Towards Addressing Model Uncertainty: Robust Execution-time Coordination for Teamwork (Short Paper)

Robust Execution-time Coordination in DEC-POMDPs Under Model Uncertainty

Despite their worst-case NEXP-complete planning complexity, DEC-POMDPs remain a popular framework for multiagent teamwork. This paper introduces effective teamwork under model uncertainty (i.e., potentially inaccurate transition and observation functions) as a novel challenge for DEC-POMDPs and presents MODERN, the first execution-centric framework for DEC-POMDPs explicitly motivated by address...

Teamwork in distributed POMDPs: execution-time coordination under model uncertainty

Unleashing Dec-MDPs in Security Games: Enabling Effective Defender Teamwork

Multiagent teamwork and defender-attacker security games are two areas that are currently receiving significant attention within multiagent systems research. Unfortunately, despite the need for effective teamwork among multiple defenders, little has been done to harness the teamwork research in security games. This paper is the first to remedy this situation by integrating the powerful teamwork...

Developing Robust Project Scheduling Methods for Uncertain Parameters

A common problem arising in project management is the fact that the baseline schedule is often disrupted during the project execution because of uncertain parameters. As a result, project managers are often unable to meet the deadline time of the milestones. Robust project scheduling is an effective approach in case of uncertainty. Upon adopting this approach, schedules are protected against po...

Teamwork and Coordination under Model Uncertainty in DEC-POMDPs

Distributed Partially Observable Markov Decision Processes (DEC-POMDPs) are a popular planning framework for multiagent teamwork to compute (near-)optimal plans. However, these methods assume a complete and correct world model, which is often violated in real-world domains. We provide a new algorithm for DEC-POMDPs that is more robust to model uncertainty, with a focus on domains with sparse ag...