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.
@article{arxiv.2502.09208,
title = {Autonomous Task Completion Based on Goal-directed Answer Set Programming},
author = {Alexis R. Tudor},
journal= {arXiv preprint arXiv:2502.09208},
year = {2025}
}