Neural-Symbolic Learning and Reasoning
Papers from the 2012 AAAI Workshop
Artur d'Avila Garcez, Pascal Hitzler, Luis C. Lamb, Workshop Cochairs
Artificial Intelligence (AI) researchers continue to face huge challenges in their quest to develop truly intelligent systems. The recent developments in the area of neural-symbolic integration offer an opportunity to combine symbolic AI and robust neural computation to help tackle some of these challenges. This workshop invited authors to discuss the representation of symbolic knowledge by subsymbolic systems; integrated neural-symbolic approaches to machine learning; extraction of symbolic knowledge from trained neural networks; integrated neural-symbolic approaches to human and logical reasoning; cognitive and biologically-inspired neural-symbolic agents; integration of logic and probabilities in neural networks; structured learning and relational learning in neural networks; and applications in robotics, simulation, fraud prevention, semantic web, software engineering, fault diagnosis, verification and validation, bioinformatics, visual intelligence, and so on.