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Swaroop Vattam

I am a technical staff member at Lincoln Lab at MIT. Previously, I was a research scientist at Georgia Tech and a NAS fellow at NRL. I got my PhD in Computer Science from Georgia Tech in 2012. My research is broadly focused on applications of machine reasoning and machine learning to natural language processing problems. I am currently investigating data-driven model discovery systems in an effort to tackle the problem of automated machine learning.



cs.AI updates on arXiv.org: Computer Science -- Artificial Intelligence (cs.AI) updates on the arXiv.org e-print archive
Multi-Agent Pathfinding (MAPF) with Continuous Time. (arXiv:1901.05506v1 [cs.AI]):

MAPF is the problem of finding paths for multiple agents such that every agent reaches its goal and the agents do not collide. Most prior work on MAPF were on grid, assumed all actions cost the same, agents do not have a volume, and considered discrete time steps. In this work we propose a MAPF algorithm that do not assume any of these assumptions, is complete, and provides provably optimal solutions. This algorithm is based on a novel combination of SIPP, a continuous time single agent planning algorithms, and CBS, a state of the art multi-agent pathfinding algorithm. We analyze this algorithm, discuss its pros and cons, and evaluate it experimentally on several standard benchmarks.

Survey of Bayesian Networks Applications on Unmanned Intelligent Autonomous Vehicles. (arXiv:1901.05517v1 [cs.AI]):

This article review the applications of Bayesian networks on Unmanned Intelligent Autonomous Vehicles (UIAV) from the decision making point of view, which represents the final step for fully autonomous unmanned vehicles (currently under discussion). Until now when it comes to make high level decisions for unmanned autonomous vehicles (UAV) the humans have the last word. Based on the works exposed in this article and current analysis, the modules of a general decision making framework and its variables are inferred. Many efforts have been made in the labs showing Bayesian networks as a promising computer model for decision making. Remains for the future to test Bayesian networks models in real situations. Besides the applications, Bayesian networks fundaments are introduced as elements to consider when we try to develop (UIAVs) with the potential of achieving high level judgements.

Distance-Guided GA-Based Approach to Distributed Data-Intensive Web Service Composition. (arXiv:1901.05564v1 [cs.AI]):

Distributed computing which uses Web services as fundamental elements, enables high-speed development of software applications through composing many interoperating, distributed, re-usable, and autonomous services. As a fundamental challenge for service developers, service composition must fulfil functional requirements and optimise Quality of Service (QoS) attributes, simultaneously. On the other hand, huge amounts of data have been created by advances in technologies, which may be exchanged between services. Data-intensive Web services are of great interest to implement data-intensive processes. However, current approaches to Web service composition have omitted either the effect of data, or the distribution of services. Evolutionary Computing (EC) techniques allow for the creation of compositions that meet all the above factors. In this paper, we will develop Genetic Algorithm (GA)-based approach for solving the problem of distributed data-intensive Web service composition (DWSC). In particular, we will introduce two new heuristics, i.e. Longest Common Subsequence(LCS) distance of services, in designing crossover operators. Additionally, a new local search technique incorporating distance of services will be proposed.

Generating Realistic Sequences of Customer-level Transactions for Retail Datasets. (arXiv:1901.05577v1 [cs.LG]):

In order to better engage with customers, retailers rely on extensive customer and product databases which allows them to better understand customer behaviour and purchasing patterns. This has long been a challenging task as customer modelling is a multi-faceted, noisy and time-dependent problem. The most common way to tackle this problem is indirectly through task-specific supervised learning prediction problems, with relatively little literature on modelling a customer by directly simulating their future transactions. In this paper we propose a method for generating realistic sequences of baskets that a given customer is likely to purchase over a period of time. Customer embedding representations are learned using a Recurrent Neural Network (RNN) which takes into account the entire sequence of transaction data. Given the customer state at a specific point in time, a Generative Adversarial Network (GAN) is trained to generate a plausible basket of products for the following week. The newly generated basket is then fed back into the RNN to update the customer's state. The GAN is thus used in tandem with the RNN module in a pipeline alternating between basket generation and customer state updating steps. This allows for sampling over a distribution of a customer's future sequence of baskets, which then can be used to gain insight into how to service the customer more effectively. The methodology is empirically shown to produce baskets that appear similar to real baskets and enjoy many common properties, including frequencies of different product types, brands, and prices. Furthermore, the generated data is able to replicate most of the strongest sequential patterns that exist between product types in the real data.

