The importance of knowledge in AI for Decisions

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In aiD we will use models of the real world as a tool to form the best decisions. However, not all models are suitable for decision-making, and not all data provide good models. In some cases we can compensate by adding knowledge to the data driven model in the form of physical laws, simulations, geometric properties, causal or logic structures, or domain understanding. I will introduce some common frameworks (soft and hard-constrained) and illustrate strengths and challenges through real-life applications with examples from energy systems, maintenance, process industry etc.

Presented by:

Signe Riemer-Sørensen –  Co-Director aiD