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In this paper, we adapt computational design approaches, widely used by the engineering design community, to address the unique challenges associated with mission design using RTS games. Specifically, we present a modeling approach that combines experimental design techniques, meta-modeling using convolutional neural networks (CNNs), uncertainty quantification, and explainable AI (XAI). We illustrate the approach using an open-source real-time strategy (RTS) game called microRTS. The modeling approach consists of microRTS player agents (bots), design of experiments that arranges games between identical agents with asymmetric initial conditions, and an AI infused layer comprising CNNs, XAI, and uncertainty analysis through Monte Carlo Dropout Network analysis that allows analysis of game balance. A sample balanced game and corresponding predictions and SHapley Additive exPlanations (SHAP) are presented in this study. Three additional perturbations were introduced to this balanced gameplay and the observations about important features of the game using SHAP are presented. Our results show that this analysis can successfully predict probability of win for self-play microRTS games, as well as capture uncertainty in predictions that can be used to guide additional data collection to improve the model, or refine the game balance measure.

We address the problem of content analysis in text-based engineering design communication. Existing methods to characterize communication content in engineering design are manual or qualitative, which is tedious for large datasets. We formulate the characterization of communication messages as an intent classification task. We identify two intents — Intent 1 captures the presence and flow of information, Intent 2 captures specific topics about design parameters and objectives. We compare the predictive accuracy of convolutional LSTM, character-based convolutional LSTM, XLNet, and BERT models for the intent classification task. The results of our comparison show that the XL-Net model predicts Intents 1 and 2 with 88% and 81% accuracy, respectively, on text data collected from 40 teams in a design experiment with university students. We analyze the differences in communication patterns between high- and low-performing teams. Time-series studies show that high-performing teams have more responsive communication and a higher consistency of information exchange.

Existing literature on information sharing in contests has established that sharing contest-specific information influences contestant behaviors, and thereby, the outcomes of a contest. However, in the context of engineering design contests, there is a gap in knowledge about how contest-specific information such as competitors’ historical performance influences designers’ actions and the resulting design outcomes. To address this gap, the objective of this study is to quantify the influence of information about competitors’ past performance on designers’ belief about the outcomes of a contest, which influences their design decisions, and the resulting design outcomes. We focus on a single-stage design competition where an objective figure of merit is available to the contestants for assessing the performance of their design. Our approach includes (i) developing a behavioral model of sequential decision making that accounts for information about competitors’ historical performance and (ii) using the model in conjunction with a human-subject experiment where participants make design decisions given controlled strong or weak performance records of past competitors. Our results indicate that participants spend greater efforts when they know that the contest history reflects that past competitors had a strong performance record than when it reflects a weak performance record. Moreover, we quantify cognitive underpinnings of such informational influence via our model parameters. Based on the parametric inferences about participants’ cognition, we suggest that contest designers are better off not providing historical performance records if past contest outcomes do not match their expectations setup for a given design contest.

Product personalization will play a key role in the future of society by making these products available for everyone, everywhere. Personalized production requires the involvement of the customer in the design process. Thus, tools to identify which components and modules allow for customer interaction are needed, and to further assess the effects of customer interaction on the product design and the manufacturing system. In this article, we propose a framework to analyze the extent of personalization a designer and a manufacturing firm can achieve in the context of efficient personalized production. We study this as a dyad of product design and manufacturing system, which uses two main attributes: product modularity and manufacturing configuration complexity. To demonstrate our ideas, we use two applications. The first is a case study for gearbox, usually manufactured at high levels of efficiency under the mass production paradigm. The second case is an application for the footwear industry for the particular case of a sneaker, a sports shoe that is prone to be customized. These case studies show the flexibility of this framework to a wide set of industries. In both cases, we compare a personalization scenario with a baseline scenario and provide managerial insights.