Blockchain Smart Contract Meta-modeling

One of the key benefits of blockchain technology is its ability to keep a permanent, unalterable record of transactions. In business environments, where companies interact with each other without a centralized authority to ensure trust between them, this has led to blockchain platforms and smart contracts being proposed as a means of implementing trustworthy collaborative processes. Software engineers must deal with them to ensure the quality of smart contracts in all phases of the smart contract lifecycle, from requirements specifications to design and deployment. This broad scope and criticality of smart contracts in business environments means that they have to be expressed in a language that is intuitive, easy-to-use, independent of the blockchain platform employed, and oriented towards software quality assurance. In this paper we present a key component: a first outline of a UML-based smart contract meta-model that would allow us to achieve these objectives. This meta-model will be enriched in future work to represent blockchain environments and automated testing.

Mission Engineering and Design using Real-Time Strategy Games: An Explainable-AI Approach

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.

A Meta-Modeling Power Consumption Forecasting Approach Combining Client Similarity and Causality

Power forecasting models offer valuable insights on the electricity consumption patterns of clients, enabling the development of advanced strategies and applications aimed at energy saving, increased energy efficiency, and smart energy pricing. The data collection process for client consumption models is not always ideal and the resulting datasets often lead to compromises in the implementation of forecasting models, as well as suboptimal performance, due to several challenges. Therefore, combinations of elements that highlight relationships between clients need to be investigated in order to achieve more accurate consumption predictions. In this study, we exploited the combined effects of client similarity and causality, and developed a power consumption forecasting model that utilizes ensembles of long short-term memory (LSTM) networks. Our novel approach enables the derivation of different representations of the predicted consumption based on feature sets influenced by similarity and causality metrics. The resulting representations were used to train a meta-model, based on a multi-layer perceptron (MLP), in order to combine the results of the LSTM ensembles optimally. This combinatorial approach achieved better overall performance and yielded lower mean absolute percentage error when compared to the standalone LSTM ensembles that do not include similarity and causality. Additional experiments indicated that the combination of similarity and causality resulted in more performant models when compared to implementations utilizing only one element on the same model structure.