Probabilistic reasoning in intelligent systems: networks of plausible inference
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Artificial Intelligence
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IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
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Proceedings of the 2008 conference on Artificial General Intelligence 2008: Proceedings of the First AGI Conference
Hybridization of cognitive models using evolutionary strategies
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Self-organized and evolvable cognitive architecture for intelligent agents and multi-agent systems
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
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The goal of this chapter is to outline the attention machine computational framework designed to make a significant advance towards creating systems with human-level intelligence (HLI). This work is based on the hypotheses that: 1. most characteristics of human-level intelligence are exhibited by some existing algorithm, but that no single algorithm exhibits all of the characteristics and that 2. creating a system that does exhibit HLI requires adaptive hybrids of these algorithms. Attention machines enable algorithms to be executed as sequences of attention fixations that are executed using the same set of common functions and thus can integrate algorithms from many different subfields of artificial intelligence. These hybrids enable the strengths of each algorithm to compensate for the weaknesses of others so that the total system exhibits more intelligence than had previously been possible.