INFRINGER : a novel interactive multi-objective optimization method able to learn a decision maker’s preferences utilizing machine learning
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2020Copyright
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Tässä tutkielmassa kehitetään interaktiivinen menetelmä –
nimeltään INFRINGER – monitavoiteoptimoinnin ongelmien ratkaisemisen tueksi. Menetelmä
kykenee oppimaan päätöksentekijän mieltymykset (preferenssit), ja esittää mieltymyksiä
käyttäen arvofunktiota mallinaan. Arvofunktio mallinnetaan käyttäen koneoppia, jossa sovelletaan todennäköisyyksiä hyödyntäviä sääntöpohjaisia järjestelmiä. Kehitettyä menetelmää
hyödynnetään tapaustutkimuksessa, jossa päätöksentekijää tuetaan Suomen metsätalouteen
liittyvän monitavoitteisen optimointiongelman ratkaisemisessa. Tapaustutkimuksen tulosten
pohjalta kehitetyn menetelmän kykyä tukea päätöksentekijää, ja oppia päätöksentekijän mieltymykset,
arvioidaan. Lopuksi kehitettyä menetelmää verrataan lyhyesti vastaaviin kirjallisuudessa esiintyviin menetelmiin, ja menetelmän kelpoisuutta selitettävänä koneopin mallina pohditaan. An interactive method – INFRINGER – for solving multi-objective optimization
problems is developed in this thesis. The method is able to learn a decision maker’s preferences using a value function model. The value function is modelled using machine learning
in conjunction with belief-rule based systems. A case study, consisting of a problem in
Finnish forestation, is then conducted where a human decision maker is aided in the decision making process using the developed method. Based on the results of the case study, the
developed method is assessed in its ability to aid the decision maker to reach a satisfying solution, and its ability to elicit the decision maker’s preferences. Lastly, the method is briefly
compared qualitatively to other similar methods in existing literature, and the viability of the
method as a potential explainable model is briefly discussed.
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