Comparing Anytime Learning to Organic Computing
Thomas Dangl · viXra · 2019
In environments where finding the best solution to a given problem is computationally infeasible or undesirable due to other restrictions, the approach of anytime learning has become the de facto standard. Anytime learning allows intelligent systems to adapt and remain operational in a constantly changing environment. Based on observation of the environment, the underlying simulation model is changed to fit the task and the learning process begins anew. This process is expected to never terminate, therefore continually improving the set of available strategies. Optimal management of uncertainty in tasks, which require a solution in real time, can be achieved by assuming faulty yet improving output. Properties of such a system are not unlike those present in organic systems. This article aims to give an introduction to anytime learning in general as well as to show the similarities to organic computing in regards to the methods and strategies used in both domains.