This paper investigates to what extent learning methods are beneficial for the Lines of Action tournament program MIA. We focus on two components of the program: (1) the evaluation function and (2) the move ordering. Using temporal difference learning the evaluation function was improved by tuning the weights. We found substantial improvements for three weights. The move ordering was enhanced b...

Ant colony optimization (ACO) is a novel and promising meta-heuristic for solving hard combinatorial optimization problems, such as travelling salesman[3] [4] [5] [6] [7] [8], quadratic assignment[9] [10] [11] [12] and set covering problem [13]. Unfortunately, according to systems performance evaluation literature, the methods adopted to experimentally validate the current ACO are not enough ac...

With more severe resource scarcity and environmental problems, the evaluation of circular economy in microcosmic level has become the focus of the academic world. Based on the concept of circular economy, this paper not only structures the evaluation index system of circular economy for iron and steel enterprises, builds the evaluation model of circular economy for iron and steel enterprises ba...

This paper investigates to what extent learning methods are beneficial for the Lines of Action tournament program MIA. We focus on two components of the program: (1) the evaluation function and (2) the move ordering. Using temporal difference learning the evaluation function was improved by tuning the weights. We found substantial improvements for three weights. The move ordering was enhanced b...