Meta-Reasoning about Decisions in Autonomous Semi-Intelligent Systems

Danielson, M. & Ekenberg, L. ORCID: https://orcid.org/0000-0002-0665-1889 (2020). Meta-Reasoning about Decisions in Autonomous Semi-Intelligent Systems. In: Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence. pp. 42-46 New York: Association for Computing Machinery. ISBN 978-1-4503-7761-4 10.1145/3396474.3396476.

Full text not available from this repository.

Abstract

For intelligent systems to become autonomous in any real sense, they need an ability to make decisions on situations that were not entirely conceived of at compile-time. Machine learning algorithms are excellent in mimicking the behaviour of some gold standard role model, and this can include decision making by the role model. But once out of familiar contexts, the decision making becomes harder and needs an element of more independent probabilistic reasoning and decision making. This paper presents such a method based on a belief mass interpretation of the decision information, where the components are imprecise and thus uncertain by means of intervals.

Item Type:	Book Section
Research Programs:	Risk & Resilience (RISK)
Depositing User:	Luke Kirwan
Date Deposited:	30 Nov 2020 08:18
Last Modified:	27 Aug 2021 17:33
URI:	https://pure.iiasa.ac.at/16887

Actions (login required)

View Item