Alex Schutz — accepted AI-conference papers

2026

Scalable Solution Methods for Dec-POMDPs with Deterministic Dynamics

Yang You, Alex Schutz, Zhikun Li, Bruno Lacerda, Robert Skilton, Nick Hawes

AAAI 2026technical

Many high-level multi-agent planning problems, such as multi-robot navigation and path planning, can be modeled with deterministic actions and observations. In this work, we focus on such domains and introduce the class of Deterministic Decentralized POMDPs (Det-Dec-POMDPs)—a subclass of Dec-POMDPs

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2025

A Finite-State Controller Based Offline Solver for Deterministic POMDPs

Alex Schutz, Yang You, Matías Mattamala, Ipek Caliskanelli, Bruno Lacerda, Nick Hawes

IJCAI 2025

Deterministic partially observable Markov decision processes (DetPOMDPs) often arise in planning problems where the agent is uncertain about its environmental state but can act and observe deterministically. In this paper, we propose DetMCVI, an adaptation of the Monte Carlo Value Iteration (MCVI) a

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