Ai 93 Proceedings Of The 6th Australian Joint Conference On Artificial Intelligence
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Ai '93 - Proceedings Of The 6th Australian Joint Conference On Artificial Intelligence
This volume reflects the state of the art in artificial intelligence in the Australasian region. It covers machine learning, knowledge acguisition, cognitive modelling, robots and vision, natural language, automated reasoning, knowledge-based systems, neural networks and genetic algorithms, distributed AI, etc.
Foundations of Rule Learning
Author: Johannes Fürnkranz
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-11-06
Rules – the clearest, most explored and best understood form of knowledge representation – are particularly important for data mining, as they offer the best tradeoff between human and machine understandability. This book presents the fundamentals of rule learning as investigated in classical machine learning and modern data mining. It introduces a feature-based view, as a unifying framework for propositional and relational rule learning, thus bridging the gap between attribute-value learning and inductive logic programming, and providing complete coverage of most important elements of rule learning. The book can be used as a textbook for teaching machine learning, as well as a comprehensive reference to research in the field of inductive rule learning. As such, it targets students, researchers and developers of rule learning algorithms, presenting the fundamental rule learning concepts in sufficient breadth and depth to enable the reader to understand, develop and apply rule learning techniques to real-world data.
Industrial and Engineering Applications of Artificial Intelligence and Expert Systems
In the areas of industry and engineering, AI techniques have become the norm in sectors including computer-aided design, intelligent manufacturing, and control. Papers in this volume represent work by both computer scientists and engineers separately and together. They directly and indirectly represent a real collaboration between computer science and engineering, covering a wide variety of fields related to intelligent systems technology ranging from neural networks, knowledge acquisition and representation, automated scheduling, machine learning, multimedia, genetic algorithms, fuzzy logic, robotics, automated reasoning, heuristic searching, automated problem solving, temporal, spatial and model-based reasoning, clustering, blackboard architectures, automated design, pattern recognition and image processing, automated planning, speech recognition, simulated annealing, and intelligent tutoring, as well as various computer applications of intelligent systems including financial analysis, artificial