Zobrazeno 1 - 10
of 113
pro vyhledávání: '"Chattopadhyay, Arpan"'
We consider the problem of source sampling and transmission scheduling for age-of-information minimization in a system consisting of multiple energy harvesting (EH) sources and a sink node. At each time, one of the sources is selected by the schedule
Externí odkaz:
http://arxiv.org/abs/2408.02570
In cognitive systems, recent emphasis has been placed on studying the cognitive processes of the subject whose behavior was the primary focus of the system's cognitive response. This approach, known as inverse cognition, arises in counter-adversarial
Externí odkaz:
http://arxiv.org/abs/2407.16623
This paper addresses the problem of detecting false data injection (FDI) attacks in a distributed network without a fusion center, represented by a connected graph among multiple agent nodes. Each agent node is equipped with a sensor, and uses a Kalm
Externí odkaz:
http://arxiv.org/abs/2402.09743
Autor:
Das, Nirjhar, Chattopadhyay, Arpan
In this work, we propose a novel inverse reinforcement learning (IRL) algorithm for constrained Markov decision process (CMDP) problems. In standard IRL problems, the inverse learner or agent seeks to recover the reward function of the MDP, given a s
Externí odkaz:
http://arxiv.org/abs/2305.08130
Rapid advances in designing cognitive and counter-adversarial systems have motivated the development of inverse Bayesian filters. In this setting, a cognitive 'adversary' tracks its target of interest via a stochastic framework such as a Kalman filte
Externí odkaz:
http://arxiv.org/abs/2304.01698
Recent research in inverse cognition with cognitive radar has led to the development of inverse stochastic filters that are employed by the target to infer the information the cognitive radar may have learned. Prior works addressed this inverse cogni
Externí odkaz:
http://arxiv.org/abs/2303.10322
Autor:
Aniket, Ayush, Chattopadhyay, Arpan
We study learning in periodic Markov Decision Process (MDP), a special type of non-stationary MDP where both the state transition probabilities and reward functions vary periodically, under the average reward maximization setting. We formulate the pr
Externí odkaz:
http://arxiv.org/abs/2303.09629
Autor:
Singh, Himali, Chattopadhyay, Arpan
Multiple-input multiple-output (MIMO) radar has several advantages with respect to the traditional radar array systems in terms of performance and flexibility. However, in order to achieve high angular resolution, a MIMO radar requires a large number
Externí odkaz:
http://arxiv.org/abs/2302.14327
In counter-adversarial systems, to infer the strategy of an intelligent adversarial agent, the defender agent needs to cognitively sense the information that the adversary has gathered about the latter. Prior works on the problem employ linear Gaussi
Externí odkaz:
http://arxiv.org/abs/2210.00359
Counter-adversarial system design problems have lately motivated the development of inverse Bayesian filters. For example, inverse Kalman filter (I-KF) has been recently formulated to estimate the adversary's Kalman-filter-tracked estimates and hence
Externí odkaz:
http://arxiv.org/abs/2208.06683