Fwd: Reminder - Thesis Defense - January 17, 2025 - Arundhati Banerjee - Learning based approaches to practical challenges in multi-agent active search

[Jeff Schneider]

reminder: Please come and see Arundhati's thesis defense happening now



-------- Forwarded Message --------
Subject: 	Reminder - Thesis Defense - January 17, 2025 - Arundhati 
Banerjee - Learning based approaches to practical challenges in 
multi-agent active search
Date: 	Fri, 17 Jan 2025 12:23:22 -0500
From: 	Diane L Stidle <stidle at andrew.cmu.edu>
To: 	ml-seminar at cs.cmu.edu <ML-SEMINAR at CS.CMU.EDU>, yyue at caltech.edu



*/Thesis Defense/*

Date: January 17, 2025
Time: 1:00pm (EST)
Place: GHC 4405 & Remote
PhD Candidate: Arundhati Banerjee

*Title: Learning based approaches to practical challenges in multi-agent 
active search*

Abstract:
Interactive decision making is essential for the functioning of 
autonomous agents in both software and embodied applications. Typically, 
agents interact in a multi-agent environment with the goal of fulfilling 
individual or shared objectives. In this thesis, we study the 
multi-agent adaptive decision making problem in the framework of 
Multi-Agent Active Search (MAAS) with a focus on applications like 
search and rescue, wildlife patrolling or environment monitoring with 
multi-robot teams.

Multi-Agent active search involves a team of robots (agents) deciding 
/when /and /where/ to gather information about their surroundings, 
conditioned on their past observations, in order to estimate the 
presence and position of different objects of interest (OOIs) or 
targets. Agents communicate with each other asynchronously, without 
relying on a central controller to coordinate the agents' 
interactions. Realistically, inter-agent communications may be 
unreliable, and robots in the wild have to deal with noisy observations 
and stochastic environment dynamics. Our setup formalizes MAAS with 
practical models of real-world sensing, noise, and communication 
constraints for aerial and ground robots.

Part I of this thesis studies the benefits of non-myopic lookahead 
decision making in MAAS with Thompson sampling and Monte Carlo Tree 
Search. Additionally, we consider a multi-objective pareto-optimization 
setup for cost-aware active search, highlighting the challenges due to 
partial observability, decentralized multi-agent decision making, and 
computational complexity with combinatorial action search spaces. In 
Part II, we focus on the practical challenges due to observation noise 
and dynamic targets in multi-agent active search and tracking. Our 
proposed algorithms using Bayesian filtering in these settings 
empirically demonstrate the importance of uncertainty modeling for 
inference and decision making with noisy observations due to sensor 
errors or environment non-stationarity. Part III shifts focus to 
generative models for decision making, particularly the applicability of 
diffusion for lookahead MAAS with observation noise. In the final part, 
we discuss the broader applicability of these methods in the context of 
multi-agent decision making in robotics and other applications with 
similar real world constraints.

*Thesis Committee*:
Jeff Schneider (Chair)
Geoff Gordon
Barnabás Póczos
Yisong Yue (Caltech)

Link to the draft document:
https://drive.google.com/drive/folders/1YsZgROFeltk4TUVYo-9ldoSlsEkTjaAB?usp=sharing

Zoom meeting link:
https://cmu.zoom.us/j/2231085641?pwd=j7ZOUGOHWPnaCe6c2FuCW3Xb0q8cWb.1&omn=99132356799 
<https://cmu.zoom.us/j/2231085641?pwd=j7ZOUGOHWPnaCe6c2FuCW3Xb0q8cWb.1&omn=99132356799>
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