@misc{Guez_Allon_Support_1992,
 author={Guez, Allon and Ahmad, Ziauddin},
 copyright={Creative Commons Attribution BY 4.0 license},
 address={Warszawa},
 journal={Książka = Book},
 howpublished={online},
 year={1992},
 publisher={Instytut Badań Systemowych. Polska Akademia Nauk},
 publisher={Systems Research Institute. Polish Academy of Sciences},
 language={eng},
 abstract={We believe that an essential feature in machine learning is the real time satisfaction of multiple objectives such as identification, tracking etc. The machine learning problem may be viewed as a nonlinear adaptive control problem where the environment plays the role of the 'plant', while the learner is the controller. Multiobjective optimization (MOO) in the control problem typically deals w1th simultaneous optrmlzatton of more than one objecttve, where cach objecttve is descnbed via a cost functional. In sucha situation there of ten exists a region of tradeoff wherein one cost may be improved at the expense of others. Such a region is called the Pareto optima (PO) set. A parameterlzation of this set simplifies the attainment of the existing tradeoff. Working within the Pareto set guaranties optimum tradeoff. We present two examples for linear time invariant systems. These examples help illustrate different issues involved in this matter.},
 title={Support systems for decision and negotiation processes * Volume 1 * Optimization of multiple objectives in control of uncertain systems},
 type={Text},
 URL={http://rcin.org.pl/Content/197784/PDF/KS-1992-02-R25.pdf},
}