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Found 11 papers, 1 papers with code
On the Effect of Quantization on Dynamic Mode Decomposition
Dynamic Mode Decomposition (DMD) is a widely used data-driven algorithm for estimating the Koopman Operator. This paper investigates how the estimation process is affected when the data is quantized.
Efficient Communication for Pursuit-Evasion Games with Asymmetric Information
The outcome of the game is determined by the control strategies of the players and the communication strategy between the sensor and the pursuer.
Optimal Intermittent Sensing for Pursuit-Evasion Games
We consider a class of pursuit-evasion differential games in which the evader has continuous access to the pursuer's location, but not vice-versa.
Planning Visual Inspection Tours for a 3D Dubins Airplane Model in an Urban Environment
Four sampling algorithms are proposed for sampling vehicle configurations within each visibility volume to define vertices of the underlying DTSPN.
A Linear Programming Approach for Resource-Aware Information-Theoretic Tree Abstractions
It is shown that the resulting information-theoretic abstraction problem over the space of multi-resolution trees can be formulated as a integer linear programming (ILP) problem.
Sensor Scheduling for Linear Systems: A Covariance Tracking Approach
We propose a convex relaxation to the sensor design problem and a reference covariance trajectory is obtained from solving the relaxed sensor design problem.
Multi-Agent Consensus Subject to Communication and Privacy Constraints
The adversaries are able to listen to the inter-agent communications and try to estimate the state of the agents.
Information-Theoretic Abstractions for Resource-Constrained Agents via Mixed-Integer Linear Programming
In this paper, a mixed-integer linear programming formulation for the problem of obtaining task-relevant, multi-resolution, graph abstractions for resource-constrained agents is presented.
Information-Theoretic Abstractions for Planning in Agents with Computational Constraints
In this paper, we develop a framework for path-planning on abstractions that are not provided to the agent a priori but instead emerge as a function of the available computational resources.
Q-Search Trees: An Information-Theoretic Approach Towards Hierarchical Abstractions for Agents with Computational Limitations
In this paper, we develop a framework to obtain graph abstractions for decision-making by an agent where the abstractions emerge as a function of the agent's limited computational resources.
Optimal Controller and Quantizer Selection for Partially Observable Linear-Quadratic-Gaussian Systems
In this paper, we consider joint optimal controller synthesis and quantizer scheduling for a partially observed Quantized-Feedback Linear-Quadratic-Gaussian (QF-LQG) system, where the measurements are quantized before being sent to the controller.
