R+D


Research - Development - Innovation

The contribution of our lab to science and technology can be divided into the following:

  • Computational Intelligence:
Computational intelligence (CI) is an offshoot of artificial intelligence. As an alternative to classical artificial intelligence it rather relies on heuristic algorithms such as in fuzzy systems, neural networks and evolutionary computation. In addition, computational intelligence also embraces techniques that use Swarm intelligence, Fractals and Chaos Theory, Artificial immune systems, Wavelets, etc.

Computational intelligence combines elements of learning, adaptation, evolution and Fuzzy logic (fuzzy sets) to create programs that are, in some sense, intelligent. Computational intelligence research does not reject statistical methods, but often gives a complementary view (as is the case with fuzzy systems). Artificial neural networks is a branch of computational intelligence that is closely related to machine learning. Computational intelligence is further closely associated with soft computing, connectionist systems and cybernetics.

  • Soft Computing:
Soft computing is a term applied to a field within computer science which is characterized by the use of inexact solutions to computationally-hard tasks such as the solution of NP-complete problems, for which an exact solution cannot be derived in polynomial time.

Soft Computing became a formal Computer Science area of study in the early 1990′s. Earlier computational approaches could model and precisely analyze only relatively simple systems. More complex systems arising in biology, medicine, the humanities, management sciences, and similar fields often remained intractable to conventional mathematical and analytical methods. That said, it should be pointed out that simplicity and complexity of systems are relative, and many conventional mathematical models have been both challenging and very productive. Soft computing deals with imprecision, uncertainty, partial truth, and approximation to achieve tractability, robustness and low solution cost. Components of soft computing include:
  • Neural networks (NN)
  • Fuzzy systems (FS)
  • Evolutionary computation (EC), including: evolutionary algorithms, harmony search, swarm intelligence.
  • Probability including: bayesian network, chaos theory.
  • Perceptron
Generally speaking, soft computing techniques resemble biological processes more closely than traditional techniques, which are largely based on formal logical systems, such as sentential logic and predicate logic, or rely heavily on computer-aided numerical analysis (as in finite element analysis). Soft computing techniques are intended to complement each other.

  • Robotics:
Robotics is the engineering science and technology of robots, and their design, manufacture, application, and structural disposition. Robotics is related to electronics, mechanics, and software.

Researchers who collaborate with our projects:
  • Uma Ramamurthy (Cognitive Computing Research Group, University of Memphis)
  • Pablo Rovarini Diaz and his Research Group (UTNFRT)
  • J. Vanschoren (Belgium)
  • Robert Berwick (Massachusetts Institute of Technology)
  • Jerry Mendel (South California University)
  • UTNFRER
  • UADER
  • UNLP