We discuss models in which the smallness of the effective vacuum energy density rL and the coincidence of the time of its dominance tL with the epoch of galaxy formation tG are due to anthropic selection effects. In such models, the probability distribution for rL is a product of an a priori distribution P*(rL) and of the number density of galaxies at a given rL ~which is proportional to the nu...

We present a dog lung model to predict the relation between inhomogeneous changes in airway morphometry and lung resistance (RL) and elastance (EL) for frequencies surrounding typical breathing rates. The RL and EL were sensitive in distinct ways to two forms of peripheral constriction. First, when there is a large and homogeneous constriction, the RL increases uniformly over the frequency rang...

INTRODUCTION To date, there are insufficient data demonstrating the benefits of preclinically administered Ringer-lactate (RL) for the treatment of hemorrhagic shock following trauma. Recent animal experiments have shown that lactate tends to have toxic effects in severe hemorrhagic shock. This study aimed to compare the effects of RL administered in a rat model of severe hemorrhagic shock (mea...

Kent planlama anlay??? ve politikalar?nda geleneksel yöntemlerin, teknik yakla??mlar?n merkeziyetçi anlay???n sonuç üretmedeki yetersizli?i giderek karma??kla?an sorunlara çözüm aray??lar? politika alan?nda i? birli?inin önemini art?rmaktad?r. ile ilgili karar alma sürecinde kentte ya?ayanlar daha az söz sahibi olmaktad?r. Önceleri ekoloji alan? ili?kisel olarak çevre konular?nda afet riskleri ...

It is commonly stated that reinforcement learning (RL) algorithms require more samples to learn than humans. In this work, we investigate this claim using two standard problems from the RL literature. We compare the performance of human subjects to RL techniques. We find that context—the meaningfulness of the observations—plays a significant role in the rate of human RL. Moreover, without conte...

This paper presents CBRetaliate, an agent that combines Case-Based Reasoning (CBR) and Reinforcement Learning (RL) algorithms. Unlike most previous work where RL is used to improve accuracy in the action selection process, CBRetaliate uses CBR to allow RL to respond more quickly to changing conditions. CBRetaliate combines two key features: it uses a time window to compute similarity and stores...

Ranking web pages for presenting the most relevant web pages to user’s queries is one of the main issues in any search engine. In this paper, two new ranking algorithms are offered, using Reinforcement Learning (RL) concepts. RL is a powerful technique of modern artificial intelligence that tunes agent’s parameters, interactively. In the first step, with formulation of ranking as an RL problem,...

In this paper, we apply Reinforcement Learning (RL) to a real-world task. While complex problems have been solved by RL in simulated worlds, the costs of obtaining enough training examples often prohibits the use of plain RL in real-world scenarios. We propose three approaches to reduce training expenses for real-world RL. Firstly, we replace the random exploration of the huge search space, whi...

Humans can select actions by learning, planning, or retrieving motor memories. Reinforcement Learning (RL) associates these processes with three major classes of strategies for action selection: exploratory RL learns state-action values by exploration, model-based RL uses internal models to simulate future states reached by hypothetical actions, and motor-memory RL selects past successful state...