Multi-agent Diffusion of Decision Experiences

Abstract

Diffusion geometry offers a general framework for multiscale analysis of massive data sets on manifold. However, its applicability is greatly limited due to the lack of work on distributed diffusion computing, as a data set expands over time, it can quickly exceed the processing capacity of a single agent. In this paper, we propose a multi-agent diffusion approach where a massive data set can be split into several subsets and each diffusion agent only needs to work with one subset in diffusion computation. We conduct an experiment by applying various splitting strategies to a large set of human decision-making experiences. The result indicates that the multi-agent diffusion approach is promising, and it is possible to benefit from using a large group of diffusion agents if their diffusion maps were constructed from subsets with shared data points (experiences). This study encourages the application of multi-agent diffusion approach to systems that rely on massive data analysis, and will stimulate further investigations on distributed diffusion computing.