Similar to humans, RL agents use interactive learning to successfully obtain satisfactory
decision strategies. However, in many cases, it is desirable to learn directly from …

An intelligent agent interacting with the real world will encounter individual people, courses,
test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of …

Multi-agent systems (MASs) is an area of distributed artificial intelligence that emphasizes
the joint behaviors of agents with some degree of autonomy and the complexities arising …

The number of Electric Vehicle (EV) owners is expected to significantly increase in the near
future, since EVs are regarded as valuable assets both for transportation and energy storage …

In robotic domains, learning and planning are complicated by continuous state spaces,
continuous action spaces, and long task horizons. In this work, we address these challenges …

In the field of Sequential Decision Making (SDM), two paradigms have historically vied for
supremacy: Automated Planning (AP) and Reinforcement Learning (RL). In the spirit of …

This paper makes a first step toward the integration of two subfields of machine learning,
namely preference learning and reinforcement learning (RL). An important motivation for a …

We present PDDLGym, a framework that automatically constructs OpenAI Gym
environments from PDDL domains and problems. Observations and actions in PDDLGym …

We propose a novel relational state representation and an action-value function learning
algorithm that learns from planning experience for geometric task-and-motion planning …

The field of inductive logic programming (ILP) has made steady progress, since the first ILP
workshop in 1991, based on a balance of developments in theory, implementations and …