I will discuss a number of roles for logic in AI today that are being pursued by my group, which include probabilistic reasoning, machine learning and explaining AI systems. For probabilistic reasoning, I will show how probabilistic graphical models can be compiled into tractable Boolean circuits, allowing probabilistic reasoning to be conducted efficiently using weighted model counting.For machine learning, I will show how one can learn from a combination of data and knowledge expressed in logical form, where symbolic manipulations end up playing the key role.
Finally, I will show how some common machine learning classifiers over discrete features can be compiled intro tractable Boolean circuits that have the same input-output behavior, allowing one to symbolically explain the decisions made by these numeric classifiers in addition to reasoning formally about their behavior.
Professor Adnan Darwiche is a professor and former chairman of the computer science department at UCLA. He directs the Automated Reasoning Group, which focuses on symbolic and probabilistic reasoning and their applications to machine learning. Professor Darwiche is Fellow of AAAI and ACM. He is a former editor-in-chief of the Journal of Artificial Intelligence Research (JAIR) and author of “Modeling and Reasoning with Bayesian Networks,” by Cambridge University Press.
The event will be followed by an informal drinks reception in Huxley Room 436 (DoC Common Room).