6533b862fe1ef96bd12c7705
RESEARCH PRODUCT
Data-driven Interactive Multiobjective Optimization : Challenges and a Generic Multi-agent Architecture
Kaisa MiettinenBekir AfsarDmitry Podkopaevsubject
decision supportComputer science020209 energyCompromisemedia_common.quotation_subjectpäätöksentekopäätöksentukijärjestelmät02 engineering and technologycomputer.software_genreMulti-objective optimizationField (computer science)Data-drivenIntelligent agentcomputational intelligence0202 electrical engineering electronic engineering information engineeringmulti-agent systemsAgent architecturemultiple criteria optimizationGeneral Environmental Sciencemedia_commoninteractive methodsmonitavoiteoptimointiagentsRisk analysis (engineering)data-driven decision makinginteraktiivisuusälykkäät agentitGeneral Earth and Planetary Sciences020201 artificial intelligence & image processingcomputerdescription
In many decision making problems, a decision maker needs computer support in finding a good compromise between multiple conflicting objectives that need to be optimized simultaneously. Interactive multiobjective optimization methods have a lot of potential for solving such problems. However, the growth of complexity in problem formulations and the abundance of data bring new challenges to be addressed by decision makers and method developers. On the other hand, advances in the field of artificial intelligence provide opportunities in this respect. We identify challenges and propose directions of addressing them in interactive multiobjective optimization methods with the help of multiple intelligent agents. We describe a generic architecture of enhancing interactive methods with specialized agents to enable more efficient and reliable solution processes and better support for decision makers. peerReviewed
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2020-01-01 |