2014 Reprint of 1954 American Edition. Full facsimile of the original edition, not reproduced with Optical Recognition Software. This two volume classic comprises two titles: "Patterns of Plausible Inference" and "Induction and Analogy in Mathematics." This is a guide to the practical art of plausible reasoning, particularly in mathematics, but also in every field of human activity. Using mathematics as the example par excellence, Polya shows how even the most rigorous deductive discipline is heavily dependent on techniques of guessing, inductive (...) reasoning, and reasoning by analogy. In solving a problem, the answer must be guessed at before a proof can be given, and guesses are usually made from a knowledge of facts, experience, and hunches. The truly creative mathematician must be a good guesser first and a good prover afterward; many important theorems have been guessed but no proved until much later. In the same way, solutions to problems can be guessed, and a god guesser is much more likely to find a correct solution. This work might have been called "How to Become a Good Guesser."-From the Dust Jacket. (shrink)

Here the author of How to Solve It explains how to become a "good guesser." Marked by G. Polya's simple, energetic prose and use of clever examples from a wide range of human activities, this two-volume work explores techniques of guessing, inductive reasoning, and reasoning by analogy, and the role they play in the most rigorous of deductive disciplines.

Law and legal reasoning are a natural target for artificial intelligence systems. Like medical diagnosis and other tasks for expert systems, legal analysis is a matter of interpreting data in terms of higher-level concepts. But in law the data are more like those for a system aimed at understanding natural language: they tell a story about human events that may lead to a lawsuit. Statements of the law, too, are written in natural language and legal arguments are often arguments about (...) what that language means or ought to mean.This study is one of the few research efforts in this fertile area. It is unique in developing a computational model for analyzing legal problems in a way that brings these strands of AI research together and makes sense from a jurisprudential perspective as well.Gardner first analyzes several positions in Anglo-American jurisprudence and their relevance for work in artificial intelligence. She identifies aspects of legal reasoning that any truly expert system in law must make a place for and suggests a way of decomposing the process of legal analysis that takes these aspects into account. She compares the resulting framework with those used by other legal analysis programs. A solid exposition of current AI techniques follows in chapters covering the author's system (written in Maclisp) for offer and acceptance problems, taken from law examinations, involved in contract law.Anne von der Lieth Gardner has a law degree and a Ph.D. in computer science, both from Stanford University. An Artificial Intelligence Approach to Legal Reasoning inaugurates the series Artificial Intelligence and the Law: Processes and Models of Legal Reasoning, edited by L. Thorne McCarty and Edwina L. Rissland. A Bradford Book. (shrink)

The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

There was a long tradition in philosophy according to which good reasoning had to be deductively valid. However, that tradition began to be questioned in the 1960’s, and is now thoroughly discredited. What caused its downfall was the recognition that many familiar kinds of reasoning are not deductively valid, but clearly confer justification on their conclusions. Here are some simple examples.

No detailed description available for "From Axiom to Dialogue".

Much work on legal knowledge systems treats legal reasoning as arguments that lead from a description of the law and the facts of a case, to the legal conclusion for the case. The reasoning steps of the inference engine parallel the logical steps by means of which the legal conclusion is derived from the factual and legal premises. In short, the relation between the input and the output of a legal inference engine is a logical one. The truth of the (...) conclusion only depends on the premises, and is independent of the argument that leads to the conclusion.This paper opposes the logical approach, and defends a procedural approach to legal reasoning. Legal conclusions are not true or false independent of the reasoning process that ended in these conclusions. In critical cases this reasoning process consists of an adversarial procedure in which several parties are involved. The course of the argument determines whether the conclusion is true or false. The phenomenon of hard cases is used to demonstrate this essential procedural nature of legal reasoning. (shrink)

The Pleadings Game is a normative formalization and computational model of civil pleading, founded in Roberty Alexy''s discourse theory of legal argumentation. The consequences of arguments and counterarguments are modelled using Geffner and Pearl''s nonmonotonic logic,conditional entailment. Discourse in focussed using the concepts of issue and relevance. Conflicts between arguments can be resolved by arguing about the validity and priority of rules, at any level. The computational model is fully implemented and has been tested using examples from Article Nine of (...) the Uniform Commercial Code. (shrink)

tational background of dialectic: the structure of formal disputation. Formal disputation Perhaps the clearest, and surely historically the most prominent, ...