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Dr. Kokolakis’s research develops rigorous game-theoretic control and learning frameworks for trustworthy autonomy. Trustworthy autonomy refers to intelligent systems that are reliable and equipped with interpretable decision-making mechanisms. The themes below outline the main directions of this work.
Learning-Enabled Bounded Rational Multiagent Autonomy
Learning-Enabled Bounded Rational Multiagent Autonomy
Core Idea
Core Idea
Inspired by theory of mind, we develop an interpretable Level-k Thinking model to characterize the reasoning depth of bounded-rational agents. Online learning enables autonomous agents to infer their opponents’ reasoning levels and adapt their strategies in real time.
Contributions
Contributions
Level-k Thinking model for characterizing bounded rationality via interpretable hierarchical reasoning
Online inference of opponents’ reasoning levels enabling real-time strategic adaptation
Rationality-aware multi-agent coordination using a learning-based assignment mechanism
Non-Asymptotic Reinforcement Learning for Safe Autonomy
Non-Asymptotic Reinforcement Learning for Safe Autonomy
Core Idea
Core Idea
Using Lyapunov design, we develop reinforcement learning algorithms with non-asymptotic convergence guarantees, enabling robust, safe, and time-critical decision-making in unknown adversarial environments.
Contributions
Contributions
Finite-time safe learning for pursuit–evasion games using Gaussian processes to model unknown obstacles
Fixed-time learning of backward reachable sets for time-critical safety verification
Predefined-time reinforcement learning for optimal feedback control
Physics-Informed Learning for Safe and Resilient Time-Critical Autonomy
Physics-Informed Learning for Safe and Resilient Time-Critical Autonomy
Core Idea
Core Idea
We develop an adversarial physics-informed learning framework that synthesizes robust optimal control strategies with guaranteed safety and predefined-time stability under worst-case disturbances.
Contributions
Contributions
A game-theoretic control framework for robust optimal safe predefined-time stabilization under adversarial disturbances
An adversarial physics-informed learning architecture that embeds safety constraints and predefined-time stability conditions into the training process
Generative AI-Enabled Safe Information-Theoretic Control for Physical Autonomy
Generative AI-Enabled Safe Information-Theoretic Control for Physical Autonomy
Core Idea
Core Idea
Leveraging parallel execution and historical experience data, we develop generative AI-enabled information-theoretic decision-making mechanisms to synthesize safe, efficient, robust, and adaptive predictive control strategies for autonomous systems performing iterative tasks in uncertain physical environments.
Contributions
Contributions
A generative AI-enabled information-theoretic control framework for safe, robust, efficient, and adaptive decision-making under uncertainty
Parallel trajectory sampling enables real-time optimization that balances safety and performance
Historical experience data is used to refine expressive predictive models, enabling continual performance improvement while preserving safety
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