Interactive Plan Explicability in Human-Robot Teaming. (arXiv:1901.05642v1 [cs.RO]):

Human-robot teaming is one of the most important applications of artificial intelligence in the fast-growing field of robotics. For effective teaming, a robot must not only maintain a behavioral model of its human teammates to project the team status, but also be aware that its human teammates' expectation of itself. Being aware of the human teammates' expectation leads to robot behaviors that better align with human expectation, thus facilitating more efficient and potentially safer teams. Our work addresses the problem of human-robot cooperation with the consideration of such teammate models in sequential domains by leveraging the concept of plan explicability. In plan explicability, however, the human is considered solely as an observer. In this paper, we extend plan explicability to consider interactive settings where human and robot behaviors can influence each other. We term this new measure as Interactive Plan Explicability. We compare the joint plan generated with the consideration of this measure using the fast forward planner (FF) with the plan created by FF without such consideration, as well as the plan created with actual human subjects. Results indicate that the explicability score of plans generated by our algorithm is comparable to the human plan, and better than the plan created by FF without considering the measure, implying that the plans created by our algorithms align better with expected joint plans of the human during execution. This can lead to more efficient collaboration in practice.



Mathematical Moments from the American Mathematical Society: The American Mathematical Societys Mathematical Moments program promotes appreciation and understanding of the role mathematics plays in science, nature, technology, and human culture. Listen to researchers talk about how they use math: from presenting realistic animation to beating cancer.
Screening for Autism: Researcher: Jordan Hashemi, Duke University Moment: http://www.ams.org/samplings/mathmoments/mm142-autism.pdf Moment Title: Screening for Autism Description: Jordan Hashemi talks about an easy-to-use app to screen for autism. Podcast page: http://www.ams.org/samplings/mathmoments/mm142-autism-podcast Audio file: podcast-mom-autism.mp3
Unbunching Buses: Researchers: Vikash V. Gayah and S. Ilgin Guler, Pennsylvania State University Moment: http://www.ams.org/samplings/mathmoments/mm141-bus-bunching.pdf Moment Title: Unbunching Buses Description: Gayah and Guler talk about mitigating the clustering of buses on a route. Podcast page: http://www.ams.org/samplings/mathmoments/mm141-bus-bunching-podcast Audio file: podcast-mom-bus-bunching.mp3
Winning the Race: Researcher: Christine Darden, NASA (retired) Moment: http://www.ams.org/publicoutreach/mathmoments/mm140-hidden-figures.pdf Moment Title: Winning the Race Description: Christine Darden on working at NASA. Podcast page: http://www.ams.org/publicoutreach/mathmoments/mm140-hidden-figures-podcast
Revolutionizing and Industry: Researchers: Christopher Brinton, Zoomi, Inc. and Princeton University, and Mung Chiang, Purdue University Moment: http://www.ams.org/samplings/mathmoments/mm139-netflix.pdf Description: Christopher Brinton and Mung Chiang talk about the Netflix Prize competition.
Going Into a Shell: Researcher: Derek Moulton, University of Oxford Moment: http://www.ams.org/samplings/mathmoments/mm138-shells.pdf Description: Derek Moulton explains the math behind the shapes of seashells.


AMS Feature Column: AMS Feature Column - RSS Feed
Branko Grunbaum Remembered--A Great Geometer!:
Upgrading Slums Using Topology:
Topology and Elementary Electric Circuit Theory, I:
Recognition:
Getting in Sync:
Reading the Bakhshali Manuscript:
Crochet Topology:
Mathematical Economics for Mathematics and Statistics Awareness Month:
Neural Nets and How They Learn:
Jakob Bernoulli's Zoo:






Last modified 18 November 2018 at 5:47 pm by svattam