most citedDynamic Arrival Rate Estimation for Campus Mobility on Demand Network Graphs

12 citations · 19 across the 6 of their papers we have counts for

collaborators

6 papers

cs.AI2017

Near-Optimal Adversarial Policy Switching for Decentralized Asynchronous Multi-Agent Systems

Trong Nghia Hoang, Yuchen Xiao, Kavinayan Sivakumar +2

A key challenge in multi-robot and multi-agent systems is generating solutions that are robust to other self-interested or even adversarial parties who actively try to prevent the…

cs.MA20174 cited

Learning for Multi-robot Cooperation in Partially Observable Stochastic Environments with Macro-actions

Miao Liu, Kavinayan Sivakumar, Shayegan Omidshafiei +2

This paper presents a data-driven approach for multi-robot coordination in partially-observable domains based on Decentralized Partially Observable Markov Decision Processes (Dec-P…

stat.ML2017

Dynamic Clustering Algorithms via Small-Variance Analysis of Markov Chain Mixture Models

Trevor Campbell, Brian Kulis, Jonathan How

Bayesian nonparametrics are a class of probabilistic models in which the model size is inferred from data. A recently developed methodology in this field is small-variance asymptot…

cs.MA20171 cited

Scalable Accelerated Decentralized Multi-Robot Policy Search in Continuous Observation Spaces

Shayegan Omidshafiei, Christopher Amato, Miao Liu +3

This paper presents the first ever approach for solving \emph{continuous-observation} Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs) and their semi-Marko…

cs.MA20172 cited

Semantic-level Decentralized Multi-Robot Decision-Making using Probabilistic Macro-Observations

Shayegan Omidshafiei, Shih-Yuan Liu, Michael Everett +5

Robust environment perception is essential for decision-making on robots operating in complex domains. Intelligent task execution requires principled treatment of uncertainty sourc…

cs.RO201712 cited

Dynamic Arrival Rate Estimation for Campus Mobility on Demand Network Graphs

Justin Miller, Andres Hasfura, Shih-Yuan Liu +1

Mobility On Demand (MOD) systems are revolutionizing transportation in urban settings by improving vehicle utilization and reducing parking congestion. A key factor in the success…