Integrating associative processing concepts with massively parallel SIMD technology, this volume explores a model for accessing data by content rather than abstract address mapping.

Clark charts a fundamental shift from a static, inner-code-oriented conception of the subject matter of cognitive science to a more dynamic, developmentally rich, process-oriented view.

First published in 1973. This book proposes and tests a theory about human memory, about how a person encodes, retains, and retrieves information from memory. The book is especially concerned with memory for sentential materials. We propose a theoretical framework which is adequate for describing comprehension of linguistic materials, for exhibiting the internal representation of propositional materials, for characterizing the interpretative processes which encode this information into memory and make use of it for remembering, for answering questions, recognizing instances of known categories, drawing inferences, and making deductions.

Contents:A New Way to Acquire Knowledge (H-Y Wang)An SPN Knowledge Representation Scheme (J Gattiker & N Bourbakis)On the Deep Structures of Word Problems and Their Construction (F Gomez)Resolving Conflicts in Inheritance Reasoning with Statistical Approach (C W Lee)Integrating High and Low Level Computer Vision for Scene Understanding (R Malik & S So)The Evolution of Commercial AI Tools: The First Decade (F Hayes-Roth)Reengineering: The AI Generation — Billions on the Table (J S Minor Jr)An Intelligent Tool for Discovering Data Dependencies in Relational DBS (P Gavaskar & F Golshani)A Case-Based Reasoning (CBR) Tool to Assist Traffic Flow (B Das & S Bayles)A Study of Financial Expert System Based on Flops (T Kaneko & K Takenaka)An Associative Data Parallel Compilation Model for Tight Integration of High Performance Knowledge Retrieval and Computation (A K Bansal)Software Automation: From Silly to Intelligent (J-F Xu et al.)Software Engineering Using Artificial Intelligence: The Knowledge Based Software Assistant (D White)Knowledge Based Derivation of Programs from Specifications (T Weight et al.)Automatic Functional Model Generation for Parallel Fault Design Error Simulations (S-E Chang & S A Szygenda)Visual Reverse Engineering Using SPNs for Automated Diagnosis and Functional Simulation of Digital Circuits (J Gattiker & S Mertoguno)The Impact of AI in VLSI Design Automation (M Mortazavi & N Bourbakis)The Automated Acquisition of Subcategorizations of Verbs, Nouns and Adjectives from Sample Sentences (F Gomez)General Method for Planning and Rendezvous Problems (K I Trovato)Learning to Improve Path Planning Performance (P C Chen)Incremental Adaptation as a Method to Improve Reactive Behavior (A J Hendriks & D M Lyons)An SPN-Neural Planning Methodology for Coordination of Multiple Robotic Arms with Constrained Placement (N Bourbakis & A Tascillo) Readership: Computer scientists, artificial intelligence practitioners and robotics users. keywords:

Many-body interactions have been successfully described through models based on classical or quantum physics. More recently, some of the models have been related to cognitive science by researchers who are interested in describing brain activity through the use of artificial neural networks (ANNs). Biological and Quantum Computing for Human Vision: Holonomic Models and Applications presents an integrated model of human image processing up to conscious visual experience, based mainly on the Holonomic Brain Theory by Karl Pribram. This work researches possibilities for complementing neural models of early vision with the new preliminary quantum models of consciousness in order to construct a model of human image processing.

The ultimate goal of machines is to help humans to solve problems. Such problems range between two extremes: structured problems for which the solution is totally defined (and thus are easily programmed by humans), and random problems for which the solution is completely undefined (and thus cannot be programmed). Problems in the vast middle ground have solutions that cannot be well defined and are, thus, inherently hard to program. Machine Learning is the way to handle this vast middle ground, so that many tedious and difficult hand-coding tasks would be replaced by automatic learning methods. There are several machine learning tasks, and this work is focused on a major one, which is known as classification. Some classification problems are hard to solve, but we show that they can be decomposed into much simpler sub-problems. We also show that independently solving these sub-problems by taking into account their particular demands, often leads to improved classification performance.

This book constitutes the refereed post-conference proceedings of the Second International Andrei Ershov Memorial Conference on System Informatics, held in Akademgorodok, Novosibirsk, Russia, in June 1996. The 27 revised full papers presented together with 9 invited contributions were thoroughly refereed for inclusion in this volume. The book is divided in topical sections on programming methodology, artificial intelligence, natural language processing, machine learning, dataflow and concurrency models, parallel programming, supercompilation, partial evaluation, object-oriented programming, semantics and abstract interpretation, programming and graphical interfaces, and logic programming.

Information modelling and knowledge bases have become hot topics, not only in academic communities concerned with information systems and computer science, but also wherever information technology is applied in the world of business. This book presents the proceedings of the 21st European-Japanese Conference on Information Modelling and Knowledge Bases (EJC 2011), held in Tallinn, Estonia, in June 2011. The EJC conferences provide a worldwide forum for researchers and practitioners in the field to exchange results and experiences achieved in computer science and related disciplines such as conceptual analysis, design and specification of information systems, multimedia information modelling, multimedia systems, software engineering, knowledge and process management, cross cultural communication and context modelling. Attention is also paid to theoretical disciplines including cognitive science, artificial intelligence, logic, linguistics and analytical philosophy. The selected papers (16 full papers, 9 short papers, 2 papers based on panel sessions and 2 on invited presentations), cover a wide range of topics, including database semantics, knowledge representation, software engineering, www information management, context-based information retrieval, ontology, image databases, temporal and spatial databases, document data management, process management, cultural modelling and many others. Covering many aspects of system modelling and optimization, this book will be of interest to all those working in the field of information modelling and knowledge bases.