Autonomous Task Completion Based on Goal-directed Answer Set Programming

Abstract

Task planning for autonomous agents has typically been done using deep learning models and simulation-based reinforcement learning. This research proposes combining inductive learning techniques with goal-directed answer set programming to increase the explainability and reliability of systems for task breakdown and completion. Preliminary research has led to the creation of a Python harness that utilizes s(CASP) to solve task problems in a computationally efficient way. Although this research is in the early stages, we are exploring solutions to complex problems in simulated task completion.

arXiv commentIn Proceedings ICLP 2024, arXiv:2502.08453

References in corpus (2)

• Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents
• Reliable Natural Language Understanding with Large Language Models and Answer Set Programming