Neutralisation state driven single-agent search strategy for solving constraint satisfaction problem / Saajid Akram Ahmed Abuluaih

In the past seven decades, Constraint Satisfaction (CS) has been extensively studied and remarkably evolved to where the scientific community perceives it as the centre of the intelligent behaviour. Therefore, most of the recent research in the field is devoted to improving the problem solvers that utilize search strategies and techniques. Since Constraint Satisfaction Problem (CSP) is an NP-complete problem, brute-force search algorithms such as Backtracking algorithm (BT) are required as the guarantee to find a solution, when there is one. Moreover, since the establishment of the field, AI pioneers and specialists have setup instructions and guidelines on how to solve this type of problems back in the seventies of the last century and have not been changed or improved. For example, the framework of solving CSP imposes a complete permutation of assignments to all remaining variables in order to derive a valid model. The author argues in this study that the problem can be neutralised and it is not necessary to perform brute-force searching all the time if a search strategy could have guided the process to the level where the values of the remaining variables can be determined implicitly, creating what the author calls Solo-Path of assignments in the problem search tree.