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Intelligence Emerging
An investigation of intelligence as an emergent phenomenon, integrating the perspectives of evolutionary biology, neuroscience, and artificial intelligence. Emergence—the formation of global patterns from solely local interactions—is a frequent and fascinating theme in the scientific literature both popular and academic. In this book, Keith Downing undertakes a systematic investigation of the widespread (if often vague) claim that intelligence is an emergent phenomenon. Downing focuses on neural networks, both natural and artificial, and how their adaptability in three time frames—phylogenetic (evolutionary), ontogenetic (developmental), and epigenetic (lifetime learning)—underlie th...
Gradient Expectations
An insightful investigation into the mechanisms underlying the predictive functions of neural networks—and their ability to chart a new path for AI. Prediction is a cognitive advantage like few others, inherently linked to our ability to survive and thrive. Our brains are awash in signals that embody prediction. Can we extend this capability more explicitly into synthetic neural networks to improve the function of AI and enhance its place in our world? Gradient Expectations is a bold effort by Keith L. Downing to map the origins and anatomy of natural and artificial neural networks to explore how, when designed as predictive modules, their components might serve as the basis for the simula...
Evolvable Systems: From Biology to Hardware
The idea of evolving machines, whose origins can be traced to the cybernetics movementofthe1940sand1950s,hasrecentlyresurgedintheformofthenascent ?eld of bio-inspired systems and evolvable hardware. The inaugural workshop, Towards Evolvable Hardware, took place in Lausanne in October 1995, followed by the First International Conference on Evolvable Systems: From Biology to Hardware (ICES), held in Tsukuba, Japan in October 1996. The second ICES conference was held in Lausanne in September 1998, with the third and fourth being held in Edinburgh, April 2000 and Tokyo, October 2001 respectively. This has become the leading conference in the ?eld of evolvable systems and the 2003 conference prom...
Genetic and Evolutionary Computation - GECCO 2003
The set LNCS 2723 and LNCS 2724 constitutes the refereed proceedings of the Genetic and Evolutionaty Computation Conference, GECCO 2003, held in Chicago, IL, USA in July 2003. The 193 revised full papers and 93 poster papers presented were carefully reviewed and selected from a total of 417 submissions. The papers are organized in topical sections on a-life adaptive behavior, agents, and ant colony optimization; artificial immune systems; coevolution; DNA, molecular, and quantum computing; evolvable hardware; evolutionary robotics; evolution strategies and evolutionary programming; evolutionary sheduling routing; genetic algorithms; genetic programming; learning classifier systems; real-world applications; and search based softare engineering.
Recent Advances in Qualitative Physics
These twenty-eight contributions report advances in one of the most active research areas in artificial intellgence. Qualitative modeling techniques are an essential part of building second generation knowledge-based systems. This book provides a timely overview of the field while also giving some indications about applications that appear to be feasible now or in the near future. Chapters are organized into sections covering modeling and simulation, ontologies, computational issues, and qualitative analysis. Modeling a physical system in order to simulate it or solve particular problems regarding the system is an important motivation of qualitative physics, involving formal procedures and c...
Eighth Scandinavian Conference on Artificial Intelligence
The Eighth Scandinavian Conference on Artificial Intelligence continues a tradition of being one of the most important regional AI conferences in Europe. Keith Downing focuses on the low road to artificial intelligence, that is, the development of AI through evolutionary artificial life approaches. The topics of the accepted papers range from multi-agent systems, robots, natural languages and machine learning to general knowledge-based systems and formal approaches to AI. This collection of papers together exemplifies the diversity of research in artificial intelligence today. Two of the invited speakers, both focus on vision, although each from slightly different viewpoints. One considers biological models for vision and its consequences for artificial vision, whereas the other considers the relation between real world objects and their internal representation in robots. The last keynote speaker, presents answer set programming, a new idea for declarative programming.
Uncertainty in Artificial Intelligence
Uncertainty Proceedings 1994
Scientists Debate Gaia
Leading scientists bring the controversy over Gaia up to date by exploring a broad range of recent thinking on Gaia theory.
Gradient Expectations
An insightful investigation into the mechanisms underlying the predictive functions of neural networks—and their ability to chart a new path for AI. Prediction is a cognitive advantage like few others, inherently linked to our ability to survive and thrive. Our brains are awash in signals that embody prediction. Can we extend this capability more explicitly into synthetic neural networks to improve the function of AI and enhance its place in our world? Gradient Expectations is a bold effort by Keith L. Downing to map the origins and anatomy of natural and artificial neural networks to explore how, when designed as predictive modules, their components might serve as the basis for the simula...
AAAI-92
AAAI proceedings describe innovative concepts, techniques, perspectives, and observations that present promising research directions in artificial intelligence.The focus of the AAAI-92 conference is on the re integration of AI as a diverse but coherent whole. Accordingly the traditional list of community-based content areas has been replaced by a more neutral set of taxonomies that span the field. For example, a paper proposing a new epistemology for representing the physical world based on an analysis of human brain structure would be described as "representation, physical world, biological." The papers collected here represent significant research contributions to such areas as the principles underlying cognition, perception, and action in man and machine; the design, application, and evaluation of AI algorithms and systems; and the analysis of domains in which AI systems perform.
