Search Results for author: Milind Tambe

Found 39 papers, 11 papers with code

Facilitating human-wildlife cohabitation through conflict prediction

no code implementations22 Sep 2021 Susobhan Ghosh, Pradeep Varakantham, Aniket Bhatkhande, Tamanna Ahmad, Anish Andheria, Wenjun Li, Aparna Taneja, Divy Thakkar, Milind Tambe

With increasing world population and expanded use of forests as cohabited regions, interactions and conflicts with wildlife are increasing, leading to large-scale loss of lives (animal and human) and livelihoods (economic).

Robust Restless Bandits: Tackling Interval Uncertainty with Deep Reinforcement Learning

1 code implementation4 Jul 2021 Jackson A. Killian, Lily Xu, Arpita Biswas, Milind Tambe

To make RMABs more useful in settings with uncertain dynamics: (i) We introduce the Robust RMAB problem and develop solutions for a minimax regret objective when transitions are given by interval uncertainties; (ii) We develop a double oracle algorithm for solving Robust RMABs and demonstrate its effectiveness on three experimental domains; (iii) To enable our double oracle approach, we introduce RMABPPO, a novel deep reinforcement learning algorithm for solving RMABs.

Multi-agent Reinforcement Learning

Q-Learning Lagrange Policies for Multi-Action Restless Bandits

1 code implementation22 Jun 2021 Jackson A. Killian, Arpita Biswas, Sanket Shah, Milind Tambe

Multi-action restless multi-armed bandits (RMABs) are a powerful framework for constrained resource allocation in which $N$ independent processes are managed.

Multi-Armed Bandits Q-Learning

Robust Reinforcement Learning Under Minimax Regret for Green Security

1 code implementation15 Jun 2021 Lily Xu, Andrew Perrault, Fei Fang, Haipeng Chen, Milind Tambe

We formulate the problem as a game between the defender and nature who controls the parameter values of the adversarial behavior and design an algorithm MIRROR to find a robust policy.

Decision Making

Contingency-Aware Influence Maximization: A Reinforcement Learning Approach

1 code implementation13 Jun 2021 Haipeng Chen, Wei Qiu, Han-Ching Ou, Bo An, Milind Tambe

Empirical results show that our method achieves influence as high as the state-of-the-art methods for contingency-aware IM, while having negligible runtime at test phase.

Combinatorial Optimization

Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Problems by Reinforcement Learning

no code implementations6 Jun 2021 Kai Wang, Sanket Shah, Haipeng Chen, Andrew Perrault, Finale Doshi-Velez, Milind Tambe

In the predict-then-optimize framework, the objective is to train a predictive model, mapping from environment features to parameters of an optimization problem, which maximizes decision quality when the optimization is subsequently solved.

Learn to Intervene: An Adaptive Learning Policy for Restless Bandits in Application to Preventive Healthcare

no code implementations17 May 2021 Arpita Biswas, Gaurav Aggarwal, Pradeep Varakantham, Milind Tambe

In many public health settings, it is important for patients to adhere to health programs, such as taking medications and periodic health checks.

Q-Learning

Active Screening for Recurrent Diseases: A Reinforcement Learning Approach

no code implementations7 Jan 2021 Han-Ching Ou, Haipeng Chen, Shahin Jabbari, Milind Tambe

However, given the limited number of health workers, only a small subset of the population can be visited in any given time period.

Combinatorial Optimization Curriculum Learning

Reinforcement Learning for Unified Allocation and Patrolling in Signaling Games with Uncertainty

no code implementations18 Dec 2020 Aravind Venugopal, Elizabeth Bondi, Harshavardhan Kamarthi, Keval Dholakia, Balaraman Ravindran, Milind Tambe

We therefore first propose a novel GSG model that combines defender allocation, patrolling, real-time drone notification to human patrollers, and drones sending warning signals to attackers.

Decision Making Multiagent Systems

Collapsing Bandits and Their Application to Public Health Intervention

1 code implementation NeurIPS 2020 Aditya Mate, Jackson Killian, Haifeng Xu, Andrew Perrault, Milind Tambe

Our main contributions are as follows: (i) Building on the Whittle index technique for RMABs, we derive conditions under which the Collapsing Bandits problem is indexable.

Enhancing Poaching Predictions for Under-Resourced Wildlife Conservation Parks Using Remote Sensing Imagery

no code implementations20 Nov 2020 Rachel Guo, Lily Xu, Drew Cronin, Francis Okeke, Andrew Plumptre, Milind Tambe

To ensure under-resourced parks have access to meaningful poaching predictions, we introduce the use of publicly available remote sensing data to extract features for parks.

Dual-Mandate Patrols: Multi-Armed Bandits for Green Security

1 code implementation14 Sep 2020 Lily Xu, Elizabeth Bondi, Fei Fang, Andrew Perrault, Kai Wang, Milind Tambe

Conservation efforts in green security domains to protect wildlife and forests are constrained by the limited availability of defenders (i. e., patrollers), who must patrol vast areas to protect from attackers (e. g., poachers or illegal loggers).

Multi-Armed Bandits

Tracking disease outbreaks from sparse data with Bayesian inference

no code implementations12 Sep 2020 Bryan Wilder, Michael J. Mina, Milind Tambe

For example, case counts may be sparse when only a small fraction of infections are caught by a testing program.

Bayesian Inference Epidemiology +1

Collapsing Bandits and Their Application to Public Health Interventions

no code implementations5 Jul 2020 Aditya Mate, Jackson A. Killian, Haifeng Xu, Andrew Perrault, Milind Tambe

(ii) We exploit the optimality of threshold policies to build fast algorithms for computing the Whittle index, including a closed-form.

