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>> OptaPlanner is an AI constraint solver. It optimizes planning and scheduling problems, such as the Vehicle Routing Problem, Employee Rostering, Maintenance Scheduling, Task Assignment, School Timetabling, Cloud Optimization, Conference Scheduling, Job Shop Scheduling, Bin Packing and many more.

Just today I was moaning to my thesis advisor that most people entering AI today don't really know the history of AI before ca. 2012. He told me about a researcher he knew back in the 1970's who used to complain about the same thing exactly in machine learning meetings they had back then.

In any case, I wanted to ask- folks on HN who are interested in machine learning (or actively working in the field, one way or another), did you know that there is a thing such as "planning" (or "constraint solving") and that it's a part of AI?




Yeah, was generally aware that optimization ranging from (linear programming, MIP, genetic algorithms, PSO, simulated annealing, logic programming...etc) are generally considered to be in the AI group which also has everything from neural networks to gosh knows what.

I think AI is just one of those hard to describe terms. I remember sitting around with some friends over a decade ago arguing over what a "hipster" was lol. That's another term that is a little nebulous.


Good question!

SHort answer: Local Search is in the AI bible (AI a modern approach by Russell and Norvig), so OptaPlanner is part of AI.

Long answer: It depends how old you are. See https://www.optaplanner.org/blog/2017/09/07/DoesAIIncludeCon...


That's a good post, thanks for linking to it. It takes a good stab at answering "what is AI" (in the sense of what kind of computer techniques are AI), which is always a stickler. And it even has time for a very quick bit of history. I love it :)


I know about it but mostly because I had a professor who was really interested in all of this. I also think it is interesting that there has been quite a lot of improvements on this front without a lot of fanfare.


I think the reason is that these kinds of techniques are as old as nails. There's steady development but it's natural that there won't be as much excitement as for a fast growing (read: exploding) field as neural nets.




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