To achieve an excellent thermal-mechanical performance of CMCs, it is necessary to analyze and design the thickness of the multi-layered interphases for an optimized TRS distribution. An optimization was performed with a new version of the particle swarm optimization, the BSG-Starcraft Radius PSO linked to a finite element software.

We study Lorentzian D-particles in linear dilaton and the two dimensional black hole backgrounds. The D-particle trajectory follows an accelerated trajectory which is smeared by stringy corrections. For the black hole background we find that the portion of the trajectory behind the horizon appears to an asymptotic observer as ghost D-particle. This suggests a way of constructing a matrix model ...

Real-time strategy games have been an important field of game artificial intelligence in recent years. This paper presents a reinforcement learning and curriculum transfer learning method to control multiple units in StarCraft micromanagement. We define an efficient state representation, which breaks down the complexity caused by the large state space in the game environment. Then a parameter s...

Smart decision making at the tactical level is important for Artificial Intelligence (AI) agents to perform well in the domain of real-time strategy (RTS) games. This paper presents a Bayesian model that can be used to predict the outcomes of isolated battles, as well as predict what units are needed to defeat a given army. Model parameters are learned from simulated battles, in order to minimi...

We present the first ever large scale analysis of actual strategy video game play. Using a corpus of over 2,000 Starcraft: Brood War games from international tournaments, we analyze every player’s actions to determine which of their capacities lead to success. We also tie these capacities to their likely cognitive underpinnings, showing that the ability to distribute attention is correlated wit...

This paper presents an application of Monte-Carlo planning for controlling units in a RTS game StarCraft. We developed an original simulator for applying Monte-Carlo Planning (MCPlan) to solve the problem of random and simultaneous moves in the RTS game. We also apply an greedy   algorithm to model the opponent in a simulation for improving MCPlan’s performance. Experimental results are provi...