Automatically Learning Compact Quality-aware Surrogates for Optimization Problems

1 code implementation NeurIPS 2020 Kai Wang, Bryan Wilder, Andrew Perrault, Milind Tambe

Solving optimization problems with unknown parameters often requires learning a predictive model to predict the values of the unknown parameters and then solving the problem using these values.

Portfolio Optimization

Fair Influence Maximization: A Welfare Optimization Approach

no code implementations14 Jun 2020 Aida Rahmattalabi, Shahin Jabbari, Himabindu Lakkaraju, Phebe Vayanos, Max Izenberg, Ryan Brown, Eric Rice, Milind Tambe

Under this framework, the trade-off between fairness and efficiency can be controlled by a single inequality aversion design parameter.

Fairness

AI for Social Impact: Learning and Planning in the Data-to-Deployment Pipeline

no code implementations16 Dec 2019 Andrew Perrault, Fei Fang, Arunesh Sinha, Milind Tambe

With the maturing of AI and multiagent systems research, we have a tremendous opportunity to direct these advances towards addressing complex societal problems.

Solving Online Threat Screening Games using Constrained Action Space Reinforcement Learning

no code implementations20 Nov 2019 Sanket Shah, Arunesh Sinha, Pradeep Varakantham, Andrew Perrault, Milind Tambe

To solve the online problem with a hard bound on risk, we formulate it as a Reinforcement Learning (RL) problem with constraints on the action space (hard bound on risk).

MIPaaL: Mixed Integer Program as a Layer

no code implementations12 Jul 2019 Aaron Ferber, Bryan Wilder, Bistra Dilkina, Milind Tambe

It has been successfully applied to several limited combinatorial problem classes, such as those that can be expressed as linear programs (LP), and submodular optimization.

Decision Making

Influence maximization in unknown social networks: Learning Policies for Effective Graph Sampling

1 code implementation8 Jul 2019 Harshavardhan Kamarthi, Priyesh Vijayan, Bryan Wilder, Balaraman Ravindran, Milind Tambe

A serious challenge when finding influential actors in real-world social networks is the lack of knowledge about the structure of the underlying network.

Graph Sampling

End to end learning and optimization on graphs

1 code implementation NeurIPS 2019 Bryan Wilder, Eric Ewing, Bistra Dilkina, Milind Tambe

However, graphs or related attributes are often only partially observed, introducing learning problems such as link prediction which must be solved prior to optimization.

Link Prediction

End-to-End Game-Focused Learning of Adversary Behavior in Security Games

no code implementations3 Mar 2019 Andrew Perrault, Bryan Wilder, Eric Ewing, Aditya Mate, Bistra Dilkina, Milind Tambe

Stackelberg security games are a critical tool for maximizing the utility of limited defense resources to protect important targets from an intelligent adversary.

Group-Fairness in Influence Maximization

1 code implementation3 Mar 2019 Alan Tsang, Bryan Wilder, Eric Rice, Milind Tambe, Yair Zick

Influence maximization is a widely used model for information dissemination in social networks.

Computer Science and Game Theory Social and Information Networks

Learning to Prescribe Interventions for Tuberculosis Patients Using Digital Adherence Data

no code implementations5 Feb 2019 Jackson A. Killian, Bryan Wilder, Amit Sharma, Daksha Shah, Vinod Choudhary, Bistra Dilkina, Milind Tambe

Digital Adherence Technologies (DATs) are an increasingly popular method for verifying patient adherence to many medications.

Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization

no code implementations14 Sep 2018 Bryan Wilder, Bistra Dilkina, Milind Tambe

These components are typically approached separately: a machine learning model is first trained via a measure of predictive accuracy, and then its predictions are used as input into an optimization algorithm which produces a decision.

Combinatorial Optimization

Mitigating the Curse of Correlation in Security Games by Entropy Maximization

no code implementations11 Mar 2017 Haifeng Xu, Milind Tambe, Shaddin Dughmi, Venil Loyd Noronha

To mitigate this issue, we propose to design entropy-maximizing defending strategies for spatio-temporal security games, which frequently suffer from CoC.

Pilot Testing an Artificial Intelligence Algorithm That Selects Homeless Youth Peer Leaders Who Promote HIV Testing

no code implementations19 Aug 2016 Eric Rice, Robin Petering, Jaih Craddock, Amanda Yoshioka-Maxwell, Amulya Yadav, Milind Tambe

To pilot test an artificial intelligence (AI) algorithm that selects peer change agents (PCA) to disseminate HIV testing messaging in a population of homeless youth.

Using Social Networks to Aid Homeless Shelters: Dynamic Influence Maximization under Uncertainty - An Extended Version

no code implementations30 Jan 2016 Amulya Yadav, Hau Chan, Albert Jiang, Haifeng Xu, Eric Rice, Milind Tambe

This paper presents HEALER, a software agent that recommends sequential intervention plans for use by homeless shelters, who organize these interventions to raise awareness about HIV among homeless youth.

Learning Adversary Behavior in Security Games: A PAC Model Perspective

no code implementations30 Oct 2015 Arunesh Sinha, Debarun Kar, Milind Tambe

We provide four main contributions: (1) a PAC model of learning adversary response functions in SSGs; (2) PAC-model analysis of the learning of key, existing bounded rationality models in SSGs; (3) an entirely new approach to adversary modeling based on a non-parametric class of response functions with PAC-model analysis and (4) identification of conditions under which computing the best defender strategy against the learned adversary behavior is indeed the optimal strategy.

Security Games with Information Leakage: Modeling and Computation

no code implementations23 Apr 2015 Haifeng Xu, Albert X. Jiang, Arunesh Sinha, Zinovi Rabinovich, Shaddin Dughmi, Milind Tambe

Our experiments confirm the necessity of handling information leakage and the advantage of our algorithms.

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