RI: Small: From a Machine Detector to a Machine Detective: Decisions and Queries with Uncertain and Incomplete Information

大部分机器学习都致力于不切实际的无菌设置，其中预测任务的所有相关信息都已预先记录并输入算法。这与现实世界中的许多问题是不协调的，在这些问题中，预测和决策必须在不完整和混乱的信息下做出。此外，当前的机器学习范式还没有为自动化的未来做好准备，在自动化的未来中，算法代理不仅仅是被动地获得信息，而是能够在做出决策时主动地从环境中获取信息。该项目将超越当前的范式，开发一种“机器探测器”，一种能够用不完整的实例进行推理并与环境交互以获得新信息或新线索的系统。该项目不仅将提高机器在医疗保健等记录数据充满缺失值的领域的预测能力，而且还应该在交互领域中实现更有效和鲁棒的预测，例如用于教育评估的计算机化自适应测试和客户服务/故障排除聊天机器人，其中机器可以与用户交互（或环境）以获得新的相关信息。该奖项所产生的工作将作为智能代理的基础，这些智能代理对他们的决策过程进行了强有力的推理，并权衡了错误预测的成本（例如，假阳性或假阴性）和成本（例如，在时间、风险或金钱上）获得额外信息。这扩展了机器学习中的典型范例，使机器能够顺序查询未观察到的特征，从而通过以下目标做出更确定的预测。首先，该项目开发了可以通过新的生成模型来推断部分观察到的实例的方法，该模型可以学习特征之间的条件依赖关系。其次，学习的依赖关系被用作基础，学习非贪婪策略，以获取手头上的特定实例的信息未观察到的功能。最后，该项目构建可以在时空域上获取数据的模型，其中特征通过空间坐标索引（例如，当在现场收集信息时），或者按时间索引（例如，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Much of machine learning is devoted to unrealistic, sterile settings where all relevant information for a prediction task has been pre-recorded and fed to one’s algorithm. This is incongruous with many real-world problems where predictions and decisions must be made with incomplete and muddied information. Moreover, the current machine learning paradigm is unprepared for the automated future where algorithmic agents are not just passively given information, but instead are able to actively obtain information from their environment as they make decisions. This project will extend past the current paradigm to develop a ‘machine detective’, a system that is capable of reasoning with incomplete instances and interacting with the environment to obtain new information, or new clues, on-the-fly as it is making decisions. The project shall not only increase machines’ predictive abilities in domains like health-care where recorded data is abounding with missing values, but shall also enable more efficient and robust predictions in interactive domains like computerized adaptive testing for education assessment and customer-service/troubleshooting chatbots where the machine can interact with a user (or the environment) to obtain new and relevant information.The work stemming from this award will serve as the underpinnings to intelligent agents that reason robustly about their decision-making process and weigh the cost of an incorrect prediction (e.g., a false positive or false negative) and the cost (e.g., in time, risk, or money) of obtaining additional information. This extends the typical paradigm in machine learning to give machines the ability to sequentially query for unobserved features to make more certain predictions through the following aims. First, the project develops methods that may infer with partially observed instances through novel generative models that learn conditional dependencies among features. Second, the learned dependencies are used as a foundation to learn non-greedy policies for acquiring informative unobserved features of a particular instance on hand. Lastly, the project builds models that may acquire data over a spatial-temporal domain, where features are indexed by spatial coordinates (e.g., when collecting information in the field), or are indexed by time (e.g., when collecting from sensors at specific moments).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.