Papers
3
Total Citations
25
H-Index
3
About
Catherine Dezan’s research lies at the critical intersection of artificial intelligence and autonomous systems, where she develops robust decision-making frameworks for safe mission planning. Her work centers on two key areas: probabilistic reasoning using Bayesian Networks (BN) and sequential decision-making through Markov Decision Processes (MDPs). In her most cited work, “Embedded Bayesian Network Contribution for a Safe Mission Planning of Autonomous Vehicles” (18 citations), she demonstrates how BN-based intelligent monitors can incorporate environmental context to enhance vehicle safety—a foundational contribution to reliable autonomous navigation. Dezan further advances the field by systematically comparing reinforcement learning methods (Value Iteration, Policy Iteration, Q-Learning) for solving decision problems, providing practitioners with clear guidance on algorithm selection. Her innovative approach to “Reward Tuning for self-adaptive Policy in MDP based Distributed Decision-Making” addresses the complex challenge of multi-agent coordination, ensuring safe mission execution even in uncertain environments. Through these contributions, Dezan has established herself as a key figure in developing the theoretical and practical tools needed to make autonomous vehicles not just capable, but trustworthy.
Research Focus
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Top Papers
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