Introduction to expert systems
Introduction to expert systems
Multilayer feedforward networks are universal approximators
Neural Networks
Maximum matchings in general graphs through randomization
Journal of Algorithms
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Comparison of broadcasting techniques for mobile ad hoc networks
Proceedings of the 3rd ACM international symposium on Mobile ad hoc networking & computing
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Computational Architectures Integrating Neural and Symbolic Processes: A Perspective on the State of the Art
Artificial Intelligence and Neural Networks; Steps toward Principled Integration
Artificial Intelligence and Neural Networks; Steps toward Principled Integration
Computers and Intractability; A Guide to the Theory of NP-Completeness
Computers and Intractability; A Guide to the Theory of NP-Completeness
Ant Colony Optimization
The Wisdom of Crowds
Derivation and Analysis of Basic Computational Operations of Thalamocortical Circuits
Journal of Cognitive Neuroscience
Modeling individual's aging within a bacterial population using a pi-calculus paradigm
Natural Computing: an international journal
Introduction: Guest editors' introduction: Foundation of peer-to-peer computing
Computer Communications
Stationary Distributions for the Random Waypoint Mobility Model
IEEE Transactions on Mobile Computing
The Puzzle of Granular Computing
The Puzzle of Granular Computing
Controlling the losing probability in a monotone game
Information Sciences: an International Journal
Sub-symbolically managing pieces of symbolical functions for sorting
IEEE Transactions on Neural Networks
Simple model of spiking neurons
IEEE Transactions on Neural Networks
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Leaving the expert systems framework of the 80s and the early connectionist paradigm of the 90s, the scientific community is now drawn by social computing paradigms, where a huge number of agents individually do an elementary job and jointly give rise to a sophisticated functionality. There is no doubt that the complexity of this functionality is connected to the randomness of the agents' work. What comes increasingly clear is that this randomness is a guarantee of success, not a drawback, provided we avoid falling in the ordinary Gaussian phenomenology in the province of the central limit theorem. We envisage a jointly biased asymmetry of the agents' actions to be the main feature distinguishing them from the molecules of a gas in Brownian motion, and toss this idea in the paper through specific statistical models we elaborated in recent works.