Integrating Sequence Learning and Game Theory to Predict Design Decisions Under Competition
Integrating Sequence Learning and Game Theory to Predict Design Decisions Under Competition
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DOI：
10.1115/1.4048222
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发表时间：
2020-10
影响因子：
3.3
通讯作者：
A. E. Bayrak;Zhenghui Sha
中科院分区：
文献类型：
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作者：
A. E. Bayrak;Zhenghui Sha
设计可以被视为一个顺序和迭代的搜索过程。对人类顺序设计决策的基本理解和计算建模对于开发设计自动化和人类与人工智能协作的新方法至关重要。本文提出了一种通过将序列学习与博弈论相结合来预测未知目标函数下序列设计过程中设计师未来搜索行为的方法。虽然大多数现有研究侧重于从描述性和规范性的角度分析顺序设计决策，但本研究的目的是开发一个预测框架。我们使用包含设计者在竞争下实际顺序搜索决策的数据，这些数据是从之前开发的黑盒函数优化游戏中收集的。我们将长短期记忆网络与Delta方法相结合，以分布来预测下一个采样点，并将该模型与非合作博弈相结合，以预测设计者是否会根据对对手最佳设计的信念而停止搜索设计空间。在函数优化游戏中，所提出的模型在具有上限和下限的测试数据中准确预测了 82% 的下一个设计变量值和 92% 的下一个函数值，这表明长短期记忆网络可以根据过去的决策有效地预测下一个设计决策。此外，博弈论模型预测 60.8% 的参与者比实际情况更早停止搜索设计，同时准确预测其余 39.2% 的参与者何时停止。这些结果表明，大多数设计师表现出高估对手表现的强烈倾向，导致他们在寻找更好的设计上花费的钱比他们知道对手实际表现的情况下要多。
Design can be viewed as a sequential and iterative search process. Fundamental understanding and computational modeling of human sequential design decisions are essential for developing new methods in design automation and human–AI collaboration. This paper presents an approach for predicting designers’ future search behaviors in a sequential design process under an unknown objective function by combining sequence learning with game theory. While the majority of existing studies focus on analyzing sequential design decisions from the descriptive and prescriptive point of view, this study is motivated to develop a predictive framework. We use data containing designers’ actual sequential search decisions under competition collected from a black-box function optimization game developed previously. We integrate the long short-term memory networks with the Delta method to predict the next sampling point with a distribution, and combine this model with a non-cooperative game to predict whether a designer will stop searching the design space or not based on their belief of the opponent’s best design. In the function optimization game, the proposed model accurately predicts 82% of the next design variable values and 92% of the next function values in the test data with an upper and lower bound, suggesting that a long short-term memory network can effectively predict the next design decisions based on their past decisions. Further, the game-theoretic model predicts that 60.8% of the participants stop searching for designs sooner than they actually do while accurately predicting when the remaining 39.2% of the participants stop. These results suggest that a majority of the designers show a strong tendency to overestimate their opponents’ performance, leading them to spend more on searching for better designs than they would have, had they known their opponents’ actual performance.