Quan Bai

IJCAI Conference 2024 Conference Paper

An LLM-enhanced Agent-based Simulation Tool for Information Propagation

• Yuxuan Hu
• Gemju Sherpa
• Lan Zhang
• Weihua Li
• Quan Bai
• Yijun Wang
• Xiaodan Wang

Influence diffusion models are used for simulating information propagation in social networks. While most existing influence diffusion models are probabilistic, the emergence of Large Language Model (LLM) sheds light on the language-level inferences and interactions of user agents. This paper presents an LLM-enhanced Agent-based Influence Diffusion model (LAID), and a web-based visualization tool, LAIDSim, for simulating the information propagation in social networks.

IJCAI Conference 2022 Conference Paper

AI Facilitated Isolations? The Impact of Recommendation-based Influence Diffusion in Human Society

• Yuxuan Hu
• Shiqing Wu
• Chenting Jiang
• Weihua Li
• Quan Bai
• Erin Roehrer

AI recommendation techniques provide users with personalized services, feeding them the information they may be interested in. The increasing personalization raises the hypotheses of the "filter bubble" and "echo chamber" effects. To investigate these hypotheses, in this paper, we inspect the impact of recommendation algorithms on forming two types of ideological isolation, i. e. , the individual isolation and the topological isolation, in terms of the filter bubble and echo chamber effects, respectively. Simulation results show that AI recommendation strategies severely facilitate the evolution of the filter bubble effect, leading users to become ideologically isolated at an individual level. Whereas, at a topological level, recommendation algorithms show eligibility in connecting individuals with dissimilar users or recommending diverse topics to receive more diverse viewpoints. This research sheds light on the ability of AI recommendation strategies to temper ideological isolation at a topological level.

AAAI Conference 2021 System Paper

A Novel Mountain Driving Unity Simulated Environment for Autonomous Vehicles

• Xiaohu Li
• Zehong Cao
• Quan Bai

The simulated driving environment provides a low cost and time-saving platform to test the performance of the autonomous vehicle by linkage with existing machine learning approaches. However, most of existing simulated driving environments focus on building flat roads in urban areas. Still, they neglected to endeavour the tough steep, curvy hill roads, such as mountain paths around suburban areas. In this study, by deploying in Unity engine, we developed the first complex mountain driving simulated environment with characterizing continuous curves and up/downhill. Then, two state-of-art reinforcement learning (RL) algorithms are used to train a vehicle agent and test the performance of autonomous vehicles in our developed simulated environment. Also, we set 5 different levels of vehicle’s speeds and observe the cumulative rewards during the vehicle agent training. Our demonstration presents the developed environment supports for complex mountain scenario configurations and RL-based autonomous vehicles, and our findings show that the vehicle agent could achieve high cumulative rewards during the training stage, suggesting that our work is a potential new simulation environment for autonomous vehicles research. The demonstration video can be viewed via the link: https: //youtu. be/0wSqGeCn-NU.

AAMAS Conference 2021 Conference Paper

Graph-based Self-Adaptive Conversational Agent

• Lan Zhang
• Weihua Li
• Quan Bai
• Edmund Lai

Conversational agents have been widely adopted in dialogue systems for various business purposes. Many existing conversational agents are rule-based and require significant human intervention to adapt the knowledge and conversational flow. In this paper, we propose a graph-based adaptive conversational agent model which is capable of learning knowledge from human beings and adapting the knowledge-base according to human-agent interactions. Studies to evaluate the proposed model are conducted and presented, which compare the responses from the proposed adaptive agent model and a conventional agent.

AAMAS Conference 2021 Conference Paper

Learning Policies for Effective Incentive Allocation in Unknown Social Networks

• Shiqing Wu
• Quan Bai
• Weihua Li

Most existing incentive allocation approaches rely on sufficient information about users’ attributes, such as their preferences, followers in the social network, and activities, to customize effective incentives. However, this may lead to failure when such knowledge is unavailable. In this light, we propose an end-to-end reinforcement learning-based framework, named Geometric Actor-Critic (GAC), to discover effective incentive allocation policies towards users in a social network. More specifically, given a limited budget, the proposed approach can extract information from a high-level network representation for learning effective incentive allocation policies. The proposed GAC only requires the topology of the social network and does not rely on any prior information about users’ attributes. We use three real-world social network datasets to evaluate the performance of the proposed GAC. The experimental results demonstrate the effectiveness of the proposed approach.

