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Learning from Data
Ten years ago Bill Gale of AT&T Bell Laboratories was primary organizer of the first Workshop on Artificial Intelligence and Statistics. In the early days of the Workshop series it seemed clear that researchers in AI and statistics had common interests, though with different emphases, goals, and vocabularies. In learning and model selection, for example, a historical goal of AI to build autonomous agents probably contributed to a focus on parameter-free learning systems, which relied little on an external analyst's assumptions about the data. This seemed at odds with statistical strategy, which stemmed from a view that model selection methods were tools to augment, not replace, the abilities...
Brain-mind
"Brain-Mind presents a unified, brain-based theory of cognition and emotion, with applications to the most complex kinds of thinking, right up to consciousness and creativity. Unification comes from systematic application of Chris Eliasmith's powerful new Semantic Pointer Architecture, a highly original synthesis of neural network and symbolic ideas about how the mind works. Thagard will show the relevance of semantic pointers to a full range of important kinds of mental representations, from sensations and imagery to concepts, rules, analogies, and emotions. Neural mechanisms can then be used to explain many phenomena concerning consciousness, action, intention, language, creativity, and the self. Because of their broad importance, Thagard has tried to make Eliasmith's ideas accessible to a broad audience with no special background in neuroscience or mathematics. The value of a unified theory of thinking goes well beyond psychology, neuroscience, and the other cognitive sciences"--
Anatomy of the Mind
This book aims to understand human cognition and psychology through a comprehensive computational theory of the mind, namely, a "cognitive architecture." The goal is to develop a unified framework for understanding the human mind, and within the unified framework, to develop process-based, mechanistic explanations of a variety of psychological phenomena.
Massive Data Sets
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Algorithmic Learning Theory
This volume contains the papers presented at the 12th Annual Conference on Algorithmic Learning Theory (ALT 2001), which was held in Washington DC, USA, during November 25–28, 2001. The main objective of the conference is to provide an inter-disciplinary forum for the discussion of theoretical foundations of machine learning, as well as their relevance to practical applications. The conference was co-located with the Fourth International Conference on Discovery Science (DS 2001). The volume includes 21 contributed papers. These papers were selected by the program committee from 42 submissions based on clarity, signi?cance, o- ginality, and relevance to theory and practice of machine learni...
Minding Norms
This volume presents an unprecedented attempt to illustrate via agent based simulation the emergence of norms meant as prescribed conducts applied by the majority. The simulated scenarios are populated with cognitive agents generating norms by detecting and deciding to respect them.
Readings in Agents
This book collects the most significant literature on agents in an attempt top forge a broad foundation for the field. Includes papers from the perspectives of AI, databases, distributed computing, and programming languages. The book will be of interest to programmers and developers, especially in Internet areas.
Advances in Intelligent Data Analysis. Reasoning about Data
This book constitutes the refereed proceedings of the Second International Symposium on Intelligent Data Analysis, IDA-97, held in London, UK, in August 1997. The volume presents 50 revised full papers selected from a total of 107 submissions. Also included is a keynote, Intelligent Data Analysis: Issues and Opportunities, by David J. Hand. The papers are organized in sections on exploratory data analysis, preprocessing and tools; classification and feature selection; medical applications; soft computing; knowledge discovery and data mining; estimation and clustering; data quality; qualitative models.
Natural Philosophy
--Volume 3. Natural philosophy: from social brains to knowledge, reality, morality, and beauty
How to Build a Brain
One goal of researchers in neuroscience, psychology, and artificial intelligence is to build theoretical models that are able to explain the flexibility and adaptiveness of biological systems. How to build a brain provides a detailed guided exploration of a new cognitive architecture that takes biological detail seriously, while addressing cognitive phenomena. The Semantic Pointer Architecture (SPA) introduced in this book provides a set of tools for constructing a wide range of biologically constrained perceptual, cognitive, and motor models. Examples of such models are provided, and they are shown to explain a wide range of data including single cell recordings, neural population activity,...
