Privacy-Preserving Policy Iteration for Decentralized POMDPs

Wu, Feng; Zilberstein, Shlomo; Chen, Xiaoping

doi:10.1609/aaai.v32i1.11584

Cited by 5 publications

(1 citation statement)

References 14 publications

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“…To address this issue, we used expectation maximization (EM) to estimate the model parameters. The EM algorithm is an established method for maximum likelihood estimation of model parameters in the presence of a latent process or missing observations [34]. Other approaches including fully Bayesian or variational-Bayesian approaches, can be applied to our modeling approach in the case that a prior distribution for the model parameters can be defined [35].…”

Section: Hi-dgd Model Parameters Inference Using Neural Activity and ...mentioning

confidence: 99%

Inferring cognitive state underlying conflict choices in verbal Stroop task using heterogeneous input discriminative-generative decoder model

et al. 2023

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The subthalamic nucleus (STN) of the basal ganglia interacts with the medial prefrontal cortex (mPFC) and shapes a control loop, specifically when the brain receives contradictory information from either different sensory systems or conflicting information from sensory inputs and prior knowledge that developed in the brain. Experimental studies demonstrated that significant increases in theta activities (2-8 Hz) in both the STN and mPFC as well as increased phase synchronization between mPFC and STN are prominent features of conflict processing. While these neural features reflect the importance of STN-mPFC circuitry in conflict processing, a low-dimensional representation of the mPFC-STN interaction referred to as a cognitive state, that links neural activities generated by these sub-regions to behavioral signals (e.g., the response time), remains to be identified. Here, we propose a new model, namely, the heterogeneous input discriminative-generative decoder (HI-DGD) model, to infer a cognitive state underlying decision-making based on neural activities (STN and mPFC) and behavioral signals (individuals’ response time) recorded in 10 Parkinson’s disease patients while they performed a Stroop task. PD patients may have conflict processing which is quantitatively (may be qualitative in some) different from healthy population. Using extensive synthetic and experimental data, we showed that the HI-DGD model can diffuse information from neural- and behavioral data simultaneously and estimate cognitive states underlying conflict and non-conflict trials significantly better than traditional methods. Additionally, the HI-DGD model identified which neural features made significant contributions to conflict and non-conflict choices. Interestingly, the estimated features match well with those reported in experimental studies. Finally, we highlight the capability of the HI-DGD model in estimating a cognitive state from a single trial of observation, which makes it appropriate to be utilized in closed-loop neuromodulation systems.

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Section: Hi-dgd Model Parameters Inference Using Neural Activity and ...mentioning

confidence: 99%

Inferring cognitive state underlying conflict choices in verbal Stroop task using heterogeneous input discriminative-generative decoder model

et al. 2023

View full text Add to dashboard Cite

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Privacy preserving planning in multi-agent stochastic environments

Hefner

Shani

Stern

2022

Auton Agent Multi-Agent Syst

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More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence

Zhu¹,

Ye²,

Wang³

et al. 2022

IEEE Trans. Knowl. Data Eng.

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Artificial Intelligence (AI) has attracted a great deal of attention in recent years. However, alongside all its advancements, problems have also emerged, such as privacy violations, security issues and model fairness. Differential privacy, as a promising mathematical model, has several attractive properties that can help solve these problems, making it quite a valuable tool. For this reason, differential privacy has been broadly applied in AI but to date, no study has documented which differential privacy mechanisms can or have been leveraged to overcome its issues or the properties that make this possible. In this paper, we show that differential privacy can do more than just privacy preservation. It can also be used to improve security, stabilize learning, build fair models, and impose composition in selected areas of AI. With a focus on regular machine learning, distributed machine learning, deep learning, and multi-agent systems, the purpose of this article is to deliver a new view on many possibilities for improving AI performance with differential privacy techniques.

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Privacy-Preserving Policy Iteration for Decentralized POMDPs

Cited by 5 publications

References 14 publications

Inferring cognitive state underlying conflict choices in verbal Stroop task using heterogeneous input discriminative-generative decoder model

Inferring cognitive state underlying conflict choices in verbal Stroop task using heterogeneous input discriminative-generative decoder model

Privacy preserving planning in multi-agent stochastic environments

More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence

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