A Cost-Aware Multi-Agent System for Black-Box Design Space Exploration

Abstract

Effective coordination of design teams must account for the influence of costs incurred while searching for the best design solutions. This paper introduces a cost-aware multi-agent system (MAS), a theoretical model to 1) explain how individuals in a team should search, assuming that they are all rational utility-maximizing decision-makers, and 2) study the impact of cost on the search performance of both individual agents and the system. First, we develop a new multi-agent Bayesian Optimization framework accounting for information exchange among agents to support their decisions on where to sample in search. Second, we employ a reinforcement learning approach based on the multi-agent deep deterministic policy gradient for training MAS to identify where agents cannot sample due to design constraints. Third, we propose a new cost-aware stopping criterion for each agent to determine when costs outweigh potential gains in search as a criterion to stop. Our results indicate that cost has a more significant impact on MAS communication in complex design problems than in simple ones. For example, when searching in complex design spaces, some agents could initially have low-performance gains, thus stopping prematurely due to negative payoffs, even if those agents could perform better in the later stage of the search. Therefore, global-local communication becomes more critical in such situations for the entire system to converge. The proposed model can serve as a benchmark for empirical studies to quantitatively gauge how humans would rationally make design decisions in a team.