We consider the problem of learning structured causal models from observational data. In
this work, we use causal Bayesian networks to represent causal relationships among model …

Probabilistic programming languages are used for developing statistical models. They
typically consist of two components: a specification of a stochastic process (the prior) and a …

This paper aims to study the protection of data privacy in news crowdfunding in the era of
artificial intelligence. This paper respectively quotes the encryption algorithm of artificial …

We propose a practical integration of logical state abstraction with AIXI, a Bayesian
optimality notion for reinforcement learning agents, to significantly expand the model class …

We consider the problem of Approximate Dynamic Programming in relational domains.
Inspired by the success of fitted Q-learning methods in propositional settings, we develop …

Abstract Despite its successes, Deep Reinforcement Learning (DRL) yields non-
interpretable policies. Moreover, since DRL does not exploit symbolic relational …

This paper presents a revision of Real Logic and its implementation with Logic Tensor
Networks and its application to Semantic Image Interpretation. Real Logic is a framework …

This paper aims to study the fault diagnosis method of the mechanical hydraulic system
based on artificial intelligence dynamic monitoring. According to the characteristics of …

Real-world datasets are dirty and contain many errors, such as violations of integrity
constraints and entity duplicates. Learning over dirty databases may result in inaccurate …

Recently there are great efforts on leveraging machine learning and logical reasoning. Many
approaches start from a given knowledge base, and then try to utilize the knowledge to help …