Things are about to get interesting. You've likely heard that Google's DeepMind recently beat the world's best Go player. But in far more practical and pervasive ways, artificial intelligence (AI) is creeping into every aspect of life--every screen you view, every search, every purchase, and every customer service contact. It's the confluence of several technologies--Moore's law made storage, computing, and access devices almost free. This Venn diagram illustrates how deep learning is a subset of AI and how, when combined with big data, can inform enabling technologies in many sectors.

We tackle the problem of providing engineering decision makers with relevant information extracted from data obtained via a process model based on deliberation and voting. We list examples of potential applications from the area of bug-fix scheduling for software, as well as space-vehicles 'go'-'no-go' decision making. In such application domains, important decisions have to be made hastily and therefore the decision factors have to be informed timely of the main issues discovered by the teams. A logic is proposed for reasoning with comments available in such deliberations. Search based algorithms are proposed which recommend the best justifications for a decision and retain the voting decisions for interested parties to tally. We have developed a Bayesian network for generating data by simulation based on probabilistic models that we can train from collected deliberation databases. The data generated in this way was used for evaluating the proposed search algorithm, showing how it can provide better than random recommendations of arguments to decision makers.