Little bit of a late start for me so I am starting with Geoffrey De Smet from Red Hat talking about constraint planning. He points out that some decisions cannot be easily solved with rules-based approaches – they can be described as decision (and as a DMN decision model in our experience) but not readily made with rules and decision tables only. His key point is that different decision problems require different technology
- Eligibility is a rules problem
- License plate recognition is a neural network (analytic) problem
- Roster scheduling is a constraint planning problem
And our experience is that you can do this decision by decision in a decision model too, making it easy to identify the right technology and to combine them.
He went into some detail on the difference between hard and soft constraints and on the interesting way in which the Red Hat planner leverages the Red Hat rules format and engine to handle constraint definition, score solutions etc. They support various approaches to planning too, allowing you to mix and match rules-based constraints and various algorithms for searching for a solution. The integration also allows for some incremental work, taking advantage of the standard rule engine features.
I wrote about some of the early work around Drools Planner back in 2011.
I went next, presenting on the role of decision models in analytic excellence