Lazy Fully Probabilistic Design: Application Potential
The article addresses a lazy learning approach to fully probabilistic decision making when a decision maker (human or artificial) uses incomplete knowledge of environment and faces high computational limitations. The resulting lazy Fully Probabilistic Design (FPD) selects a decision strategy that moves a probabilistic description of the closed decision loop to a pre-specified ideal description. The lazy FPD uses currently observed data to find past closed-loop similar to the actual ideal model. The optimal decision rule of the closest model is then used in the current step. The effectiveness and capability of the proposed approach are manifested through example.
KeywordsLazy learning Fully Probabilistic Design Decision making Linear quadratic gaussian control
The authors would like to thank Miroslav Kárný for valuable discussions and comments. The research has been partially supported by the Czech Science Foundation, project GA16-09848S.
- 1.Kárný, M., et al. (eds.): Optimized Bayesian Dynamic Advising: Theory and algorithms. Springer, London (2006). https://doi.org/10.1007/1-84628-254-3
- 6.Powell, W.B.: Approximate Dynamic Programming: Solving the Curses of Dimensionality. vol. 703. Wiley (2007)Google Scholar
- 7.Aha, D.W.: Artif. Intell. Rev. Special Issue Lazy Learn. 11, 1–5 (1997)Google Scholar
- 8.Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Commun. 7(1), 39–59 (1994)Google Scholar
- 9.Bitanti, S., Picci, G.: Identification, adaptation, learning. NATO ASI Series F on Computer and Systems Sciences (1996)Google Scholar
- 16.Gil, I.A., Barrientos, A., Del Cerro, J.: Attitude control of a minihelicopter in hover using different types of control. Revista Técnica de la Facultad de Ingeniería. Universidad del Zulia 29(3) (2006)Google Scholar
- 19.Kárný, M., Guy, T.V.: Preference elicitation in fully probabilistic design of decision strategies. In: Proceedings of the 49th IEEE Conference on Decision and Control (2010)Google Scholar
- 20.Braziunas, D., Boutilier, C.: Preference elicitation and generalized additive utility (nectar paper). In: Proceedings of the 21st National Conference on AI (AAAI-2006), Boston, MA (2006)Google Scholar