I'm trying to find a planning approach to solve a problem that attempts to model learning of new material. We assume that we only have one resource such as Wikipedia, which contains a list of articles represented as a vector of knowledge it contains and an effort to read that article.
Knowledge vector and effort
Before we start, we set a size for the vector, depending on the number of different types of knowledge. For example, we can define the items in the vector to be
(algebra, geometry, dark ages), and then 'measure' all the articles from this point of view. So, a math article will probably be
(5,7,0), since it will talk a lot about algebra and geometry but not about the dark ages. It will also have an effort to read it, which is simply an integer.
Given all the articles (represented as knowledge vectors with an effort), we want to find the optimal set of articles that help us to reach a knowledge goal (also represented as a vector).
So, a knowledge goal can be
(4,4,0), and it's enough to read an article
(2,3,0), since, when added, it adds up to the knowledge goal. We want to do this with minimal effort.
I've tried to some heuristics to find an approximation, but I was wondering if there is any state of the art strategic planning method that can be used instead?