The article discusses the integration of rough sets and Boolean reasoning for applications in pattern recognition, machine learning, data mining, and conflict analysis. It highlights the importance of discernibility relations in rough set theory, which help in distinguishing between objects and are crucial for constructing reducts, decision rules, and classifiers. The paper explores various methods for computing reducts, decision-relative reducts, and approximate reducts, which are essential for attribute selection and classification tasks. It also addresses value set reduction techniques, including discretization for real-valued attributes and symbolic attribute value grouping for categorical attributes. The article emphasizes the use of Boolean reasoning to derive prime implicants, which are used to construct minimal decision rules. These methods are applied to develop efficient classifiers and decision rules for real-world problems, demonstrating their effectiveness in handling complex data sets and improving classification accuracy. The paper also discusses the computational complexity of these tasks and the development of heuristics to address NP-hard problems, showing promising results in practical applications.The article discusses the integration of rough sets and Boolean reasoning for applications in pattern recognition, machine learning, data mining, and conflict analysis. It highlights the importance of discernibility relations in rough set theory, which help in distinguishing between objects and are crucial for constructing reducts, decision rules, and classifiers. The paper explores various methods for computing reducts, decision-relative reducts, and approximate reducts, which are essential for attribute selection and classification tasks. It also addresses value set reduction techniques, including discretization for real-valued attributes and symbolic attribute value grouping for categorical attributes. The article emphasizes the use of Boolean reasoning to derive prime implicants, which are used to construct minimal decision rules. These methods are applied to develop efficient classifiers and decision rules for real-world problems, demonstrating their effectiveness in handling complex data sets and improving classification accuracy. The paper also discusses the computational complexity of these tasks and the development of heuristics to address NP-hard problems, showing promising results in practical applications.