Yonghong Wang

EAAI Journal 2025 Journal Article

Regional coverage balance and efficient worker recruitment for self-organized mobile crowdsourcing

• Ruiqing Liu
• Yonghong Wang
• Xiaofeng Wang

With the widespread adoption of smart devices, self-organized mobile crowdsourcing has become a popular method for decentralized data collection, where mobile users autonomously participate in task completion by leveraging their mobility and proximity to tasks. A key challenge in this context is achieving regional coverage balance, ensuring that tasks are equitably distributed across geographic areas to prevent both underserved and overserved regions. However, focusing solely on regional coverage without considering user satisfaction can negatively impact the quality of service. On the other hand, optimal worker recruitment in self-organized mobile crowdsourcing can maximize the expected overall quality of service. To address this, we develop a framework that balances regional coverage while enhancing user satisfaction by predicting user trajectories and identifying optimal service providers. Additionally, we tackle the worker recruitment problem by formulating a model that maximizes the expected quality of service. Our approach incorporates two collaborative deep learning networks: we first employ Proximal Policy Optimization (PPO) for matching candidates and training batches during the training phase, and then use Long Short-Term Memory (LSTM) to extract learning patterns of candidates, aiding PPO in making more effective recruitment decisions. We evaluate the performance of the proposed approach through extensive experiments using real-world data and by comparing it with existing strategies from previous research. Simulation results demonstrate that our method significantly improves both coverage balance and service quality in large-scale, decentralized mobile crowdsourcing environments.

TAAS Journal 2010 Journal Article

Evidence-based trust

• Yonghong Wang
• Munindar P. Singh

An evidence-based account of trust is essential for an appropriate treatment of application-level interactions among autonomous and adaptive parties. Key examples include social networks and service-oriented computing. Existing approaches either ignore evidence or only partially address the twin challenges of mapping evidence to trustworthiness and combining trust reports from imperfectly trusted sources. This article develops a mathematically well-formulated approach that naturally supports discounting and combining evidence-based trust reports. This article understands an agent Alice's trust in an agent Bob in terms of Alice's certainty in her belief that Bob is trustworthy. Unlike previous approaches, this article formulates certainty in terms of evidence based on a statistical measure defined over a probability distribution of the probability of positive outcomes. This definition supports important mathematical properties ensuring correct results despite conflicting evidence: (1) for a fixed amount of evidence, certainty increases as conflict in the evidence decreases and (2) for a fixed level of conflict, certainty increases as the amount of evidence increases. Moreover, despite a subtle definition of certainty, this work (3) establishes a bijection between evidence and trust spaces, enabling robust combination of trust reports and (4) provides an efficient algorithm for computing this bijection.

AAMAS Conference 2009 Conference Paper

Operators for Propagating Trust and their Evaluation in Social Networks

• Chung-Wei Hang
• Yonghong Wang
• Munindar P. Singh

AAMAS Conference 2008 Conference Paper

An Adaptive Probabilistic Trust Model and its Evaluation

• Chung-Wei Hang
• Yonghong Wang
• Munindar Singh

In open settings, the participants are autonomous and there is no central authority to ensure the felicity of their interactions. When agents interact in such settings, each relies upon being able to model the trustworthiness of the agents with whom it interacts. Fundamentally, such models must consider the past behavior of the other parties in order to predict their future behavior. Further, it is sensible for the agents to share information via referrals to trustworthy agents. Much progress has recently been made on probabilistic trust models including those that support the aggregation of information from multiple sources. However, current models do not support trust updates, leaving updates to be handled in an ad hoc manner. This paper proposes a trust representation that combines probabilities and certainty (defined as a function of a probability-certainty density function). Further, it offers a trust update mechanism to estimate the trustworthiness of referrers. This paper describes a testbed that goes beyond existing testbeds to enable the evaluation of a composite probability-certainty model. It then evaluates the proposed trust model showing that the trust model can (a) estimate trustworthiness of damping and capricious agents correctly, (b) update trust values of referrers accurately, and (c) resolve the conflicts in referral networks by certainty discounting.

IJCAI Conference 2007 Conference Paper

• Yonghong Wang
• Munindar P. Singh

Trust should be substantially based on evidence. Further, a key challenge for multiagent systems is how to determine trust based on reports from multiple sources, who might themselves be trusted to varying degrees. Hence an ability to combine evidence-based trust reports in a manner that discounts for imperfect trust in the reporting agents is crucial for multiagent systems. This paper understands trust in terms of belief and certainty: A 's trust in B is reflected in the strength of A 's belief that B is trustworthy. This paper formulates certainty in terms of evidence based on a statistical measure defined over a probability distribution of the probability of positive outcomes. This novel definition supports important mathematical properties, including (1) certainty increases as conflict increases provided the amount of evidence is unchanged, and (2) certainty increases as the amount of evidence increases provided conflict is unchanged. Moreover, despite a more subtle definition than previous approaches, this paper (3) establishes a bijection between evidence and trust spaces, enabling robust combination of trust reports and (4) provides an efficient algorithm for computing this bijection.

AAAI Conference 2006 Conference Paper

Trust Representation and Aggregation in a Distributed Agent System

• Yonghong Wang

This paper considers a distributed system of software agents who cooperate in helping their users to find services, provided by different agents. The agents need to ensure that the service providers they select are trustworthy. Because the agents are autonomous and there is no central trusted authority, the agents help each other determine the trustworthiness of the service providers they are interested in. This help is rendered via a series of referrals to other agents, culminating in zero or more trustworthy service providers being identified. A trust network is a multiagent system where each agent potentially rates the trustworthiness of another agent. This paper develops a formal treatment of trust networks. At the base is a recently proposed representation of trust via a probability certainty distribution. The main contribution of this paper is the definition of two operators, concatenation and aggregation, using which trust ratings can be combined in a trust network. This paper motivates and establishes some important properties regarding these operators, thereby ensuring that trust can be combined correctly. Further, it shows that effects of malicious agents, who give incorrect information, are limited.