Talespin
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Tale Spin
Some people see things as they are and turn them happily out of shape while retaining the core. That is the essence of humour and only a few individuals are blessed with felicity to take everyday situations and give them an extra dimension.
The Creative Process
Someday computers will be artists. They'll be able to write amusing and original stories, invent and play games of unsurpassed complexity and inventiveness, tell jokes and suffer writer's block. But these things will require computers that can both achieve artistic goals and be creative. Both capabilities are far from accomplished. This book presents a theory of creativity that addresses some of the many hard problems which must be solved to build a creative computer. It also presents an exploration of the kinds of goals and plans needed to write simple short stories. These theories have been implemented in a computer program called MINSTREL which tells stories about King Arthur and his knights. While far from being the silicon author of the future, MINSTREL does illuminate many of the interesting and difficult issues involved in constructing a creative computer. The results presented here should be of interest to at least three different groups of people. Artificial intelligence researchers should find this work an interesting application of symbolic AI to the problems of story-telling and creativity. Psychologists interested in creativity and imagination should benefit from the attempt to build a detailed, explicit model of the creative process. Finally, authors and others interested in how people write should find MINSTREL's model of the author-level writing process thought-provoking.
Machine Learning
Author: Ryszard S. Michalski
language: en
Publisher: Morgan Kaufmann
Release Date: 1994-02-09
Multistrategy learning is one of the newest and most promising research directions in the development of machine learning systems. The objectives of research in this area are to study trade-offs between different learning strategies and to develop learning systems that employ multiple types of inference or computational paradigms in a learning process. Multistrategy systems offer significant advantages over monostrategy systems. They are more flexible in the type of input they can learn from and the type of knowledge they can acquire. As a consequence, multistrategy systems have the potential to be applicable to a wide range of practical problems. This volume is the first book in this fast growing field. It contains a selection of contributions by leading researchers specializing in this area. See below for earlier volumes in the series.
