Neural Networks for State Evaluation in General Game Playing

Abstract. Unlike traditional game playing, General Game Playing is concerned with agents capable of playing classes of games. Given the rules of an unknown game, the agent is supposed to play well without human intervention. For this purpose, agent systems that use deterministic game tree search need to automatically construct a state value function to guide search. Successful systems of this type use evaluation functions derived solely from the game rules, thus neglecting further improvements by experience. In addition, these functions are fixed in their form and do not necessarily capture the game’s real state value function. In this work we present an approach for obtaining evaluation functions on the basis of neural networks that overcomes the aforementioned problems. A network initialization extracted from the game rules ensures reasonable behavior without the need for prior training. Later training, however, can lead to significant improvements in evaluation quality, as our results indicate.

Survival of Late Pleisticene Hunter-gatherer ancestry in the Iberian Peninsula

The Iberian Peninsula in southwestern Europe represents an important test case for the study of human population movements during prehistoric periods. During the Last Glacial Maximum (LGM), the peninsula formed a periglacial refugium for hunter-gatherers (HGs) and thus served as a potential source for the re-peopling of northern latitudes. The post-LGM genetic signature was previously described as a cline from Western HG (WHG) to Eastern HG (EHG), further shaped by later Holocene expansions from the Near East and the North Pontic steppes. Western and central Europe were dominated by ancestry associated with the 14,000-year-old individual from Villabruna, Italy, which had largely replaced earlier genetic ancestry, represented by 19,000-15,000-year-old individuals associated with the Magdalenian culture. However, little is known about the genetic diversity in southern European refugia, the presence of distinct genetic clusters, and correspondence with geography. Here, we report new genome-wide data from 11 HGs and Neolithic individuals that highlight the late survival of Paleolithic ancestry in Iberia, reported previously in Magdalenian-associated individuals. We show that all Iberian HGs, including the oldest, a 19,000-year-old individual from El Mirón in Spain, carry dual ancestry from both Villabruna and the Magdalenian-related individuals. Thus, our results suggest an early connection between two potential refugia, resulting in a genetic ancestry that survived in later Iberian HGs. Our new genomic data from Iberian Early and Middle Neolithic individuals show that the dual Iberian HG genomic legacy pertains in the peninsula, suggesting that expanding farmers mixed with local HGs

A machine learning approach to predict the stress results of quadratic tetrahedral elements

Industries are always looking for an effective and efficient way to reduce the computation time of simulation because of the huge expenditure involved. From basics of Finite Element Method (FEM), it is known that, linear order finite elements consume less computation time and are less accurate compared to higher order finite elements say quadratic elements. An approach to get the benefit of less computation cost of linear elements and the good accuracy of quadratic elements can be of a good thought. The methodology to get the accurate results of quadratic elements with the advantage of less simulation run time of linear elements is presented here. Machine Learning (ML) algorithms are found to be effective in making predictions based on some known data set. The present paper discusses a methodology to implement ML model to predict the results equivalent to that of quadratic elements based on the solutions obtained from the linear elements. Here, a ML model is developed using python code, the stress results from Finite Element (FE) model of linear tetrahedral elements is given as the input to it to predict the stress results of quadratic tetrahedral elements. Abaqus is used as the FEM tool to develop the FE models. A python script is used to extract the stresses and the corresponding node numbers. The results showed that the developed ML model is successful in prediction of the accurate stress results for the set of test data. The scatter plots showed that the Z-score method was effective in removing the singularities. The proposed methodology is effective to reduce the computation time for simulation. </jats:p