Work

Knowledge-Based Probabilistic Logic Learning

Year: 2015

Type: conference-paper

Abstract: Advice giving has been long explored in artificial intelligence to build robust learning algorithms. We consider advice giving in relational domains where the noise is systematic. The advice is provided as logical statements that are then explicitly considered by the learning algorithm at every update. Our empirical evidence proves that human advice can effectively accelerate learning in noisy structured domains where so far humans have been merely used as labelers or as designers of initial structure of the model.

Source: Proceedings of the AAAI Conference on Artificial Intelligence

DOI: 10.1609/aaai.v29i1.9690

Authors: Phillip Odom, Tushar Khot, Reid Porter, Sriraam Natarajan

Institutions: Indiana University, University of Wisconsin System, Los Alamos National Laboratory

Language: English

Topic: Bayesian Modeling and Causal Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funders: Defense Advanced Research Projects Agency, Army Research Office, Air Force Research Laboratory