Knowledge-Based Business and Computational Intelligence

ECTS - 3
Course level

Course prerequisites (start competences)
◦ Flexibility of speaking and writing in English
◦ Information Technology applications in business and basics of IT courses (ECDL etc)
◦ Mathematics of Data Management
◦ Some basic programming skills are preferred
◦ Analytical, evaluative, and innovative skills
◦ Self-motivation and Self-reliance

Course description & goals (final competences)

In many parts of the world, knowledge is being put to work to accelerate and deepen the development process, promoting innovation and helping to generate wealth and jobs. This course discusses advanced development strategies that take into account information and communication technology, knowledge-based infrastructure, innovation, and especially emphases the computational intelligence methods as prerequisites for in-depth exploration into the adaptive mechanisms that enable intelligent behavior in complex and changing environments. So, the course is integrating two main topics: knowledge-based business and computational intelligence methods.The main focus of the second part is centered on the computational modeling of biological and natural intelligent systems, encompassing swarm intelligence, fuzzy systems, artificial neutral networks, artificial immune systems and evolutionary computation.

Goals (final competences).
This course invites students to implement and problem solve real-world, complex problems within the knowledge-based computational intelligence framework. This framework will introduce students how to tackle new challenges, crises, emergent knowledge and complexity using self-organizing and adaptive intelligent systems.

Course contents (some chapters can be readapted)

Knowledge management
1.        Knowledge economies and business
2.        Knowledge assets
3.        E-business in the changing environments
4.        Social emergence
5.        Self-organization and evolution of social systems
6.        Complex adaptive systems

Computational intelligence
1.        Evolutionary computation
2.        Neural networks
3.        Fuzzy systems
4.        Hybrid systems
5.        Multiagent systems

Teaching methods
Lecturing (hands-on based presentation of the material)
Workshops (in the PC class using Virtual Lab and MatLab code)
Individual preparation of the project and PP presentations 

Course material
Hands-on based presentation of the material
Web links and on-line courses
o         Hemelrijk Charlotte (Ed.) (2005) Self-organisation and Evolution of Social Systems. Cambridge University Press, pp. 194.
o         Sawyer R. Keith (2005) Social Emergence: Societies as Complex Systems. Cambridge University Press, pp. 275.
o         Ormerod P. (1997). The Death of Economics. John Willey&Sons, NY, pp.230.
o         Russell C. Eberhart and Yuhui Shi (2007) Computational Intelligence: Concepts to Implementations. Morgan Kaumann Publishers, pp. 467.
o         Russell, S. J. and Norvig P. (2003). Artificial Intelligence:A Modern Approach. Englewood Cliffs, NJ: Prentice-Hall.
o         Wilson A. R. And Keil F. C. (Eds.)(2001) The MIT Encyclopedia of the Cognitive Sciences. MIT, pp. 965.
o         Gilbert N. and Troitzsch K. Simulation for the Social Scientist. Open University Press, 2003, pp. 273.
o         Tesfatsion L. and Judd K. (2006) Hanbook of Computational Economics: Agent-based Computational Economics, Vol. 2. Elsevier, pp. 1658. 


Workshop assignments 25%
Half-term exam25%
Individual project20%
Final exam30%

Exam retaking in during RIBA studies time

Contact for information
Prof. Darius Plikynas
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