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Efficient and Reconfigurable Optimal Planning in Large-Scale Systems Using Hierarchical Finite State Machines
In this paper, we consider a planning problem for a large-scale system modelled as a hierarchical finite state machine (HFSM) and develop a control algorithm for computing optimal plans between any two states.
Hierarchical Finite State Machines for Efficient Optimal Planning in Large-scale Systems
The algorithm consists of an offline and an online step.
Computing Complexity-aware Plans Using Kolmogorov Complexity
We present two algorithms obtaining low-complexity policies, where the first algorithm obtains a low-complexity optimal policy, and the second algorithm finds a policy maximising performance while maintaining local (stage-wise) complexity constraints.
Hierarchical Game-Theoretic Planning for Autonomous Vehicles
This mutual dependence, best captured by dynamic game theory, creates a strong coupling between the vehicle's planning and its predictions of other drivers' behavior, and constitutes an open problem with direct implications on the safety and viability of autonomous driving technology.