AAMAS Conference 2019 Conference Paper

Dynamic Source Weight Computation for Truth Inference over Data Streams

• Yi Yang
• Quan Bai
• Qing Liu

Truth inference, a method that resolves conflicts among multi-agent data, has been widely studied in the field of AI. Most existing truth inference methods use iterative approaches to achieve high accuracy, but are inefficient to infer object truths over data streams. The methods developed for streaming data can achieve high efficiency but suffer from low accuracy. In this paper, we propose a novel truth inference method, Dynamic Source Weight Computation truth inference (DSWC), that can work with a wide range of iterative-based truth inference methods to dynamically compute source weights over data streams. Specifically, we use Taylor expansion to analyze the unit error of object truths inferred by source weights computed at a previous timestamp. If the source weight at present is predicted to be able to limit the error under a threshold, we use the source weights computed previously to approximate object truths at present to avoid the expensive source weight computation step. Compared with the existing work, the proposed method is more effective in predicting source weights and can be applied to a wider range of applications. Experimental results based on four real-world datasets demonstrate that DSWC is both accurate and efficient for truth inference over data streams.

AAMAS Conference 2019 Conference Paper

Modeling Random Guessing and Task Difficulty for Truth Inference in Crowdsourcing

• Yi Yang
• Quan Bai
• Qing Liu

This paper addresses the challenge of truth inference in crowdsourcing applications. We propose a generative method that jointly models tasks’ difficulties, workers’ abilities and guessing behavior to estimate the truths of crowdsourced tasks, which leads to a more accurate estimation on the workers’ abilities and tasks’ truths. Experiments demonstrate that the proposed method is more effective for estimating truths of crowdsourced tasks compared with the state-of-art methods.

TAAS Journal 2018 Journal Article

An Innovative Approach for Ad Hoc Network Establishment in Disaster Environments by the Deployment of Wireless Mobile Agents

• Xing Su
• Minjie Zhang
• Quan Bai

In disasters, many stationary tasks, such as saving survivors in debris, extinguishing fire of buildings, and so on, need first responders to complete on site. In such circumstances, wireless mobile robots are usually employed to search for tasks and establish ad hoc networks to assist first responders. Due to the unknown and complexity of environments and limited capabilities of wireless mobile robots, searching and establishing ad hoc networks in disaster environments is a challenging issue in both theory and practice. To this end, a task-based wireless mobile robot deployment approach is proposed in this article. The proposed approach consists of a search process and a deployment process. The search process can guide wireless mobile robots to efficiently find tasks in unknown and complex environments. The deployment process can find suitable deployment locations for wireless mobile robots to establish ad hoc networks. The established ad hoc networks can ensure the communication of wireless mobile robots in the network and can cover the maximum number of task locations and the maximum areas in a disaster environment. Experimental results demonstrate that based on the proposed approach, wireless mobile robots have better performance in terms of search and ad hoc network establishment in disaster environments.

AAMAS Conference 2018 Conference Paper

Modelling Multiple Influences Diffusion in On-line Social Networks

• Weihua Li
• Quan Bai
• Minjie Zhang
• Tung Doan Nguyen

In on-line social networks, innovations in the presence of one or more influences disseminate through the topological structure of the networks rapidly. In reality, various influences normally coexist in the same context and have subtle relations, such as supportive, contradictory and competitive relations, affecting the users’ decisions of adopting any innovations. Therefore, modelling diffusion process of multiple influences is an important, yet challenging research question. By employing the agent-based modelling, in this paper, a distributed approach has been proposed to model the diffusion process of multiple influences in social networks. The proposed model has been applied in the undesirable influence minimisation problem, where the time series is taken into consideration. The experimental results show our model can be utilised to minimise the adverse impact of a certain influence by injecting other influences. Furthermore, the proposed model also sheds light on understanding, investigating and analysing multiple influences in social networks

AAMAS Conference 2017 Conference Paper

Agent-based Influence Maintenance in Social Networks

• Weihua Li
• Quan Bai
• Tung Doan Nguyen
• Minjie Zhang

We study on how to maintain long-term influence in a social network by proposing an agent-based influence maintenance model. Within the context of our investigation, the experimental results reveal that multiple-time seed selection is capable of achieving more constant impact than one-shot selection.

AAMAS Conference 2016 Conference Paper

Trust Management for Composite Services in Distributed Multi-Agent Systems with Indirect Ratings (Extended Abstract)

• Tung Doan Nguyen
• Quan Bai

AAMAS Conference 2012 Conference Paper

Effective Methods for Generating Collision Free Paths for Multiple Robots based on Collision Type

• Fan Liu
• Ajit Narayanan
• Quan Bai