D. Kumlander (Estonia)
NP-hard, artificial intelligence, graph, clique.
The paper discusses where the artificial intelligence principles can be applied in the graph theory NP-hard problems’ algorithms in order to make those quicker and better. The paper aim is to concentrate different ideas on making graph algorithms to be intelligent in one place instead of leaving a research process in this area to be still chaotic. The artificial intelligence for the graph algorithms can be divided into an internal one, where the intelligence is a part of an algorithm, and an external, where it is used in a form of a meta-algorithm providing an infrastructure and handling other algorithms (dedicated for solving this particular NP-hard problem). Another classification can be done by NP-hard problems algorithms’ elements to which the intelligence can be applied. Those can be summarised in a form of the following list: upper and lower bounds, algorithms’ internal elements and techniques, a meta-algorithm intelligence deciding which algorithm to run on a particular graph, and a testing intelligence. It is possible to conclude that using the internal and external artificial intelligences can both improve the NP-hard problems algorithms’ performance and produce adaptive algorithms that can be successfully used in different environments.
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