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Jiuchuan Jiang

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9 papers

1 author row

TIST Journal 2026 Journal Article

Chain Disruption Risk-Oriented Task Migration in Multiplex Networked Industrial Chains

Kai Di
Tian-Yu Zuo
Pan Li
Jiuchuan Jiang
Yichuan Jiang

In industrial production processes, disruptions within the industrial chain can severely affect the collaborative capabilities of production agents. A notable example occurred during the COVID-19 pandemic, when many agents faced interruption risks and were unable to participate in coordinated production. Ensuring continuity under such conditions requires migrating tasks from disrupted agents to viable alternatives. Designing effective task migration strategies, however, must account for the emergent multiplex nature of modern industrial chains. In these multiplex networked industrial chains, disruption risk in one layer can propagate to others, generating cascading failures across the system. This introduces two key challenges: (1) disruption risk creates mismatches not only between product agents and tasks but also across network layers, enlarging the problem dimensionality; and (2) simultaneous disruptions across multiple agents and layers increase the volume of tasks needing migration, greatly expanding the solution space. To address these challenges, we introduce the notion of a multiplex potential field, which captures cross-layer interdependencies and system-level dynamics in multiplex industrial chains. Building on this concept, we develop a hierarchical contextual task migration algorithm that exploits the multiplex potential field to guide both inter-layer and intra-layer task reallocations. Extensive experiments show that our approach consistently achieves superior utility, markedly improves task completion ratios, and reduces execution costs compared to benchmark algorithms. Furthermore, it attains solution quality comparable to that of the optimal CPLEX solver while requiring substantially less computation time. Finally, a case study on the FAO international food trade network demonstrates that the proposed framework is not only theoretically robust but also practically effective when deployed on large-scale real-world multiplex systems.

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EAAI Journal 2026 Journal Article

Mitigating influence of selection and conformity biases for user interest in recommender systems

Tiansheng Zheng
Xia Xu
Shuqing Li
Qingwei Pan
Jiuchuan Jiang
Mengzhu Du

Recent studies have revealed the existence of selection bias and conformity bias in collaborative filtering, but few works have explored their joint impact. Moreover, most existing debiasing methods are designed for specific recommendation models, often relying on non-numerical inputs, and thus lack general applicability to models with different input forms, such as numerical inputs. To address these limitations, this paper proposes a general debiasing framework that mitigates selection and conformity biases without requiring uniform data and can be flexibly integrated with diverse recommendation models. Specifically, this paper first introduces a bias discretization method that maps continuous bias values into discrete bias labels, followed by a label fusion strategy to combine different types of bias labels. This paper then designs a bias learner, which leverages discretization and fusion to capture and model biases generated in user–item interactions. Finally, this paper presents Debiasing Learning for Selection and Conformity Biases, a novel framework that separates recommendation learning from bias learning, enabling unbiased recommendations by removing bias information during testing. Extensive experiments on three real-world datasets demonstrate that the proposed method mitigates data bias and exhibits strong robustness and generality.

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AAMAS Conference 2023 Conference Paper

Structural Credit Assignment-Guided Coordinated MCTS: An Efficient and Scalable Method for Online Multiagent Planning

Qian Che
Wanyuan Wang
Fengchen Wang
Tianchi Qiao
Xiang Liu
Jiuchuan Jiang
Bo An
Yichuan Jiang

Online planning has been widely focused in many areas, such as industry chain and collective intelligence. Due to the trade-off nature of trading computation time for solution quality, Monte-Carlo tree search (MCTS) methods have shown great success in online planning. However, the exponential growth of global joint-action space makes it challenging to apply MCTS to online multiagent planning (MAP). Our goal in this paper is to design an efficient and scalable coordinated MCTS method for online MAP. Combining with coordination graphs, recent Factored Value MCTS (FV-MCTS) has attempted to recover the trade-off property for MCTS-based online MAP. However, FV-MCTS directly uses the global payoff to reward each agent, and has difficulty in finding coordination actions in multiagent MCTS settings where other agents are also taking exploratory actions. We overcome this limitation by designing a generalized structural credit assignment (SCA)-guided coordinated MCTS, where SCA is used to promote coordination and MCTS is used to search promising global joint-actions. Specially, we use the Shapley value to provide a fair SCA, which can be efficiently computed by exploiting locality of interaction between agents. Moreover, theoretical analysis shows that the proposed method can bound the bias of the estimated value of the global join-action under certain conditions. Finally, we conduct extensive experiments in some typical sequential multiagent coordination domains such as multi-robot warehouse patrolling in industry chain, etc. to validate the efficiency and scalability of the proposed method over other benchmarks. ∗corresponding author. Proc. of the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023), A. Ricci, W. Yeoh, N. Agmon, B. An (eds.), May 29 – June 2, 2023, London, United Kingdom. © 2023 International Foundation for Autonomous Agents and Multiagent Systems (www. ifaamas. org). All rights reserved.

