NDLI: The Impacts of Diffusion Kernels on Recognition-Primed Multi-agent Decision Making

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The Impacts of Diffusion Kernels on Recognition-Primed Multi-agent Decision Making

Content Provider	ACM Digital Library
Author	Fan, Xiaocong Su, Meng
Abstract	Novel perspectives on multiscale information analysis are highly demanded in several areas of multi-agent research, including large-scale agent organization and experience-based decision making. A recent breakthrough in harmonic analysis is diffusion geometry and diffusion wavelets, which offers a general framework for multiscale analysis of massive data sets. In this paper, we investigate the impacts of various diffusion kernel functions on the performance of solution synthesis in experience-based multi-agent decision making. In particular, we take a two-phase approach to conduct our experiment. In phase one (learning), we apply four commonly-used kernel functions on a data set including a large collection of battlefield decision experiences, identifying the kernel functions that can outperform the others. In phase two, we apply the kernels identified in phase one to an independent data set for testing. It is shown that Cosine exponential outperformed the other kernel functions. In general, this study indicates that, in order to achieve the best possible performance in diffusion multiscale analysis, it is critical to identify kernel functions that are applicable to the massive data set under concern.
Starting Page	117
Ending Page	124
Page Count	8
ISBN	9780769541914
DOI	10.1109/WI-IAT.2010.17
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-08-31
Access Restriction	Subscribed
Subject Keyword	Cognitive agent recognition decision making diffusion distance experience
Content Type	Text
Resource Type	Article

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