Human-Guided Learning for Probabilistic Logic Models
By Phillip Odom, Sriraam Natarajan. 2018
20 citations recorded by the source corpus.
Advice-giving has been long explored in the artificial intelligence community to build robust learning algorithms when the data is noisy, incorrect or even insufficient. While logic based systems were effectively used in building expert systems, the role of the human has been restricted to being a “mere labeler” in recent times. We hypothesize and demonstrate that probabilistic logic can provide an effective and natural way for the expert to specify domain advice. Specifically, we consider different types of advice-giving in relational domains where noise could arise due to systematic errors or class-imbalance inherent in the domains. The advice is provided as logical statements or privileged features that are thenexplicitly considered by an iterative learning algorithm at every update. Our empirical evidence shows 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 the (initial or final) structure of the model.
Subfields: Artificial Intelligence, Information Systems
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Kristian Kersting
- Stefano Teso
- Sriraam Natarajan
- Luc De Raedt
- Patrick Schramowski
- David Poole
- Christopher Morris
- Mathias Niepert
In Information Systems
Citations in the corpus
- Explanatory Interactive Machine Learning (2019, background): “Other interactive learning approaches such as coactive [33], active imitation [15], mixed-initiative interactive [5] and guided probabilistic learning [21] should be made explanatory.”