Decision-Making under Uncertainty: Human-Machine Teaming
Speaker: Nelly Bencomo – Durham, United KingdomTopic(s): Artificial Intelligence, Machine Learning, Computer Vision, Natural language processing , Computational Theory, Algorithms and Mathematics
Abstract
There is growing uncertainty about the environment of software systems. Therefore, how the system should behave under different contexts cannot be fully predicted at design time. It is considerations such as these that have led to the development of self-adaptive systems (SAS), which can dynamically and autonomously reconfigure their behaviour to respond to changing external conditions.
The scope of the talk is in the area of Software Engineering (SE) and Requirements Engineering (RE), and the development of techniques to quantify uncertainty to improve decision-making. The explicit treatment of uncertainty by the running system improves its judgment to make decisions supported by evaluating evidence found during runtime, possibly including the human-in-the-loop. I will also discuss how quantification of uncertainty can be used to improve requirements elicitation (using simulations, for example). The talk will cover different approaches to quantifying uncertainty and its role in Human-Machine Teaming.
About this Lecture
Number of Slides: 40 - 45Duration: 40 - 45 minutes
Languages Available: English, Spanish
Last Updated:
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