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In Decision Support System (DSS), we rank items based on predetermined weighted criteria. For example, we want to rank prospective programmers based on their working experience, required salary, set of skills, age, etc. We rank using weights for each criterion that we have previously defined. The simplest method is using Simple Additive Weighting (SAW).

As far as I know, DSS is included in knowledge-based AI (it's a mandatory subject in AI specialization in most universities in my country).

My question:

With the development of AI/ML/DL today, is there another modern approach that can be used to solve similar problems?

At first, I thought it's similar with Content-Based Recommender System, but it looks different as we don't have "user" in DSS.

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In my opinion, the sucess level of the Deep Learning models is perfectly suited for DSS. For example, if you have data you can built perfect Deep Reinforcement Model to rank programmers. In general AI models, specifically Deep models, are not mature enough to use without supervision of human being. But they are very suitable to build support systems.

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    $\begingroup$ I'm not the one who give you -1, but I think you should add more references for your answer. For example, how do you use RL to rank? $\endgroup$
    – malioboro
    Feb 6, 2021 at 4:21

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