activity
20112023
most citedMonte Carlo Sampling Methods for Approximating Interactive POMDPs

74 citations · 279 across the 24 of their papers we have counts for

collaborators
Showing cs.MAShow all

5 papers · 1 filter

cs.MA2022

SIPOMDPLite-Net: Lightweight, Self-Interested Learning and Planning in POSGs with Sparse Interactions

Gengyu Zhang, Prashant Doshi

This work introduces sIPOMDPLite-net, a deep neural network (DNN) architecture for decentralized, self-interested agent control in partially observable stochastic games (POSGs) wit…

cs.MA2020★ 2 cited

Active Deception using Factored Interactive POMDPs to Recognize Cyber Attacker's Intent

Aditya Shinde, Prashant Doshi, Omid Setayeshfar

This paper presents an intelligent and adaptive agent that employs deception to recognize a cyber adversary's intent. Unlike previous approaches to cyber deception, which mainly fo…

cs.MA2019

Scalable Decision-Theoretic Planning in Open and Typed Multiagent Systems

Adam Eck, Maulik Shah, Prashant Doshi +1

In open agent systems, the set of agents that are cooperating or competing changes over time and in ways that are nontrivial to predict. For example, if collaborative robots were t…

cs.MA2015

Individual Planning in Agent Populations: Exploiting Anonymity and Frame-Action Hypergraphs

Ekhlas Sonu, Yingke Chen, Prashant Doshi

Interactive partially observable Markov decision processes (I-POMDP) provide a formal framework for planning for a self-interested agent in multiagent settings. An agent operating…

cs.MA2014★ 2 cited

Team Behavior in Interactive Dynamic Influence Diagrams with Applications to Ad Hoc Teams

Muthukumaran Chandrasekaran, Prashant Doshi, Yifeng Zeng +1

Planning for ad hoc teamwork is challenging because it involves agents collaborating without any prior coordination or communication. The focus is on principled methods for a singl…