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Found 8 papers, 0 papers with code
Task-Oriented Active Perception and Planning in Environments with Partially Known Semantics
Furthermore, as new data arrive, the belief over the atomic propositions evolves and, subsequently, the planning strategy adapts accordingly.
Formal Methods for Autonomous Systems
Formal methods refer to rigorous, mathematical approaches to system development and have played a key role in establishing the correctness of safety-critical systems.
No-Regret Learning in Dynamic Stackelberg Games
The regret of the proposed learning algorithm is independent of the size of the state space and polynomial in the rest of the parameters of the game.
Multiple Plans are Better than One: Diverse Stochastic Planning
In planning problems, it is often challenging to fully model the desired specifications.
Online Active Perception for Partially Observable Markov Decision Processes with Limited Budget
In applications in which the agent does not have prior knowledge about the available information sources, it is crucial to synthesize active perception strategies at runtime.
Identifying Sparse Low-Dimensional Structures in Markov Chains: A Nonnegative Matrix Factorization Approach
We formulate the task of representation learning as that of mapping the state space of the model to a low-dimensional state space, called the kernel space.
Perception-Aware Point-Based Value Iteration for Partially Observable Markov Decision Processes
However, in a variety of real-world scenarios the agent has an active role in its perception by selecting which observations to receive.
Counterexamples for Robotic Planning Explained in Structured Language
Automated techniques such as model checking have been used to verify models of robotic mission plans based on Markov decision processes (MDPs) and generate counterexamples that may help diagnose requirement violations.
