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Bhaskar Krishnamachari

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8 papers

2 author rows

AAMAS Conference 2022 Conference Paper

Intelligent Communication over Realistic Wireless Networks in Multi-Agent Cooperative Games

Diyi Hu
Chi Zhang
Viktor Prasanna
Bhaskar Krishnamachari

In MARL, communication among agents is essential to establish cooperation. Over the realistic wireless network, many factors can affect transmission reliability, especially considering that the wireless network condition varies with agents’ mobility. We propose a framework that improves the intelligence of communication over realistic wireless networks in two fundamental aspects: (1) When: Agents learn the timing of communication based on message importance and wireless channel condition. We further propose a communication lagging technique to make the training end-to-end differentiable. (2) What: Agents augment message contents with wireless network measurements. The messages improve both the game and communication actions of the agents. Experiments on a standard environment show that compared with state-of-the-art, our framework enables more intelligent collaboration and thus achieves significantly better game performance, convergence speed and communication efficiency.

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AAAI Conference 2022 Short Paper

Using Reinforcement Learning for Operating Educational Campuses Safely during a Pandemic (Student Abstract)

Elizabeth Akinyi Ondula
Bhaskar Krishnamachari

The COVID-19 pandemic has brought a significant disruption not only on how schools operate but also affected student sentiments on learning and adoption to different learning strategies. We propose CampusPandemicPlanR, a reinforcement learning-based simulation tool that could be applied to suggest to campus operators how many students from each course to allow on a campus classroom each week. The tool aims to strike a balance between the conflicting goals of keeping students from getting infected, on one hand, and allowing more students to come into campus to allow them to benefit from in-person classes, on the other. Our preliminary results show that a Q-learning agent is able to learn better policies over iterations, and that different Pareto-optimal trade-offs between these conflicting goals could be obtained by varying the reward weight parameter.

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IROS Conference 2018 Conference Paper

Intelligent Robotic IoT System (IRIS)Testbed

Jason A. Tran
Pradipta Ghosh
Yutong Gu
Richard Kim
Daniel D'Souza
Nora Ayanian
Bhaskar Krishnamachari

We present the Intelligent Robotic IoT System (IRIS), a modular, portable, scalable, and open-source testbed for robotic wireless network research. There are two key features that separate IRIS from most of the state-of-the-art multi-robot testbeds. (1)Portability: IRIS does not require a costly static global positioning system such as a VICON system nor time-intensive vision-based SLAM for its operation. Designed with an inexpensive Time Difference of Arrival (TDoA)localization system with centimeter level accuracy, the IRIS testbed can be deployed in an arbitrary uncontrolled environment in a matter of minutes. (2)Programmable Wireless Communication Stack: IRIS comes with a modular programmable low-power IEEE 802. 15. 4 radio and IPv6 network stack on each node. For the ease of administrative control and communication, we also developed a lightweight publish-subscribe overlay protocol called ROMANO that is used for bootstrapping the robots (also referred to as the IRISbots), collecting statistics, and direct control of individual robots, if needed. We detail the modular architecture of the IRIS testbed design along with the system implementation details and localization performance statistics.

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AAMAS Conference 2016 Conference Paper

Restless Poachers: Handling Exploration-Exploitation Tradeoffs in Security Domains

Yundi Qian
Chao Zhang
Bhaskar Krishnamachari
Milind Tambe

The success of Stackelberg Security Games (SSGs) in counterterrorism domains has inspired researchers’ interest in applying game-theoretic models to other security domains with frequent interactions between defenders and attackers, e. g. , wildlife protection. Previous research optimizes defenders’ strategies by modeling this problem as a repeated Stackelberg game, capturing the special property in this domain — frequent interactions between defenders and attackers. However, this research fails to handle exploration-exploitation tradeoff in this domain caused by the fact that defenders only have knowledge of attack activities at targets they protect. This paper addresses this shortcoming and provides the following contributions: (i) We formulate the problem as a restless multi-armed bandit (RMAB) model to address this challenge. (ii) To use Whittle index policy to plan for patrol strategies in the RMAB, we provide two sufficient conditions for indexability and an algorithm to numerically evaluate indexability. (iii) Given indexability, we propose a binary search based algorithm to find Whittle index policy efficiently.

