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Guanghui Yu

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4 papers

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TMLR Journal 2025 Journal Article

Goal Recognition Design for General Behavioral Agents using Machine Learning

Robert Kasumba
Guanghui Yu
Chien-Ju Ho
Sarah Keren
William Yeoh

Goal recognition design (GRD) aims to make limited modifications to decision-making environments to make it easier to infer the goals of agents acting within those environments. Although various research efforts have been made in goal recognition design, existing approaches are computationally demanding and often assume that agents are (near-)optimal in their decision-making. To address these limitations, we leverage machine learning methods for goal recognition design that can both improve run-time efficiency and account for agents with general behavioral models. Following existing literature, we use worst-case distinctiveness (wcd) as a measure of the difficulty in inferring the goal of an agent in a decision-making environment. Our approach begins by training a machine learning model to predict the wcd for a given environment and the agent behavior model. We then propose a gradient-based optimization framework that accommodates various constraints to optimize decision-making environments for enhanced goal recognition. Through extensive simulations, we demonstrate that our approach outperforms existing methods in reducing wcd and enhances runtime efficiency. Moreover, our approach also adapts to settings in which existing approaches do not apply, such as those involving flexible budget constraints, more complex environments, and suboptimal agent behavior. Finally, we conducted human-subject experiments that demonstrate that our method creates environments that facilitate efficient goal recognition from human decision-makers.

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NeurIPS Conference 2023 Conference Paper

Encoding Human Behavior in Information Design through Deep Learning

Guanghui Yu
Wei Tang
Saumik Narayanan
Chien-Ju Ho

We initiate the study of $\textit{behavioral information design}$ through deep learning. In information design, a $\textit{sender}$ aims to persuade a $\textit{receiver}$ to take certain actions by strategically revealing information. We address scenarios in which the receiver might exhibit different behavior patterns other than the standard Bayesian rational assumption. We propose HAIDNet, a neural-network-based optimization framework for information design that can adapt to multiple representations of human behavior. Through extensive simulation, we show that HAIDNet can not only recover information policies that are near-optimal compared with known analytical solutions, but also can extend to designing information policies for settings that are computationally challenging (e. g. , when there are multiple receivers) or for settings where there are no known solutions in general (e. g. , when the receiver behavior does not follow the Bayesian rational assumption). We also conduct real-world human-subject experiments and demonstrate that our framework can capture human behavior from data and lead to more effective information policy for real-world human receivers.

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IJCAI Conference 2022 Conference Paper

Environment Design for Biased Decision Makers

Guanghui Yu
Chien-Ju Ho

We study the environment design problem for biased decision makers. In an environment design problem, an informed principal aims to update the decision making environment to influence the decisions made by the agent. This problem is ubiquitous in various domains, e. g. , a social networking platform might want to update its website to encourage more user engagement. In this work, we focus on the scenario in which the agent might exhibit biases in decision making. We relax the common assumption that the agent is rational and aim to incorporate models of biased agents in environment design. We formulate the environment design problem under the Markov decision process (MDP) and incorporate common models of biased agents through introducing general time-discounting functions. We then formalize the environment design problem as constrained optimization problems and propose corresponding algorithms. We conduct both simulations and real human-subject experiments with workers recruited from Amazon Mechanical Turk to evaluate our proposed algorithms.

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IJCAI Conference 2018 Conference Paper

Improving Implicit Recommender Systems with View Data

Jingtao Ding
Guanghui Yu
Xiangnan He
Yuhan Quan
Yong Li
Tat-Seng Chua
Depeng Jin
Jiajie Yu

Most existing recommender systems leverage the primary feedback data only, such as the purchase records in E-commerce. In this work, we additionally integrate view data into implicit feedback based recommender systems (dubbed as Implicit Recommender Systems). We propose to model the pairwise ranking relations among purchased, viewed, and non-viewed interactions, being more effective and ﬂexible than typical pointwise matrix factorization (MF) methods. However, such a pairwise formulation poses efﬁciency challenges in learning the model. To address this problem, we design a new learning algorithm based on the element-wise Alternating Least Squares (eALS) learner. Notably, our algorithm can efﬁciently learn model parameters from the whole user-item matrix (including all missing data), with a rather low time complexity that is dependent on the observed data only. Extensive experiments on two real-world datasets demonstrate that our method outperforms several state-of-the-art MF methods by 10% ∼ 28. 4%. Our implementation is available at: https: //github. com/ dingjingtao/View_enhanced_ALS.

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