Back to Strong AI? A Unifying Language for Deep Reasoning   March 4 , 2015, 11AM

302- 308

 

Abstract:

 

 Knowledge Representation and reasoning is at the heart of AI, in which balancing expressive representation and efficient reasoning is a key challenge. I will introduce Answer Set Programming (ASP), which has become a major knowledge representation method that has been applied to several areas in AI including planning, diagnosis, information integration, and bioinformatics. Wide applications of ASP motivated various extensions to its language and implementations, including integrations with constraint processing, satisfiability modulo theories, fuzzy logic, and probabilistic graphical models. I will present an overview of answer set programming and in particular a recent extension involving methods from statistical relational learning, which I expect to contribute towards the integration of knowledge representation and machine learning.

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