Most decisions are guided by more than one desirable outcome. Think about health care, with doctors deciding about treatments for cancer by taking into account:
- health benefit for the patient
- toxicity (negative side-effects)
- cost of the treatment
Some information is present (the desirable outcomes like: high benefit, small cost, low toxicity,...) but some information is missing, the doctor may not have a complete and repeatable way to combine the different desirable outcomes in order to choose the best possible treatment. And the choice can be among hundreds of different treatments, some of them introduced recently, with new results about cure effectiveness being produced by the medical community on a daily basis. Indeed, a daunting task for a human person.
Many real-world problems like picking the best cancer treatment have a natural formulation as Multiobjective Optimization Problems (MOPs), in which multiple conflicting objectives need to be simultaneously optimized.
Because the most desirable combination of objectives is not known, the final decision maker must be kept in the loop: the additional information to guide the decision has to be extracted ...from his brain! The system interacts with the Decision Maker (DM) during optimization, by progressively focusing towards her preferred area in the decision space.
After some time, the system can learn about the preferences, so that it gradually becomes more and more automated. When human intelligence is coupled with big data, massive amounts of memory and computing power, much better decisions can be reached.
A recent paper on the topic:
Learning to diversify in complex interactive Multiobjective Optimization
by Roberto Battiti and his co-authors Dinara Mukhlisullina and Andrea Passerini,
received the "best paper award" at MIC 2013: The X Metaheuristics International Conference, Singapore, Aug 5-8, 2013.The motivation of the award was "for a groundbreaking contribution in this area."