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arXiv · 2502.09208

Autonomous Task Completion Based on Goal-directed Answer Set Programming

Alexis R. Tudor
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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.

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Alexis R. Tudor. 2025-02-13. Autonomous Task Completion Based on Goal-directed Answer Set Programming. https://doi.org/10.4204/eptcs.416.39

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