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TIST Journal 2022 Journal Article

A Foraging Strategy with Risk Response for Individual Robots in Adversarial Environments

Kai Di
Yifeng Zhou
Fuhan Yan
Jiuchuan Jiang
Shaofu Yang
Yichuan Jiang

As an essential problem in robotics, foraging means that robots collect objects from a given environment and return them to a specified location. On many occasions, robots are required to perform foraging tasks in adversarial environments, such as battlefield rescue, where potential adversaries may damage robots with a certain probability. The longer an individual robot moves through adversarial environments, the higher the probability of being damaged by adversaries. The robot system can gain utility only when the robot brings carried objects back to a predetermined home station. Such a risk of being damaged makes returning home at different locations potentially relevant to the expected utility produced by the robot. Thus, the individual robot faces a dilemma when it responds to the potential risks in adversarial environments: whether to return the carried resources home or continue foraging tasks. In this article, two fundamental environment settings are discussed, homogeneous cases and heterogeneous cases. The former is analyzed as having both the optimal substructure property and the non-aftereffect property. Then, we present a dynamic programming (DP) algorithm that can find an optimal solution with polynomial time complexity. For the latter, it is proven that finding an optimal solution is \( \mathcal {NP} \) -hard. We then propose a heuristic algorithm: A division hierarchical path planning (DHPP) algorithm that is based on the idea of dividing the foraging routes generated initially into a certain number of subroutes to dilute risks. Finally, these algorithms are extensively evaluated in simulations, concluding that in adversarial environments, they can significantly improve the productivity of an individual robot before it is damaged.

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IS Journal 2021 Journal Article

Enhancing the Quality in Crowdsourcing E-Markets Through Team Formation Games

Bing Ai
Wanyuan Wang
Minghui Hua
Yichuan Jiang
Jiuchuan Jiang
Yifeng Zhou

Crowdsourcing e-markets have been widely used to complete various complex tasks with the help of seamlessly integrating the ubiquitous intelligent systems and artificial intelligence. Most of traditional crowdsourcing e-markets focus primarily on individual incentives to motivate workers to participate. However, workers generally have heterogeneous values, which brings forth an undesirable outcome that workers with larger values monopolize a fixed total payment. This monopolization discourages the workers with smaller values from participating, thereby severely reducing the quality of individual-oriented crowdsourcing e-markets. Thus, this article proposes a team formation (TF) games-based mechanism in which workers are incentivized to form teams and obtain payments according to their contributions. Moreover, the existence of Nash equilibrium for a given TF game is rigorously analyzed. Finally, the experimental results in terms of both simulated and realistic datasets demonstrate that, as compared with state-of-the-art methods, our mechanism can achieve higher crowdsourcing e-markets quality.

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TAAS Journal 2021 Journal Article

Risk-aware Collection Strategies for Multirobot Foraging in Hazardous Environments

