This treatise is an outgrowth of a series of seminars and tutorials on selected legal aspects of geology that were offered to several generations of undergraduate students at Lawrence University. The offerings were in response to a keen interest in how the law and legal institutions relate to the professional geologist. Much of the student interest was undoubtedly sparked by the legal controversies as sociated with the "environmental movement" that became so active during the 1970s and continues today to look to the law for the resolution of conflicting goals. Other students were interested in the role allocated to law by society in general, or were simply curious about law as a profession. Existing published material did not meet my needs, and I had to rely on "handouts" summarizing legal principles, reported appellate cases, and guest lectures from the county bar association. The more formally prepared course materials were edited by practicing attorneys and scholars in academia who encouraged me to seek a publisher who might make the materials available to a broader audience-an audience that might include not only students of the law but also the professional geologist, geological engineers, planners, policy makers, and attorneys, whether in industry, government, education, or private practice, who want to know more about the relationship between law and geology.
This volume includes some of the key research papers in the area of machine learning produced at MIT and Siemens during a three-year joint research effort. It includes papers on many different styles of machine learning, organized into three parts. Part I, theory, includes three papers on theoretical aspects of machine learning. The first two use the theory of computational complexity to derive some fundamental limits on what isefficiently learnable. The third provides an efficient algorithm for identifying finite automata. Part II, artificial intelligence and symbolic learning methods, includes five papers giving an overview of the state of the art and future developments in the field of machine learning, a subfield of artificial intelligence dealing with automated knowledge acquisition and knowledge revision. Part III, neural and collective computation, includes five papers sampling the theoretical diversity and trends in the vigorous new research field of neural networks: massively parallel symbolic induction, task decomposition through competition, phoneme discrimination, behavior-based learning, and self-repairing neural networks.
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