Artificial Intelligence


Comparing Anytime Learning to Organic Computing

Authors: Thomas Dangl

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.

Comments: 5 Pages.

Download: PDF

Submission history

[v1] 2019-03-11 09:03:33

Unique-IP document downloads: 13 times is a pre-print repository rather than a journal. Articles hosted may not yet have been verified by peer-review and should be treated as preliminary. In particular, anything that appears to include financial or legal advice or proposed medical treatments should be treated with due caution. will not be responsible for any consequences of actions that result from any form of use of any documents on this website.

Add your own feedback and questions here:
You are equally welcome to be positive or negative about any paper but please be polite. If you are being critical you must mention at least one specific error, otherwise your comment will be deleted as unhelpful.

comments powered by Disqus