17,737 research outputs found
A Review on the Application of Natural Computing in Environmental Informatics
Natural computing offers new opportunities to understand, model and analyze
the complexity of the physical and human-created environment. This paper
examines the application of natural computing in environmental informatics, by
investigating related work in this research field. Various nature-inspired
techniques are presented, which have been employed to solve different relevant
problems. Advantages and disadvantages of these techniques are discussed,
together with analysis of how natural computing is generally used in
environmental research.Comment: Proc. of EnviroInfo 201
Neural network controller against environment: A coevolutive approach to generalize robot navigation behavior
In this paper, a new coevolutive method, called Uniform Coevolution, is introduced to learn weights of a neural network controller in autonomous robots. An evolutionary strategy is used to learn high-performance reactive behavior for navigation and collisions avoidance. The introduction of coevolutive over evolutionary strategies allows evolving the environment, to learn a general behavior able to solve the problem in different environments. Using a traditional evolutionary strategy method, without coevolution, the learning process obtains a specialized behavior. All the behaviors obtained, with/without coevolution have been tested in a set of environments and the capability of generalization is shown for each learned behavior. A simulator based on a mini-robot Khepera has been used to learn each behavior. The results show that Uniform Coevolution obtains better generalized solutions to examples-based problems.Publicad
What is Computational Intelligence and where is it going?
What is Computational Intelligence (CI) and what are its relations with Artificial Intelligence (AI)? A brief survey of the scope of CI journals and books with ``computational intelligence'' in their title shows that at present it is an umbrella for three core technologies (neural, fuzzy and evolutionary), their applications, and selected fashionable pattern recognition methods. At present CI has no comprehensive foundations and is more a bag of tricks than a solid branch of science. The change of focus from methods to challenging problems is advocated, with CI defined as a part of computer and engineering sciences devoted to solution of non-algoritmizable problems. In this view AI is a part of CI focused on problems related to higher cognitive functions, while the rest of the CI community works on problems related to perception and control, or lower cognitive functions. Grand challenges on both sides of this spectrum are addressed
Quantum Interaction Approach in Cognition, Artificial Intelligence and Robotics
The mathematical formalism of quantum mechanics has been successfully
employed in the last years to model situations in which the use of classical
structures gives rise to problematical situations, and where typically quantum
effects, such as 'contextuality' and 'entanglement', have been recognized. This
'Quantum Interaction Approach' is briefly reviewed in this paper focusing, in
particular, on the quantum models that have been elaborated to describe how
concepts combine in cognitive science, and on the ensuing identification of a
quantum structure in human thought. We point out that these results provide
interesting insights toward the development of a unified theory for meaning and
knowledge formalization and representation. Then, we analyze the technological
aspects and implications of our approach, and a particular attention is devoted
to the connections with symbolic artificial intelligence, quantum computation
and robotics.Comment: 10 page
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