For at kunstig intelligente kontrollsystemer skal kunne bli gitt ansvaret for å ta beslutninger i sanntid i en industrisetting, må AI metodene blir mer robuste. Robusthet er evnen til å yte stabilt under usikkerhet, når det oppstår forstyrrelser, når modellen er feil og uvanlige hendelser inntreffer. Robustheten til kunstig intelligente kontrollmetoder er mindre viktig i virtuelle settinger hvor kostnaden ved en feil er liten sammenlignet med fysiske systemer hvor feil er forbundet med risiko for menneskeliv, eiendeler og omgivelsene.
I dette prosjektet søker vi å utvikle en dypere forståelse for robuste kontrollmetoder basert på kunstig intelligens og hvordan man kan evaluere robustheten i en industrisetting. Brukstilfeller de utviklede metodene skal evalueres for er vannkraft, billading, oljeproduksjon og styring av strømforbruk i smarte hus.
Robust is one of the three pillars of trustworthy AI according to EU’s High Level Expert Group on AI. The Norwegian government and the OECD also mention robustness as a key property for AI to be deployed in safety and business-critical applications. Robustness in this setting is the ability to keep performance and stability in the presence of uncertainty, disturbances, modelling errors, and out-of-distribution examples. The issue of robustness is particularly problematic in control of physical systems as it is associated with risks to human safety, assets, and the operating environment.
Control systems manages and commands other devices or systems and are used in safety and business-critical applications. Intelligent control systems is the application of AI in control systems to achieve intelligent characteristics such as adaptation, learning and planning under uncertainty. Even though intelligent controllers have been shown to outperform traditional controllers in several domains, the industrial uptake is limited by the lack of robustness in the underlying methodology. Currently there is a gap between the research on very advanced methods tested in a controllable and predictable environment and the industrial settings where the environment is neither completely known nor controllable and information can be scarce, uncertain and of low quality. This gap can be closed by developing novel methods for robust intelligent control that are tested in both controlled environments and in industrial systems.
RICO is a collaboration between researchers from the NTNU and SINTEF and the industry represented by TrønderEnergi, Ohmia Retail and Solution Seeker. New and robust methods for intelligent control and methods for evaluating their robustness will be developed and evaluated in industrial settings to meet the society’s and industry’s standards for deployment of trustworthy AI systems in industry.