Creating Brain-Like Intelligence
Creating Brain-Like Intelligence
Obtaining an FOU for a word from a single subject by an individual interval approach
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Review article: A review of soft computing applications in supply chain management
Applied Soft Computing
Bio-inspired computing: constituents and challenges
International Journal of Bio-Inspired Computation
Computing with words for hierarchical decision making applied to evaluating a weapon system
IEEE Transactions on Fuzzy Systems - Special section on computing with words
Approximate robotic mapping from sonar data by modeling perceptions with antonyms
Information Sciences: an International Journal
Measuring universal intelligence: Towards an anytime intelligence test
Artificial Intelligence
Interpretability assessment of fuzzy knowledge bases: A cointension based approach
International Journal of Approximate Reasoning
Self referenced multi-agent model, their information states and arrangements
ECS'10/ECCTD'10/ECCOM'10/ECCS'10 Proceedings of the European conference of systems, and European conference of circuits technology and devices, and European conference of communications, and European conference on Computer science
Information Sciences: an International Journal
Tackling outliers in granular box regression
Information Sciences: an International Journal
International Journal of Metadata, Semantics and Ontologies
Towards linguistic descriptions of phenomena
International Journal of Approximate Reasoning
BI'12 Proceedings of the 2012 international conference on Brain Informatics
Linguistic description of human activity based on mobile phone's accelerometers
IWAAL'12 Proceedings of the 4th international conference on Ambient Assisted Living and Home Care
Automatic linguistic reporting in driving simulation environments
Applied Soft Computing
Linguistic description about circular structures of the Mars' surface
Applied Soft Computing
Granularity of attributes in formal concept analysis
Information Sciences: an International Journal
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Officially, AI was born in 1956. Since then, very impressive progress has been made in many areas - but not in the realm of human level machine intelligence. During much of its early history, AI "was rife "with exaggerated expectations. A headline in an article published in the late forties of last century was headlined, "Electric brain capable of translating foreign languages is being built". Today, more than half a century later, we do have translation software, but nothing that can approach the quality of human translation. Clearly, achievement of human level machine intelligence is a challenge that is hard to meet. A prerequisite to achievement of human level machine intelligence is mechanization of these capabilities and, in particular, mechanization of natural language understanding. To make significant progress toward achievement of human level machine intelligence, a paradigm shift is needed. More specifically, what is needed is an addition to the armamentarium of AI of two methodologies: (a) a nontraditional methodology of computing with words (CW) or more generally, NL-Computation; and (b) a countertraditional methodology "which involves a progression from computing with numbers to computing with words. The centerpiece of these methodologies is the concept of precisiation of meaning. Addition of these methodologies to AI would be an important step toward the achievement of human level machine intelligence and its applications in decision-making, pattern recognition, analysis of evidence, diagnosis, and assessment of causality. Such applications have a position of centrality in our infocentric society.