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IROS Conference 2015 Conference Paper

The optimism principle: A unified framework for optimal robotic network deployment in an unknown obstructed environment

Shangxing Wang
Bhaskar Krishnamachari
Nora Ayanian

We consider the problem of deploying a team of robots in an unknown, obstructed environment to form a multi-hop communication network. As a solution, we present a unified framework, onLinE rObotic Network formAtion (LEONA), that is general enough to permit optimizing the communication network for different utility functions in non-convex environments. LEONA adopts the principle of “optimism in the face of uncertainty” to allow the team of robots to form optimal network configurations efficiently and rapidly without having to map link qualities in the entire area. We demonstrate and evaluate this framework on two specific scenarios concerning the formation of a multi-hop communication path between fixed end-points: one minimizing the total path cost, and another maximizing the bottleneck communication rate. Our simulation-based evaluation shows that the use of the optimism principle can significantly reduce resources spent in exploring and mapping the entire region prior to network optimization. We also present a mathematical modeling of how the searched area scales with various relevant parameters in each case.

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IROS Conference 2014 Conference Paper

Route swarm: Wireless network optimization through mobility

Ryan K. Williams
Andrea Gasparri
Bhaskar Krishnamachari

In this paper, we demonstrate a novel hybrid architecture for coordinating networked robots in sensing and information routing applications. The proposed INformation and Sensing driven PhysIcally REconfigurable robotic network (INSPIRE), consists of a Physical Control Plane (PCP) which commands agent position, and an Information Control Plane (ICP) which regulates information flow towards communication/sensing objectives. We describe an instantiation where a mobile robotic network is dynamically reconfigured to ensure high quality routes between static wireless nodes, which act as source/destination pairs for information flow. We demonstrate our propositions through simulation under a realistic wireless network regime.

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AIJ Journal 2005 Journal Article

Sensor networks and distributed CSP: communication, computation and complexity

Ramón Béjar
Carmel Domshlak
Cèsar Fernández
Carla Gomes
Bhaskar Krishnamachari
Bart Selman
Magda Valls

We introduce SensorDCSP, a naturally distributed benchmark based on a real-world application that arises in the context of networked distributed systems. In order to study the performance of Distributed CSP (DisCSP) algorithms in a truly distributed setting, we use a discrete-event network simulator, which allows us to model the impact of different network traffic conditions on the performance of the algorithms. We consider two complete DisCSP algorithms: asynchronous backtracking (ABT) and asynchronous weak commitment search (AWC), and perform performance comparison for these algorithms on both satisfiable and unsatisfiable instances of SensorDCSP. We found that random delays (due to network traffic or in some cases actively introduced by the agents) combined with a dynamic decentralized restart strategy can improve the performance of DisCSP algorithms. In addition, we introduce GSensorDCSP, a plain-embedded version of SensorDCSP that is closely related to various real-life dynamic tracking systems. We perform both analytical and empirical study of this benchmark domain. In particular, this benchmark allows us to study the attractiveness of solution repairing for solving a sequence of DisCSPs that represent the dynamic tracking of a set of moving objects.

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STOC Conference 2004 Conference Paper

Sharp thresholds For monotone properties in random geometric graphs

Ashish Goel
Sanatan Rai
Bhaskar Krishnamachari

Random geometric graphs result from taking n uniformly distributed points in the unit cube, [0,1] d , and connecting two points if their Euclidean distance is at most r, for some prescribed r. We show that monotone properties for this class of graphs have sharp thresholds by reducing the problem to bounding the bottleneck matching on two sets of $n$ points distributed uniformly in [0,1] d . We present upper bounds on the threshold width, and show that our bound is sharp for d = 1 and at most a sublogarithmic factor away for d ≥ 2. Interestingly, the threshold width is much sharper for random geometric graphs than for Bernoulli random graphs. Further, a random geometric graph is shown to be a subgraph, with high probability, of another independently drawn random geometric graph with a slightly larger radius; this property is shown to have no analogue for Bernoulli random graphs.

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