Planning based on classification by induction graph
This work addresses planning efficiency in AI systems, but it appears incremental as it adapts existing classification methods to a specific domain.
The paper tackles the problem of automated plan generation in AI by proposing a classification model using induction graphs to assign appropriate plans to new cases, aiming to optimize time compared to traditional scheduling algorithms.
In Artificial Intelligence, planning refers to an area of research that proposes to develop systems that can automatically generate a result set, in the form of an integrated decision-making system through a formal procedure, known as plan. Instead of resorting to the scheduling algorithms to generate plans, it is proposed to operate the automatic learning by decision tree to optimize time. In this paper, we propose to build a classification model by induction graph from a learning sample containing plans that have an associated set of descriptors whose values change depending on each plan. This model will then operate for classifying new cases by assigning the appropriate plan.