Associative Networks: Representation and Use of Knowledge by Computers is a collection of papers that deals with knowledge base of programs exhibiting some operational aspects of understanding. One paper reviews network formalism that utilizes unobstructed semantics, independent of the domain to which it is applied, that is also capable of handling significant epistemological relationships of concept structuring, attribute/value inheritance, multiple descriptions. Another paper explains network notations that encode taxonomic information; general statements involving quantification; information about processes and procedures; the delineation of local contexts, as well as the relationships between syntactic units and their interpretations. One paper shows that networks can be designed to be intuitively and formally interpretable. Network formalisms are computer-oriented logics which become distinctly significant when access paths from concepts to propositions are built into them. One feature of a topical network organization is its potential for learning. If one topic is too large, it could be broken down where groupings of propositions under the split topics are then based on "co-usage" statistics. As an example, one paper cites the University of Maryland artificial intelligence (AI) group which investigates the control and interaction of a meaning-based parser. The group also analyzes the inferences and predictions from a number of levels based on mundane inferences of actions and causes that can be used in AI. The collection can be useful for computer engineers, computer programmers, mathematicians, and researchers who are working on artificial intelligence.

This volume is a presentation of the theory and application of Pathfinder networks. These networks are derived from proximity data, and they have various applications in cognitive modelling, user-computer interface design, and knowledge engineering.

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.

Neural networks are a computing paradigm that is finding increasing attention among computer scientists. In this book, theoretical laws and models previously scattered in the literature are brought together into a general theory of artificial neural nets. Always with a view to biology and starting with the simplest nets, it is shown how the properties of models change when more general computing elements and net topologies are introduced. Each chapter contains examples, numerous illustrations, and a bibliography. The book is aimed at readers who seek an overview of the field or who wish to deepen their knowledge. It is suitable as a basis for university courses in neurocomputing.

Dr.Kuncham Sreenivasa Rao, Associate Professor, Department of Computer Science and Engineering, Faculty of Science and Technology (ICFAI Tech), ICFAI Foundation for Higher Education (IFHE), Hyderabad, Telangana, India. Dr.Ugendhar Addagatla, Associate Professor, Department of Computer Science and Engineering, Maturi Venkata Subba Rao (MVSR) Engineering College, Nadergul, Hyderabad, Telangana, India. Dr.Rajitha Kotoju, Assistant Professor, Department of Computer Science and Engineering, Mahatma Gandhi Institute of Technology, Hyderabad, Telangana, India.

About the Scope of This Text This book contains two types of material ~ first, the many divergent and often diffuse meanings given to the concepts of association, associative memory, and associative recaZZ are expounded. A review of this kind was felt necessary because there apparently does not exist any single monograph which could serve as a reference to these topics. But the presentation of the main body of this text is motivated by quite other reasons: in recent years, plenty of interesting mathematical and system-theoretical material has been published which makes it possible to gain a view of associative memory which is different from the conventional abstract and computationally oriented approaches. It seems that the basic operation of associative memory, the storage of information together with the relations or links between the data items, and the selective recall of stored information relative to a piece of key or cue information presented, is not restricted to certain computer-technological implementations but can also be reflected in more general mathematically describable processes in certain physical or other systems, especially in their adaptive state changes. It further seems that some generally known forms of associative memory, namely, certain computer technological artifacts, or abstract systems of concepts or data, are in fact special representations of a class of processes characterized as associative memory.

Cognitive science is a field that began with the realization that researchers in varied disciplines-psychology, artificial intelligence, linguistics, philosophy, formal semantics, neuroscience, and others-had taken on a common set of problems in representation and meaning, in reasoning and language. Nevertheless, cognitive science as a whole enjoys no common methodology or theoretical framework, and is in danger of becoming even more fragmented with time. There are two reasons for this. First, cognitive science is built on existing methodologies that have different historical origins. AB a result, the psychologist's truth is different from the linguist's truth. The artificial intelligence researcher's truth is different from the philosopher's truth. The neuroscientist's truth is different from the formal semanticist's truth. All too often there is little or no recognition of the relevance of work in other disciplines to one's own concerns. Second, cognitive scientists tend to develop theories around isolated problems. For instance, there are theories about how humans categorize concepts, about how humans analyze linguistic expressions syntactically, about how the English tense system works semantically, about how humans reason about space or reason about time, about how goal-directed problem solving occurs, about how the brain computes, and so on.