CAREER: Towards a New Synthesis of Statistical Learning and Logical Reasoning

University Of California-Los Angeles, Los Angeles CA

Investigators

Abstract

Over the past decade, the field of artificial intelligence (AI) has evolved drastically, taking a more data-centric approach. The widespread success of machine learning raises the question of which AI tasks are amenable to pure learning, which tasks require classical symbolic reasoning, and whether we can benefit from a tighter integration of both approaches. The project studies this question, and concretely asks how ideas about automated reasoning and knowledge representation studied in traditional artificial intelligence are relevant to modern connectionist and statistical machine learning. It brings together expertise, techniques, insights, and strengths from several disparate fields that are usually studied in isolation: logical reasoning, probabilistic reasoning, knowledge representation, statistical learning, and deep learning. The unified perspective taken by this research has the potential to be transformative for the broader AI field and have a lasting impact on how we perceive the interaction between learning and reasoning. The research will make AI more effective by allow it to address a larger class of problems. More capable AI and machine learning methods will have significant scientific consequences, and broad impact in all segments of society, including healthcare, manufacturing, commerce, finance, entertainment, among others. The project helps convey this new understanding through integrated research and educational activities. More specifically, current reasoning paradigms are not able to fully exploit available data and are often brittle, while learning paradigms are often incapable of answering questions beyond the one task they were explicitly trained for. Finding a synthesis of learning and reasoning allows for learned representations that can be reasoned about, and even using reasoning and logic during learning, to enforce basic invariants and knowledge of the world. This project is structured along three research thrusts. The first thrust is to develop probabilistic and logistic circuits as a new machine learning model that simultaneously easy to learn, expressive, and has elegant properties that allow for tractable reasoning and learning. The second thrust looks at more advanced reasoning tasks about classifiers and generative world models, such as taking expected predictions when features are missing, or reasoning about sufficient conditions to explain classifiers. The ability to reason about classifiers, specifically, builds more trust in our AI systems as they are deployed, and helps to better understand their limitations. The third thrust studies how logical reasoning about continuous variables and arithmetic is used for probabilistic reasoning and statistical learning. 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.