Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields,
including pattern recognition, robotics, recommendation systems, and gaming. Similarly …

Abstract Neuro-Symbolic Artificial Intelligence–the combination of symbolic methods with
methods that are based on artificial neural networks–has a long-standing history. In this …

Although deep reinforcement learning has become a promising machine learning approach
for sequential decision-making problems, it is still not mature enough for high-stake domains …

It has been recently shown that general policies for many classical planning domains can be
expressed and learned in terms of a pool of features defined from the domain predicates …

We consider the problem of learning generalized policies for classical planning domains
using graph neural networks from small instances represented in lifted STRIPS. The …

While reinforcement learning methods have delivered remarkable results in a number of
settings, generalization, ie, the ability to produce policies that generalize in a reliable and …

Neural-symbolic computing (NeSy), which pursues the integration of the symbolic and
statistical paradigms of cognition, has been an active research area of Artificial Intelligence …

Generalized planning is concerned with the computation of general policies that solve
multiple instances of a planning domain all at once. It has been recently shown that these …

Reinforcement Learning (RL), bolstered by the expressive capabilities of Deep Neural
Networks (DNNs) for function approximation, has demonstrated considerable success in …

We present three novel graph representations of planning tasks suitable for learning domain-
independent heuristics using Graph Neural Networks (GNNs) to guide search. In particular …