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PA*SE: Parallel A* for Slow Expansions

Planners need to become faster as we seek to tackle increasingly complicated problems. Much of the recent improvements in computer speed is due to multi-core processors. For planners to take advantage of these types of architectures, we must adapt algorithms for parallel processing. There are a number of planning domains where state expansions are slow. One example is robot motion planning, whe...

On (pa, p, pa, pa-1)-Relative Difference Sets

NÚMERO 196 . 12 DE FEBRER DE 2004 Nou

El proper 28 de febrer conclou el termini per a p u n t a r-se al programa Erasmus per al curs 2004/2005. Universitats ubicades a països de tot Europa esperen els estudiants valencians. Per aquest motiu, el Campus dels Tarongers i la Facultat de Psicologia seran els escenaris de la I Setmana Internacional Erasmus, que se celebrarà entre els dies 17 i 19 de febrer. Una gran carpa instal·lada a T...

Pa Learning Pa Algorithm with Unlabeled Data

We analyze the combination of online learning and semisupervised learning. Online learning is an approach to supervised machine learning, which is effective at processing a large amount of training data. Semi-supervised learning is employed when a large amount of unlabeled data is available. While both setting occurs frequently in real applications of machine learning, thus far, their theoretic...

Introducing a Lightweight Structural Model via Simulation of Vernacular “Pa Tu Pa” Arch

The knowledge of Iranian vernacular structures is based on geometry, and there is a possibility of recreating such structural patterns aimed at producing movable structures. The purpose of this research was to utilize the patterns of vernacular structures to provide a lightweight structural model. The questions raised included how to create various forms based on the structural history of any r...