Comparative study of performance of parallel Alpha Beta Pruning for different architectures

Optimization of searching the best possible action depending on various states like state of environment, system goal etc. has been a major area of study in computer systems. In any search algorithm, searching best possible solution from the pool of every possibility known can lead to the construction of the whole state search space popularly called as minimax algorithm. This may lead to a impractical time complexities which may not be suitable for real time searching operations. One of the practical solution for the reduction in computational time is Alpha Beta pruning. Instead of searching for the whole state space, we prune the unnecessary branches, which helps reduce the time by significant amount. This paper focuses on the various possible implementations of the Alpha Beta pruning algorithms and gives an insight of what algorithm can be used for parallelism. Various studies have been conducted on how to make Alpha Beta pruning faster. Parallelizing Alpha Beta pruning for the GPUs specific architectures like mesh(CUDA) etc. or shared memory model(OpenMP) helps in the reduction of the computational time. This paper studies the comparison between sequential and different parallel forms of Alpha Beta pruning and their respective efficiency for the chess game as an application.Comment: 5 pages, 6 figures, Accepted in 2019 IEEE 9th International Advance Computing Conference(IEEE Xplore

Alpha-beta pruning on evolving game trees

technical reportThe alpha-beta strategy is a widely used method for economizing on the size of game trees. Heretofore, its application has been limited to depth-first tree growth in recursive search functions. However, many modern game players use retentive (i.e. coroutine-based) control to achieve greater attention mobility in the game tree, e.g. for heuristically guided "best-first" searching. This paper reformulates the alpha-beta strategy for this generalized control setting. Algorithms are provided (in complete PASCAL code) for the following operations on appropriate nodes arbitrarily selected from a game tree: terminal node expansion, resumption of heuristically suspended move generation, tree re-rooting (i.e. top-level move selection), subtree redevelopment to satisfy a new search thoroughness condition, including restart of nodes that were cut-off but may no longer be. empirical results are presented indicating that, in addition to heuristic freedom, this method typically offers trees with fewer terminal nodes than in the recursive case, due to best-first descendant ordering, and the availability on the average of greater tree context for node cutting

Consolidation of a WSN and Minimax Method to Rapidly Neutralise Intruders in Strategic Installations

Due to the sensitive international situation caused by still-recent terrorist attacks, there is a common need to protect the safety of large spaces such as government buildings, airports and power stations. To address this problem, developments in several research fields, such as video and cognitive audio, decision support systems, human interface, computer architecture, communications networks and communications security, should be integrated with the goal of achieving advanced security systems capable of checking all of the specified requirements and spanning the gap that presently exists in the current market. This paper describes the implementation of a decision system for crisis management in infrastructural building security. Specifically, it describes the implementation of a decision system in the management of building intrusions. The positions of the unidentified persons are reported with the help of a Wireless Sensor Network (WSN). The goal is to achieve an intelligent system capable of making the best decision in real time in order to quickly neutralise one or more intruders who threaten strategic installations. It is assumed that the intruders’ behaviour is inferred through sequences of sensors’ activations and their fusion. This article presents a general approach to selecting the optimum operation from the available neutralisation strategies based on a Minimax algorithm. The distances among different scenario elements will be used to measure the risk of the scene, so a path planning technique will be integrated in order to attain a good performance. Different actions to be executed over the elements of the scene such as moving a guard, blocking a door or turning on an alarm will be used to neutralise the crisis. This set of actions executed to stop the crisis is known as the neutralisation strategy. Finally, the system has been tested in simulations of real situations, and the results have been evaluated according to the final state of the intruders. In 86.5% of the cases, the system achieved the capture of the intruders, and in 59.25% of the cases, they were intercepted before they reached their objective