Kai Di
Yifeng Zhou
Jiuchuan Jiang
Fuhan Yan
Shaofu Yang
Yichuan Jiang

Existing studies on the multirobot foraging problem often assume safe settings, in which nothing in an environment hinders the robots’ tasks. In many real-world applications, robots have to collect objects from hazardous environments like earthquake rescue, where possible risks exist, with possibilities of destroying robots. At this stage, there are no targeted algorithms for foraging robots in hazardous environments, which can lead to damage to the robot itself and reduce the final foraging efficiency. A motivating example is a rescue scenario, in which the lack of a suitable solution results in many victims not being rescued after all available robots have been destroyed. Foraging robots face a dilemma after some robots have been destroyed: whether to take over tasks of the destroyed robots or continue executing their remaining foraging tasks. The challenges that arise when attempting such a balance are twofold: (1) the loss of robots adds new constraints to traditional problems, complicating the structure of the solution space, and (2) the task allocation strategy in a multirobot team affects the final expected utility, thereby increasing the dimension of the solution space. In this study, we address these challenges in two fundamental environmental settings: homogeneous and heterogeneous cases. For the former case, a decomposition and grafting mechanism is adopted to split this problem into two weakly coupled problems: the foraging task execution problem and the foraging task allocation problem. We propose an exact foraging task allocation algorithm, and graft it to another exact foraging task execution algorithm to find an optimal solution within the polynomial time. For the latter case, it is proven \( \mathcal {NP} \) -hard to find an optimal solution in polynomial time. The decomposition and grafting mechanism is also adopted here, and our proposed greedy risk-aware foraging algorithm is grafted to our proposed hierarchical agglomerative clustering algorithm to find high-utility solutions with low computational overhead. Finally, these algorithms are extensively evaluated through simulations, demonstrating that compared with various benchmarks, they can significantly increase the utility of objects returned by robots before all the robots have been stopped.

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TAAS Journal 2018 Journal Article

Understanding Crowdsourcing Systems from a Multiagent Perspective and Approach

Jiuchuan Jiang
Bo An
Yichuan Jiang
Donghui Lin
Zhan Bu
Jie Cao
Zhifeng Hao

Crowdsourcing has recently been significantly explored. Although related surveys have been conducted regarding this subject, each has mainly consisted of a review of a single aspect of crowdsourcing systems or on the application of crowdsourcing in a specific application domain. A crowdsourcing system is a comprehensive set of multiple entities, including various elements and processes. Multiagent computing has already been widely envisioned as a powerful paradigm for modeling autonomous multi-entity systems with adaptation to dynamic environments. Therefore, this article presents a novel multiagent perspective and approach to understanding crowdsourcing systems, which can be used to correlate the research on crowdsourcing and multiagent systems and inspire possible interdisciplinary research between the two areas. This article mainly discusses the following two aspects: (1) The multiagent perspective can be used for conducting a comprehensive survey on the state of the art of crowdsourcing, and (2) the multiagent approach can bring about concrete enhancements for crowdsourcing technology and inspire future research directions that enable crowdsourcing research to overcome the typical challenges in crowdsourcing technology. Finally, this article discusses the advantages and disadvantages of the multiagent perspective by comparing it with two other popular perspectives on crowdsourcing: the business perspective and the technical perspective.

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AAMAS Conference 2008 Conference Paper

Convergence at Prominent Agents: A Non-Flat Synchronization Model of Situated Multi-Agents

Jiuchuan Jiang
Yichuan Jiang

This paper presents a novel non-flat synchronization model where the synchronization capacity of each agent is different regarding its social rank and strategy dominance. In the presented model, the prominent agents may have higher synchronization forces, and finally the collective synchronization results may incline to converge at such prominent agents’ strategies, which is called prominence convergence in collective synchronization and proved by our experimental results. The presented model can well match the peculiarities of real multi-agent societies where each agent plays a different role in the synchronization, and make up the restrictions of related benchmark works that only concerned about the flat synchronization.

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AAMAS Conference 2007 Conference Paper

Agent Coordination by Trade-off between Locally Diffusion Effects and Socially Structural Influences

Yichuan Jiang
Jiuchuan Jiang
Toru Ishida

There were always two separated methods to make agent coordination: individual-local balance perspective and individualsociety balance perspective. The first method only considered the balance between individual agents and their local neighbors; the second method only considered the balance between individual agents and the whole multi-agent society. However, in reality, the agents will be diffused by their local neighbors as well as influenced by their social contexts simultaneously; therefore, it is necessary to deal with the social performance as well as local performance. To address such problem this paper presents an agent coordination method in an integrative model where we combine the two perspectives together and make trade-off between them. With our presented model, the individual, local and social concerns can be balanced well in a unified and flexible manner. Moreover, the experimental results show that there are often situations in which the two coordination perspectives aren't conflictive but often bring out the better in each other.

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