Empirical Methods in Artificial Intelligence: A Review

Early research on AI typically involved qualitative demonstrations of intelligent behavior, with novelty being the primary focus. However, as the field has matured, there have been increasing demands for more careful evaluation using quantitative measures of behavior. In some cases, the response has taken the guise of formal analyses, and in others, it has emphasized comparisons between system and human behavior, but the predominant movement has been toward empirical studies of AI methods. As a result, techniques for experimental design, exploratory data analysis, and statistical testing, originally developed in other fields, have become increasingly relevant for AI researchers. Paul Cohen's book Empirical Methods for Artificial Intelligence aims to encourage this trend by providing AI practitioners with the knowledge and tools needed for careful empirical evaluation.