Autonomous robots operating in uncertain or hazardous environments subject to state safety
constraints must be able to identify and isolate faulty components in a time-optimal manner …

Optimal plans in Constrained Partially Observable Markov Decision Processes (CPOMDPs)
maximize reward objectives while satisfying hard cost constraints, generalizing safe …

To plan safely in uncertain environments, agents must balance utility with safety constraints.
Safe planning problems can be modeled as a chance-constrained partially observable …

Many sequential decision problems involve optimizing one objective function while
imposing constraints on other objectives. Constrained Partially Observable Markov Decision …

Controlling the cyborg rat with the guidance of an unmanned aerial vehicle (UAV) is
important yet challenging. UAV can provide a mobile bird's eye view (BEV) with multiple …

We study the problem of decentralized constrained POMDPs in a team-setting where the
multiple non-strategic agents have asymmetric information. Strong duality is established for …

Abstract Lagrangian-guided Monte Carlo tree search with global dual ascent has been
applied to solve large constrained partially observable Markov decision processes …

The work studies the problem of decentralized constrained POMDPs in a team-setting where
multiple nonstrategic agents have asymmetric information. Using an extension of Sion's …

Taking into account future risk is essential for an autonomously operating robot to find online
not only the best but also a safe action to execute. In this paper, we build upon the recently …

The work studies cooperative decentralized constrained POMDPs with asymmetric
information. Using an extension of Sion's Minimax theorem for functions with positive infinity …