The development of artificial agents with bounded rationality is still an important challenge in artificial intelligence. In fact, when we are facing real-time problems with a large number of states, if we do not reason about the computational/informational resources of our agents, it is easy to encounter exponential complexities or state-explosions.
One way to solve this problem is taking inspiration from the study of attention in cognitive science. Attention can be considered as a filter in information processing that focuses only on relevant information, leaving out possibly all the useless computations. So to be focused only on relevant subsets of the state space of a problem can be the solution to increase the quality of our algorithms. Accordingly, it is important to understand how to represent relevance and being able to compute automatically what is relevant in a given situation.