QUALITATIVE MODELING AND INTERNET

1. QUALITATIVE MODELING AND INTERNET Franjo Jović, Ninoslav Slavek and Damir Blažević University of J.J. Strossmayer in Osijek Faculty of Electrical Engineering in OsijekKneza Trpimir 2B, 31000 Osijek, Croatia

2. Presentation overview • The nature of internet • Teleonomics • The nature of internet models • Technologies of artificial learning • Qualitative modeling • Abstractions in state learning • Applications from internet analogies • Internet design hints

3. The nature of internet • Internet:”The ultimate goal is sharing ideas and information”(Rob Malda) • Forms and norms are getting tight • When you change things, they get cranky • Thus JavaScript and XML are here to stay • Internet is just a big interactive online application • So, what’s up in the internet design?

4. Teleonomics • Since 1956 when Carnap introduced the idea in the world of logic it was ment for computers in a goal driven world • One can span Voronoi diagrams everywhere - just naming goal space and time • In the individualized world teleonomics is the freshman in the socialization school • How to recognize teleonomics on internet? • Does it matter?

5. The nature of internet models • Primary model: Markov chain • Minimum energy principle:Periodic and continuous nature of the information source • Intelligent agent as information source: Learning in a periodic system. Intelligent agent. Internet and organization of intelligent agents. The triplet: action – state – reward • Models for stochasticity, periodicity and hidden information (teleonomics!)

6. Technologies of artificial learning • Model driven AL (the world of words) • how to build a model? • Trial driven AL (the world of actions) • what step to take? • Combinational AL (a combined action - word world) • build a model from the step and make step from the model?

7. Qualitative modeling • System actions are obtained from state estimations: usually a quantitative addition form of the system state value approximation is obtained • The reinforcement learning algorithm can learn values for the parameter set  such that the evaluation function V’ approximates the true utility function.

8. Qualitative modeling/2 • The true utility function may not be not known at all. However the algebraic forms can indicate on its nature. So, addition operation indicate that components contribute to the utility, but for the constant utility value they compete: increase of the weight of one component demand decrease in other component(s) • Other algebraic forms obtained from qualitative modeling introduce cooperative component (subtraction) and two synergistic effects: multiplication and division • Algebraic forms are obtained from ranked similarity function of the algebraic forms and goal - teleonomic - function by a genetic algoritm procedure

9. Abstractions in state learning • Set of models is obtained by normalization of system descriptors and introducing abstractions (such as linear reward increase) • Normalized rewards is optimized for all abstractions of all previous situations • The value Q[s,a] is obtained by optimization from the current state and normalized reward signal

10. Applications from internet analogy • Analogy in distributed natural resource - oil-field porosity estimation from geo-acoustic and laboratory measurements • Analogy in distributed artificial pool - retail business optimized - client behavior obtained from a typical set of articles bought

11. Oil-field porosity

12. Internet design hints • Automatic information retrieval from internet sources by using additive set value approximation from quantitative world description dominates • Qualitative world description is closer to information and simpler for goal function introduction • Redesign of the automatic information retrieval from the internet is expected

13. Thank you for your attention!