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Xiaonan Li

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3 papers

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EAAI Journal 2025 Journal Article

A prospect theory-based three-way consensus model for multicriteria and multi-agent conflict analysis

Jiajia Wang
Xiaonan Li

The existing research on multicriteria and multi-agent (MC&MA) conflict analysis has gained increasing attention. However, it mainly focuses on inter-agent relationships and lacks resolution strategies. To this end, this paper proposes a three-way consensus model based on prospect theory (PT), aiming to provide feasible strategies for the MC&MA conflict analysis problem. Specifically, in Phase I, a PT-based three-way decision model (3WD) is introduced, which calculates the prospect value of each option by generalized interval-valued fuzzy rough sets (GIVFRSs). Secondly, a two-stage classification method is proposed to filter out a feasible option set with maximum prospect value. In Phase II, a consensus decision model based on a graph model is established from an optimization perspective, which revises target information by maximizing consensus degree and ensures that all agents reach consensus. Finally, a labor-management negotiation case study is used to demonstrate the practicality of the proposed model. Experimental analyzes with control variables show that the effects of parameter changes on the model are consistent with theoretical analysis, and the model is able to reach consensus in fewer iterations. Therefore, this study provides a theoretical foundation and practical guidelines for resolving the MC&MA conflict analysis problem, which has significant theoretical value and practical implications.

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AAAI Conference 2020 Conference Paper

Learning Sparse Sharing Architectures for Multiple Tasks

Tianxiang Sun
Yunfan Shao
Xiaonan Li
Pengfei Liu
Hang Yan
Xipeng Qiu
Xuanjing Huang

Most existing deep multi-task learning models are based on parameter sharing, such as hard sharing, hierarchical sharing, and soft sharing. How choosing a suitable sharing mechanism depends on the relations among the tasks, which is not easy since it is difﬁcult to understand the underlying shared factors among these tasks. In this paper, we propose a novel parameter sharing mechanism, named Sparse Sharing. Given multiple tasks, our approach automatically ﬁnds a sparse sharing structure. We start with an over-parameterized base network, from which each task extracts a subnetwork. The subnetworks of multiple tasks are partially overlapped and trained in parallel. We show that both hard sharing and hierarchical sharing can be formulated as particular instances of the sparse sharing framework. We conduct extensive experiments on three sequence labeling tasks. Compared with single-task models and three typical multi-task learning baselines, our proposed approach achieves consistent improvement while requiring fewer parameters.

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TIST Journal 2012 Journal Article

Entity-Relationship Queries over Wikipedia

Xiaonan Li
Chengkai Li
Cong Yu

Wikipedia is the largest user-generated knowledge base. We propose a structured query mechanism, entity-relationship query, for searching entities in the Wikipedia corpus by their properties and interrelationships. An entity-relationship query consists of multiple predicates on desired entities. The semantics of each predicate is specified with keywords. Entity-relationship query searches entities directly over text instead of preextracted structured data stores. This characteristic brings two benefits: (1) Query semantics can be intuitively expressed by keywords; (2) It only requires rudimentary entity annotation, which is simpler than explicitly extracting and reasoning about complex semantic information before query-time. We present a ranking framework for general entity-relationship queries and a position-based Bounded Cumulative Model (BCM) for accurate ranking of query answers. We also explore various weighting schemes for further improving the accuracy of BCM. We test our ideas on a 2008 version of Wikipedia using a collection of 45 queries pooled from INEX entity ranking track and our own crafted queries. Experiments show that the ranking and weighting schemes are both effective, particularly on multipredicate queries.

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