The field of probabilistic logic programming (PLP) focuses on integrating probabilistic
models into programming languages based on logic. Over the past 30 years, numerous …

We propose probabilistic logic factored Markov decision processes (PL-fMDPs) as a
behavior selection scheme for self-driving cars. Probabilistic logic combines logic …

Rule-based strategies and probability models are among the most successful techniques for
selecting driving behaviors of self-driving cars. However, there is still the need to explore the …

We study languages that specify Markov Decision Processes with Imprecise Probabilities
(MDPIPs) by mixing probabilities and logic programming. We propose a novel language that …

Deep Learning has achieved remarkable success in a range of complex perception tasks,
games, and other real-world applications. At a high level, it can be argued that the main …

The journey captured by this dissertation centers around knowledge compilation, model
counting, and their role within state-of-the-art inference algorithms for probabilistic logic …

We present a comparative study of probabilistic logic factored Markov decision processes
(PL-fMDPs), classification and regression trees (CART), and multilayer perceptrons (MLPs) …

In this paper, we introduce a probabilistic dynamic epistemic logical framework that can be
applied for reasoning and verifying conformant probabilistic plans in a single agent setting …

We propose counterfactual reasoning through probabilistic logic twin networks (PLTNs) to
prevent collisions in self-driving cars. The basis of a PLTNs is a causal Bayesian network …

A two-stage scheme to learn MDP-ProbLog programs for self-driving cars is proposed. In a
first stage, the transition and reward functions will be learned from simulated driving …