147 citations · 309 across the 12 of their papers we have counts for
17 papers
Explainable Composition of Aggregated Assistants
Sarath Sreedharan, Tathagata Chakraborti, Yara Rizk +1
A new design of an AI assistant that has become increasingly popular is that of an "aggregated assistant" -- realized as an orchestrated composition of several individual skills or…
Online Semi-Supervised Learning with Bandit Feedback
Sohini Upadhyay, Mikhail Yurochkin, Mayank Agarwal +2
We formulate a new problem at the intersectionof semi-supervised learning and contextual bandits,motivated by several applications including clini-cal trials and ad recommendations…
Double-Linear Thompson Sampling for Context-Attentive Bandits
Djallel Bouneffouf, Raphaël Féraud, Sohini Upadhyay +2
In this paper, we analyze and extend an online learning framework known as Context-Attentive Bandit, motivated by various practical applications, from medical diagnosis to dialog s…
From Robotic Process Automation to Intelligent Process Automation: Emerging Trends
Tathagata Chakraborti, Vatche Isahagian, Rania Khalaf +4
In this survey, we study how recent advances in machine intelligence are disrupting the world of business processes. Over the last decade, there has been steady progress towards th…
A Conversational Digital Assistant for Intelligent Process Automation
Yara Rizk, Vatche Isahagian, Scott Boag +4
Robotic process automation (RPA) has emerged as the leading approach to automate tasks in business processes. Moving away from back-end automation, RPA automated the mouse-click on…
Contextual Bandit with Missing Rewards
Djallel Bouneffouf, Sohini Upadhyay, Yasaman Khazaeni
We consider a novel variant of the contextual bandit problem (i.e., the multi-armed bandit with side-information, or context, available to a decision-maker) where the reward associ…
Explainability of Intelligent Transportation Systems using Knowledge Compilation: a Traffic Light Controller Case
Salomón Wollenstein-Betech, Christian Muise, Christos G. Cassandras +2
Usage of automated controllers which make decisions on an environment are widespread and are often based on black-box models. We use Knowledge Compilation theory to bring explainab…
A Snooze-less User-Aware Notification System for Proactive Conversational Agents
Yara Rizk, Vatche Isahagian, Merve Unuvar +1
The ubiquity of smart phones and electronic devices has placed a wealth of information at the fingertips of consumers as well as creators of digital content. This has led to millio…
Federated Learning with Matched Averaging
Hongyi Wang, Mikhail Yurochkin, Yuekai Sun +2
Federated learning allows edge devices to collaboratively learn a shared model while keeping the training data on device, decoupling the ability to do model training from the need…
D3BA: A Tool for Optimizing Business Processes Using Non-Deterministic Planning
Tathagata Chakraborti, Yasaman Khazaeni
This paper builds upon recent work in the declarative design of dialogue agents and proposes an exciting new tool -- D3BA -- Declarative Design for Digital Business Automation, bui…
A Unified Conversational Assistant Framework for Business Process Automation
Yara Rizk, Abhishek Bhandwalder, Scott Boag +5
Business process automation is a booming multi-billion-dollar industry that promises to remove menial tasks from workers' plates -- through the introduction of autonomous agents --…
A Bandit Approach to Posterior Dialog Orchestration Under a Budget
Sohini Upadhyay, Mayank Agarwal, Djallel Bounneffouf +1
Building multi-domain AI agents is a challenging task and an open problem in the area of AI. Within the domain of dialog, the ability to orchestrate multiple independently trained…
Bayesian Nonparametric Federated Learning of Neural Networks
Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh +3
In federated learning problems, data is scattered across different servers and exchanging or pooling it is often impractical or prohibited. We develop a Bayesian nonparametric fram…
Event-driven Trajectory Optimization for Data Harvesting in Multi-Agent Systems
Yasaman Khazaeni, Christos G. Cassandras
We propose a new event-driven method for on-line trajectory optimization to solve the data harvesting problem: in a two-dimensional mission space, N mobile agents are tasked with t…
Event excitation for event-driven control and optimization of multi-agent systems
Yasaman Khazaeni, Christos G. Cassandras
We consider event-driven methods in a general framework for the control and optimization of multi-agent systems, viewing them as stochastic hybrid systems. Such systems often have…
An Optimal Control Approach for the Data Harvesting Problem
Yasaman Khazaeni, Christos G. Cassandras
We propose a new method for trajectory planning to solve the data harvesting problem. In a two-dimensional mission space, mobile agents are tasked with the collection of data g…
A New Event-Driven Cooperative Receding Horizon Controller for Multi-agent Systems in Uncertain Environments
Yasaman Khazaeni, Christos G. Cassandras
In previous work, a Cooperative Receding Horizon (CRH) controller was developed for solving cooperative multi-agent problems in uncertain environments. In this paper, we overcome s…