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Found 7 papers, 1 papers with code
Deceptive Planning for Resource Allocation
We consider a team of autonomous agents that navigate in an adversarial environment and aim to achieve a task by allocating their resources over a set of target locations.
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.
Deceptive Decision-Making Under Uncertainty
We study the design of autonomous agents that are capable of deceiving outside observers about their intentions while carrying out tasks in stochastic, complex environments.
Physical-Layer Security via Distributed Beamforming in the Presence of Adversaries with Unknown Locations
In such a setting, we develop a periodic transmission strategy, i. e., a sequence of joint beamforming gain and artificial noise pairs, that prevents the adversaries from decreasing their uncertainty on the information sequence by eavesdropping on the transmission.
On the Complexity of Sequential Incentive Design
We model the agent's behavior as a Markov decision process, express its intrinsic motivation as a reward function, which belongs to a finite set of possible reward functions, and consider the incentives as additional rewards offered to the agent.
Optimization and Control
Collaborative Beamforming Under Localization Errors: A Discrete Optimization Approach
In particular, we formulate a discrete optimization problem to choose only a subset of agents to transmit the message signal so that the variance of the signal-to-noise ratio (SNR) received by the base station is minimized while the expected SNR exceeds a desired threshold.
Entropy Maximization for Markov Decision Processes Under Temporal Logic Constraints
Such a policy minimizes the predictability of the paths it generates, or dually, maximizes the exploration of different paths in an MDP while ensuring the satisfaction of a temporal logic specification.
