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 …

Symbolic planning and reasoning are powerful tools for robots tackling complex tasks.
However, the need to manually design the symbols restrict their applicability, especially for …

Embodied agents face significant challenges when tasked with performing actions in diverse
environments, particularly in generalizing across object types and executing suitable actions …

Hand-crafted, logic-based state and action representations have been widely used to
overcome the intractable computational complexity of long-horizon robot planning problems …

General-purpose agents require fine-grained controls and rich sensory inputs to perform a
wide range of tasks. However, this complexity often leads to intractable decision-making …

Goal Recognition is the task of inferring an agent's intentions from a set of observations.
Existing recognition approaches have made considerable advances in domains such as …

In this letter, we propose and realize a new deep learning architecture for discovering
symbolic representations for objects and their relations based on the self-supervised …

In this paper, we propose a concept learning architecture that enables a robot to build
symbols through self-exploration by interacting with a varying number of objects. Our aim is …

Abstract Model-based planners rely on action models to describe available actions in terms
of their preconditions and effects. Nonetheless, manually encoding such models is …

Automated decision-making is a fundamental topic that spans multiple sub-disciplines in AI:
reinforcement learning (RL), AI planning (AP), foundation models, and operations research …