A Declarative Learning Based Programming Framework for Integration of Domain Knowledge and Statistical Learning
Abstract
We propose a framework and a novel underlying formalism for integrating domain knowledge and statistical learning. Our ambitious objective is to find a breakthrough and novel abstractions for designing AI complex systems based on data and knowledge representation rather than based on underlying computational units. While our approach is general, we envision its application in the context of automated decision support, where it is crucial to synthesize data and context in a meaningful way and to minimize retraining and reconfiguration efforts required by a rapidly evolving environment. The targeted abstraction layer will be high-level and intuitive for the domain experts to interact with, allowing them to design intelligent models rapidly without worrying about the underlying computations. The framework allows to seamlessly integrate knowledge about the world and goals expressed via declarative KR models and procedural knowledge about tasks and missions. Our proposed technique directly exploits domain knowledge to guide the training of the statistical models. Moreover, our models make consistent and informed predictions based on the domain knowledge at the decision-making phase. Our highly expressive formalism can handle uncertainty in various forms including soft constraints, preferences or use partial information while addressing scalability of computations. Knowledge is automatically used to identify data of interest, to generate learning examples and configurations, extract features, and to guide the training by enforcing the consistency of the outputs with the available knowledge or perform reasoning to generate the target outputs. Exploiting domain knowledge will help to learn novel concepts when there is little to no supervision.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Nov 26, 2019
- Source ID
- N000142012005
Entities
People
- Parisa Kordjamshidi
Organizations
- Michigan State University
- Office of Naval Research
- United States Navy