Machine learning: a survey of current techniques

Machine learning is the essence of machine intelligence. When we have systems that learn, we will have true artificial intelligence. Many machine-learning strategies exist, this paper reviews the state of the art in machine learning and provides a glimpse of the pioneers of present machine-learning systems and strategies. Learning in noisy domains, the evolutionary learning, learning by analogy and explanation-based learning are just some of the methods covered. Emphasis is placed on the algorithms employed by many of the systems, and the merits and disadvantages of various approaches. Finally an examination of VanLehn's theory of impasse-driven learning is made.