization, has not been directly manipulated in previous studies. Furthermore, there is evidence that sleep enhances the integration and generalization of memory, but these studies have only used reinforcement le...

Enhancement of the response of reinforced concrete (RC) beams using fiber-reinforced polymer (FRP) reinforcement bars has become a popular structural technique over the past two decades due to the well-known advantages of FRP composites including their high strength-to-weight ratio and excellent corrosion resistance. Thisstudy presents n...

Plant root reinforcement in soil bioengineering has gained increasing interest as a means of sustainable and environmentally friendly stabilisation. While Chrysopogon zizanioides is widely distributed tropical regions worldwide been advocated for use slope stabilisation erosion control, C. nemoralis normally mountainous areas Southeast Asian countries, its potential to reinforce rarely explored...

by MARGARET MARY SKELLY This dissertation investigates the incorporation of function approximation and hierarchy into reinforcement learning for use in an adaptive control setting through empirical studies. Reinforcement learning is an artificial intelligence technique whereby an agent discovers which actions lead to optimal task performance through interaction with its environment. Although re...

We present a general model for the effect of sex linkage on the evolution of reinforcement of mating preferences on an island. We find that the level of reinforcement can vary up to 80% depending on the mode of inheritance of the female preference and male trait. When reinforcement is driven mainly by selection in the male trait and intrinsic hybrid incompatibilities are weak, sex-linked prefer...

Reinforcement learning is the problem of generating optimal behavior in a sequential decision-making environment given the opportunity of interacting with it. Many algorithms for solving reinforcement-learning problems work by computing improved estimates of the optimal value function. We extend prior analyses of reinforcement-learning algorithms and present a powerful new theorem that can prov...

Recent advancements in reinforcement learning confirm that reinforcement learning techniques can solve large scale problems leading to high quality autonomous decision making. It is a matter of time until we will see large scale applications of reinforcement learning in various sectors, such as healthcare and cyber-security, among others. However, reinforcement learning can be time-consuming be...