Author name cluster

Dylan M. Asmar

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3 papers

2 author rows

AAAI Conference 2026 Conference Paper

Efficient Multiagent Planning via Shared Action Suggestions

Dylan M. Asmar
Mykel J. Kochenderfer

Decentralized partially observable Markov decision processes with communication (Dec-POMDP-Com) provide a framework for multiagent decision making under uncertainty, but the NEXP-complete complexity for finite-horizon problems renders solutions intractable in general. While sharing actions and observations can reduce the complexity to PSPACE-complete, we propose an approach that bridges POMDPs and Dec-POMDPs by communicating only suggested joint actions, eliminating the need to share observations while retaining near-centralized performance. Our algorithm estimates joint beliefs using shared actions to prune infeasible beliefs. Each agent maintains possible belief sets for other agents, pruning them based on suggested actions to form an estimated joint belief usable with any centralized policy. This approach requires solving a POMDP for each agent, reducing computational complexity while preserving performance. We demonstrate its effectiveness on several Dec-POMDP benchmarks, showing performance comparable to centralized methods when shared actions enable effective belief pruning. This action-based communication framework offers a natural avenue for integrating human-agent cooperation, opening new directions for scalable multiagent planning under uncertainty, with applications in both autonomous systems and human-agent teams.

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ECAI Conference 2025 Conference Paper

LeRAAT: LLM-Enabled Real-Time Aviation Advisory Tool

Marc R. Schlichting
Vale Rasmussen
Heba Alazzeh
Houjun Liu
Kiana Jafari Meimandi
Amelia F. Hardy
Dylan M. Asmar
Mykel J. Kochenderfer

In aviation emergencies, high-stakes decisions must be made in an instant. Pilots rely on quick access to precise, context-specific information—an area where emerging tools like large language models (LLMs) show promise in providing critical support. To help research the effects of bringing AI into an aircraft cockpit, this paper introduces LeRAAT, a framework that integrates LLMs with the X-Plane flight simulator to deliver real-time, context-aware pilot assistance. The system uses live flight data, weather conditions, and aircraft documentation to generate recommendations aligned with aviation best practices and tailored to the particular situation. It employs a Retrieval-Augmented Generation (RAG) pipeline that extracts and synthesizes information from aircraft type-specific manuals, including performance specifications and emergency procedures, as well as aviation regulatory materials, such as FAA directives and standard operating procedures. We showcase the framework in both a virtual reality and traditional on-screen simulation. LeRAAT can support a wide range of future research applications such as pilot training, human factors, and operational decision support.

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ICRA Conference 2023 Conference Paper

Model Predictive Optimized Path Integral Strategies

Dylan M. Asmar
Ransalu Senanayake
Shawn Manuel
Mykel J. Kochenderfer

We generalize the derivation of model predictive path integral control (MPPI) to allow for a single joint distribution across controls in the control sequence. This reformation allows for the implementation of adaptive importance sampling (AIS) algorithms into the original importance sampling step while still maintaining the benefits of MPPI such as working with arbitrary system dynamics and cost functions. The benefit of optimizing the proposal distribution by integrating AIS at each control step is demonstrated in simulated environments including controlling multiple cars around a track. The new algorithm is more sample efficient than MPPI, achieving better performance with fewer samples. This performance disparity grows as the dimension of the action space increases. Results from simulations suggest the new algorithm can be used as an anytime algorithm, increasing the value of control at each iteration versus relying on a large set of samples. Repository—https://github.com/sisl/MPOPIS

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