Taiwei Shi, Zhuoer Wang, Longqi Yang, Ying-Chun Lin, Zexue He, Mengting Wan, Pei Zhou, Sujay Kumar Jauhar, Sihao Chen, Shan Xia, Hongfei Zhang, Jieyu Zhao, Xiaofeng Xu, Xia Song, Jennifer Neville. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Existing ordered weighted average (OWA) characterization methods maximize similarity among information sources by seeking maximal weights entropy or by minimizing weights variance. These methods are based solely on the weights, and the uncertainties of input information sources are ignored. However, the purpose of information fusion is to decrease uncertainty and improve data quality. Following this objective, this work proposes a new method to calculate the OWA weights based on the minimization of the aggregated uncertainty. The resulting aggregated value is the most precise, in the sense that any other combination of weights produces larger uncertainty. © 2010 Wiley Periodicals, Inc.

With the emerging applications of multi-agent systems, there is always a need for simulation to verify the results before actual implementation. Multi-agent simulation provides a test bed for several soft-computing algorithms like fuzzy logic, learning automata, evolutionary algorithms, etc. In this paper we discuss the fusion of these soft-computing methodologies and existing tools for discrete event simulation (DEVS) for multi-agent simulation. We propose a methodology for combining the agent-based architecture, discrete event system and soft-computing methods in the simulation of multi-agent robotics and network security system. We also define a framework called Virtual Laboratory (V-Lab/spl reg/) for multi-agent simulation using intelligent tools.