Author name cluster

Vincent Hsiao

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

1 author row

AAMAS Conference 2026 Conference Paper

Procedural Knowledge Improves Agentic LLM Workflows

Vincent Hsiao
Mark Roberts
Leslie Smith

Large language models (LLMs) often struggle when performing agentictaskswithoutsubstantialtoolsupport, prom-ptengineering, or fine tuning. Despite research showing that domain-dependent, proceduralknowledgecandramaticallyincreaseplanningefficiency, little work evaluates its potential for improving LLM performance on agentic tasks that may require implicit planning. We formalize, implement, and evaluate an agentic LLM workflow that leverages procedural knowledge in the form of a hierarchical task network (HTN). Empirical results of our implementation show that handcodedHTNscandramaticallyimproveLLMperformanceonagentic tasks, and using HTNs can boost a 20b or 70b parameter LLM to outperform a much larger 120b parameter LLM baseline. Furthermore, LLM-created HTNs improve overall performance, though less so. The results suggest that leveraging expertise—from humans, documents, or LLMs—to curate procedural knowledge will become another important tool for improving LLM workflows.

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AAMAS Conference 2025 Conference Paper

Automating Curriculum Learning for Reinforcement Learning using a Skill-Based Bayesian Network

Vincent Hsiao
Mark Roberts
Laura M. Hiatt
George Konidaris
Dana S. Nau

A major challenge for reinforcement learning is automatically generating curricula to reduce training time or improve performance in some target task. We introduce SEBNs (Skill-Environment Bayesian Networks) which model a probabilistic relationship between a set of skills, a set of goals that relate to the reward structure, and a set of environment features to predict policy performance on (possibly unseen) tasks. We develop an algorithm that uses the inferred estimates of agent success from an SEBN to weigh the possible next tasks by expected improvement. We evaluate the benefit of the resulting curriculum on three environments: a discrete gridworld, continuous control, and simulated robotics. The results show that SEBN-based curricula frequently outperform other baselines.

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AAMAS Conference 2022 Conference Paper

A Mean Field Game Model of Spatial Evolutionary Games

Vincent Hsiao
Dana Nau

Evolutionary Game Theory (EGT) studies evolving populations of agents that interact through normal form games whose outcome determines each individual’s evolutionary fitness. In many applications, EGT models are extended to include spatial effects in which the agents are located in a structured population such as a graph. We propose a Mean Field Game (MFG) generalization, denoted Pair-MFG, of the spatial evolutionary game model such that the behavior of a given spatial evolutionary game (or more specifically the behavior of its pair approximation) is a special case trajectory of the corresponding MFG. The proposed Pair-MFG model also allows for the formulation of the spatial evolutionary game as a control problem, opening up additional avenues of research into controlling the outcomes of these games. The state evolution equations of the proposed model are highly nonlinear and none of the equations in the system are necessarily convex. This necessitates different numerical methods as compared to those for traditional Linear Quadratic Gaussian MFGs. We provide a method for solving this new Pair-MFG model using fixed point iteration with time-dependent proximal terms and show empirically that this method is capable of finding a solution to a selection of EGT games.

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AAMAS Conference 2021 Conference Paper

Approximating Spatial Evolutionary Games using Bayesian Networks

Vincent Hsiao
Xinyue Pan
Dana Nau
Rina Dechter

Evolutionary Game Theory is an application of game theory to evolving populations of organisms. Of recent interest are EGT models situated on structured populations or spatial evolutionary games. Due to the complexity added by introducing a population structure, model analysis is usually performed through agent-based Monte-Carlo simulations. However, it can be difficult to obtain desired quantities of interest from these simulations due to stochastic effects. We define a framework for modeling spatial evolutionary games using Dynamic Bayesian Networks that capture the underlying stochastic process. The resulting Dynamic Bayesian Networks can be queried for quantities of interest by performing exact inference on the network. We then propose a method for producing approximations of the spatial evolutionary game through the truncation of the corresponding DBN, taking advantage of the high symmetry of the model. This method generalizes mean-field and pair approximations in the literature for spatial evolutionary games. Furthermore, we show empirical results demonstrating the capability of the method to obtain much better accuracy than pair approximation with respect to stochastic simulations.

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