Knowledge Representation and Reasoning: Integrating Symbolic and Neural Approaches
Papers from the 2015 AAAI Spring Symposium
Evgeniy Gabrilovich, Ramanathan Guha, Andrew McCallum, Kevin Murphy, Program Chair
Technical Report SS-15-03
Published by The AAAI Press, Palo Alto, California.
Early work on knowledge representation and inference, which was done in the AI community back in the 1980s, was primarily symbolic. Subsequently, symbolic approaches fell out of favor, and were largely supplanted by statistical methods. This symposium will try to close the gap between the two paradigms, and aim to formulate a new paradigm that is inspired by our current understanding of how humans solve these tasks. Both symbolic / structured approaches and distributed / statistical approaches have a long history, and both have strengths and weaknesses. For example, symbolic systems are able to represent and reason with crisp rules, and distributed systems are able to represent (and to a much lesser extent, reason with) fuzzy concepts. It is widely believed that "general" AI systems will need both forms of functionality. This dichotomy was widely debated during the first "connectionist revolution" in the 1980s. We feel the time is ripe to revisit this discussion, based on the development and wide availability of massive symbolic knowledge bases (for example, Freebase) on the one hand, and recent advances in deep learning on the other. While historically this research has been conducted in the computer science community, we would like to bridge between this work and the study of human cognition.