Conservative decision-making and inference in uncertain dynamical systems

Thesis

The demand for automated decision making, learning and inference in uncertain, risk sensitive and dynamically changing situations presents a challenge: to design computational approaches that promise to be widely deployable and flexible to adapt on the one hand, while offering reliable guarantees on safety on the other. The tension between these desiderata has created a gap that, in spite of intensive research and contributions made from a wide range of communities, remains to be filled. T...

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Authors: Calliess, JP

• Publication date: 2014
• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: Artificial Intelligence; Machine Learning; Control; Multiagent Systems; Optimisation; Collision Avoidance; Dynamical Systems; System Identification
• Subjects: Information engineering; Vehicle guidance (information eng); Dynamical systems and ergodic theory (mathematics); Operations research,mathematical programming; Decision science; Scalable systems; Difference and functional equations; Applications and algorithms; Control engineering; Robotics