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Likun Yang

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3 papers

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AAMAS Conference 2026 Conference Paper

Repeated Deceptive Path Planning against Learnable Observer

Shiyue Cao
Pei Xu
Likun Yang
Lei Cui
Shizhao Yu
Shiyu Zhang
Yongjian Ren
Xiaotang Chen

We introduce Repeated Deceptive Path Planning (RDPP), a novel settingwhereanagentmustconcealitsdestinationfromalearnable observer that can adapt from historical trajectories. We show that existing deceptive planning methods, designed for static observers, fail in RDPP due to accumulated adaptation lag. To address this, we propose Deceptive Meta Planning (DeMP), a two-level optimization framework that anticipates and counteracts observer updates across episodes via meta-level learning. Experiments demonstrate that DeMP significantly outperforms traditional methods, enabling sustained deception against learning adversaries while maintaining efficient path costs.

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AAMAS Conference 2026 Conference Paper

SOM: Structured Opponent Modeling for LLM-based Agents via Structural Causal Model

Shiyue Cao
Pei Xu
Likun Yang
Lei Cui
Xiaotang Chen
Kaiqi Huang

Accurately predicting opponents’ behavior from interactions is a fundamental capability for large language model (LLM)-based agents in multi-agent and game-theoretic environments. Existing approaches often entangle opponent modeling with prediction, relying on implicit contextual reasoning and limiting adaptability in dynamicinteractions. Tothisend, weproposeStructuredOpponent Modeling (SOM), a two-stage opponent modeling framework that distinctly decouples opponent model construction and opponent prediction. At the construction stage, SOM employs a Structural Causal Model (SCM), a graph-based formalism for representing dependencies among variables, to capture directed links between opponents’ observations and actions, yielding an explicit and structured opponent representation. At the prediction stage, the LLM performs structured reasoning along clear pathways derived from the SCM, improving both prediction accuracy and stability. Extensive experiments on diverse multi-agent benchmarks demonstrate that SOM consistently outperforms state-of-the-art LLM-based reasoning baselines, enabling more accurate and adaptable strategic decision-making in complex and dynamic multi-agent interactions.

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AAMAS Conference 2025 Conference Paper

Uncertainty-Aware Opponent Modeling for Deep Reinforcement Learning

Likun Yang
Pei Xu
Shiyue Cao
Yongjian Ren
Xiaotang Chen
Kaiqi Huang

The ability to model opponent behavior is essential for autonomous decision-making in multi-agent games. Although stochastic behavior is universal in real-world situations, previous works have struggled to model opponents with high stochasticity, such as humans. The issue arises because stochasticity in opponent behavior introduces significant uncertainty into the opponent modeling process, which existing methods have not adequately addressed. We introduce a novel Uncertainty-Aware Opponent Modeling (UAOM) method that addresses two key sources of uncertainty stemming from the inherent randomness of the opponent’s actions. The first pertains to the uncertainty in constructing the opponent model, while the second concerns the uncertainty in applying the model during decision-making. For the first uncertainty, UAOM uses a hybrid behavior modeling module to learn a more powerful opponentaware representation by ensembling the deterministic and probabilistic models to address both aleatoric and epistemic uncertainties in opponent modeling. For the second uncertainty, UAOM uses an opponent-aware dynamic modeling module to learn a dynamicaware representation. We further provide a theoretical analysis showing that jointly optimizing our two modules can enhance downstream reinforcement learning performance while ensuring system convergence. We evaluate UAOM in both simulated settings This work is licensed under a Creative Commons Attribution International 4. 0 License. Proc. of the 24th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2025), Y. Vorobeychik, S. Das, A. Nowé (eds.), May 19 – 23, 2025, Detroit, Michigan, USA. © 2025 International Foundation for Autonomous Agents and Multiagent Systems (www. ifaamas. org). and human-agent interaction scenarios. Our experimental results show that the proposed method significantly enhances performance when facing opponents with varying degrees of stochastic behavior, while efficiently managing the uncertainties introduced by such opponents.

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