Human-Guided Learning for Probabilistic Logic Models

Learning Probabilistic Logic Models with Human Advice

We consider the problem of interactive machine learning for rich, structured and noisy domains. We present a recently successful learning algorithm and provide several extensions for incorporating rich, high level, human feedback. We then discuss some open problems in this area.

Probabilistic Relational Learning of Human Behavior Models

In this paper we introduce a probabilistic relational framework for automated human behavior modeling. This is achieved by observing a human successfully achieving given goals. The proposed modeling algorithm uses commonsense background knowledge analytically to facilitate modeling of complicated human behavior. At the same time it is grounded in physical observations and takes noisy sensory da...

Probabilistic-Logic Models: Reasoning and Learning with Relational Structures

Factored Models for Probabilistic Modal Logic

Modal logic represents knowledge that agents have about other agents’ knowledge. Probabilistic modal logic further captures probabilistic beliefs about probabilistic beliefs. Models in those logics are useful for understanding and decision making in conversations, bargaining situations, and competitions. Unfortunately, probabilistic modal structures are impractical for large real-world applicat...

Knowledge-Based Probabilistic Logic Learning

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 ...