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Self-adaptive Artificial Intelligence

de Lemos, Rogerio, Grzes, Marek (2019) Self-adaptive Artificial Intelligence. In: 2019 IEEE/ACM 14th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS). . IEEE, New York, NY, USA ISBN 978-1-72813-368-3. (doi:10.1109/SEAMS.2019.00028) (KAR id:73259)


Machine learning tools, like deep neural networks, are perceived to be black boxes. That is, the only way of changing their internal data models is to retrain these models using different inputs. This is ineffective in dynamic systems that are prone to changes, like concept drift. A new promising solution is transparent artificial intelligence, based on the notions of interpretation and explanation, whose objective is to correlate the internal data models with predictions. The research question being addressed is whether we can have a self-adaptive machine learning system that is able to interpret and explain its data model in order for it to be controlled. In this position paper, we present our initial thoughts whether this can be achieved.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1109/SEAMS.2019.00028
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.87 Neural computers, neural networks
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Rogerio de Lemos
Date Deposited: 28 Mar 2019 16:27 UTC
Last Modified: 08 Dec 2022 22:16 UTC
Resource URI: (The current URI for this page, for reference purposes)

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