Evolving intelligent system

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The term Evolving was first used to describe an intelligent system in 1996 by B. Carse, T. Fogarty and A Munro [1] for a fuzzy rule-based controller where its parameters and structure were learnt simultaneously using a Genetic Algorithm. Years later, alternative methods to learn an evolving intelligent system (EIS) via Incremental learning were suggested as a neuro-fuzzy algorithm by N. Kasabov [2] in 1998 and a rule-based model by P. Angelov [3] in 1999.

EIS are usually associated with, streaming data and on-line (often real-time) modes of operation. They can be seen as adaptive intelligent systems. EIS assumes on-line adaptation of system structure in addition to the parameter adaptation which is usually associated with the term "incremental" from Incremental learning. They have been studied as a methodological solution to learn from streaming data exhibiting non-stationary behaviours by M. Sayed-Mouchaweh and E. Lughofer.[4]

An important sub-area of EIS is represented by Evolving Fuzzy Systems (EFS) (a comprehensive survey written by E. Lughofer including real-world applications can be found in [5]), which rely on fuzzy systems architecture and incrementally update, evolve and prune fuzzy sets and fuzzy rules on demand and on-the-fly. One of the major strengths of EFS, compared to other forms of evolving system models, is that they are able to support some sort of interpretability and understandability for experts and users.[6] This opens possibilities for enriched human-machine interaction's scenarios, where the users may "communicate" with an on-line evolving system in form of knowledge exchange (active learning (machine learning) and teaching). This concept is currently motivated and discussed in the evolving systems community under the term Human-Inspired Evolving Machines and respected as "one future" generation of "EIS".[7]

References[edit]

  1. ^ Carse, Brian, Terence C. Fogarty, and Alistair Munro. "Evolving fuzzy rule based controllers using genetic algorithms." Fuzzy sets and systems 80.3 (1996): 273-293.
  2. ^ N. Kasabov (1998), Evolving Fuzzy Neural Networks—Algorithms, Applications and Biological Motivation, Proc. of Iizuka'98, Iizuka, Japan, Oct.1998, World Sci., 271– 274 (1998).
  3. ^ P. P. Angelov (1999), Evolving Fuzzy Rule-Based Models, Proc. 8th IFSA World Congress, Taiwan, vol.1, pp.19-23.
  4. ^ M. Sayed-Mouchaweh, E. Lughofer (2012), Learning in Non-Stationary Environments: Methods and Applications. Springer, New York, 2012.
  5. ^ E. Lughofer (2011), Evolving Fuzzy Systems: Methodologies, Advanced Concepts and Applications. Studies in Fuzzy and Soft Computing, Springer.
  6. ^ E. Lughofer (2013), On-line Assurance of Interpretability in Evolving Fuzzy Systems – Achievements, New concepts and Open Issues. Information Sciences, vol. 251, pp. 22-46, 2013.
  7. ^ E. Lughofer (2011), Human Inspired Evolving Machines - The Next Generation of Evolving Intelligent Systems?. IEEE Newsletter, vol. 36, 2011.