scholarly journals Characterizing the Nature of Probability-Based Proof Number Search: A Case Study in the Othello and Connect Four Games

Information ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 264
Author(s):  
Anggina Primanita ◽  
Mohd Nor Akmal Khalid ◽  
Hiroyuki Iida
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Search Algorithm ◽  
Search Algorithms ◽  
Game Tree ◽  
Tree Structures ◽  
Best First Search

Variants of best-first search algorithms and their expansions have continuously been introduced to solve challenging problems. The probability-based proof number search (PPNS) is a best-first search algorithm that can be used to solve positions in AND/OR game tree structures. It combines information from explored (based on winning status) and unexplored (through Monte Carlo simulation) nodes from a game tree using an indicator called the probability-based proof number (PPN). In this study, PPNS is employed to solve randomly generated positions in Connect Four and Othello, in which the results are compared with the two well-known best-first search algorithms (proof number search (PNS) and Monte Carlo proof number search). Adopting a simple improvement parameter in PPNS reduces the number of nodes that need to be explored by up to 57%. Moreover, further observation showed the varying importance of information from explored and unexplored nodes in which PPNS relies critically on the combination of such information in earlier stages of the Othello game. Discussion and insights from these findings are provided where the potential future works are briefly described.

Application of life cycle assessment and Monte Carlo simulation for enabling sustainable product design

2014 ◽  
Vol 12 (3) ◽  
pp. 307-315 ◽  
Author(s):  
Sekar Vinodh ◽  
Gopinath Rathod
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Life Cycle Assessment ◽  
Life Cycle ◽  
Product Design ◽  
Sustainable Product Design ◽  
Content Type ◽  
Sustainable Product ◽  
Manufacturing Organization

Purpose – The purpose of this paper is to present an integrated technical and economic model to evaluate the reusability of products or components. Design/methodology/approach – Life cycle assessment (LCA) methodology is applied to obtain the product’s environmental performance. Monte Carlo simulation is utilized for enabling sustainable product design. Findings – The results show that the model is capable of assessing the potential reusability of used products, while the usage of simulation significantly increases the effectiveness of the model in addressing uncertainties. Research limitations/implications – The case study has been conducted in a single manufacturing organization. The implications derived from the study are found to be practical and useful to the organization. Practical implications – The paper reports a case study carried out for an Indian rotary switches manufacturing organization. Hence, the model is practically feasible. Originality/value – The article presents a study that investigates LCA and simulation as enablers of sustainable product design. Hence, the contributions of this article are original and valuable.

Uncertainty Analysis and Monitoring of Earth Dam

2014 ◽  
Vol 580-583 ◽  
pp. 954-957
Author(s):  
Ling Qiang Yang ◽  
Rui Gao ◽  
Yan Wang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Probabilistic Method ◽  
Finite Difference Methods ◽  
Earth Dam ◽  
Earth Dams ◽  
Program Code ◽  
Physical Behavior ◽  
Difference Methods

Monte Carlo simulation provides a probabilistic method to evaluate the physical behavior of earth dam. Therefore, the behavior could be got in a more realistic manner. Based on the theory, an innovative software program code is developed by combining the Monte Carlo and finite difference methods to predict the performance of earth dams after impounding. In order to assess the efficiency of the method, the case study of earth dam, located at Southeast of China, has been studied in detail. The performance of this dam is predicted and compared with the field monitoring by using the monitoring data. The results shows the robustness of the proposed method.

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