Learning agent in heuristic architecture optimization

Abstract

One of the main problems of current system design and architecture tools is the communication between the tool and the user and vice-versa. This work describes an on-going effort to improve that communication by incorporating an interactive and transparent learning agent into a multi-agent tradespace exploration tool. This learning agent mines the current population of individuals, which we will name architectures, for driving features (combination of architectural variables) that appear to drive architectures towards a "good region'' or a "bad region'' of the tradespace, and shares that information with the user. This information is used to produce a surrogate model based on a decision tree. The information about driving features is also fed to an adaptive heuristic optimization agent. Information about driving features, surrogate models, and heuristics can help the user understand the results of the tool better and gain useful architectural insight.