Design Strategy Transfer in Cognitively-Inspired Agents

Planning and strategizing are essential parts of the design process and are based on the designer’s skill. Further, planning is an abstract skill that can be transferred between similar problems. However, planning and strategy transfer within design have not been effectively modeled within computational agents. This paper presents an approach to represent this strategizing behavior using a probabilistic model. This model is employed to select the operations that computational agents should perform while solving configuration design tasks. This work also demonstrates that this probabilistic model can be used to transfer strategies from human data to computational agents in a way that is general and useful. This study shows a successful transfer of design strategy from human-to-computer agents, opening up the possibility of deriving high-performing behavior from designers and using it to guide computational design agents. Finally, a quintessential behavior of transfer learning is illustrated by agents while transferring design strategies across different problems, improving agent performance significantly. The work presented in this study leverages a computational framework built by embedding cognitive characteristics into agents, which has shown to mimic human problem-solving in configuration design problems.