AIOct 22, 2018

Planification en temps réel avec agenda de buts et sauts

arXiv:1810.10907v1
Originality Incremental advance
AI Analysis

This addresses real-time planning efficiency for classical planning problems, but it is incremental as it builds on existing methods.

The paper tackled real-time planning by introducing agenda-driven planning and committed jumps, which together made the planner several orders of magnitude faster and produced shorter solution plans.

In the context of real-time planning, this paper investigates the contributions of two enhancements for selecting actions. First, the agenda-driven planning enhancement ranks relevant atomic goals and solves them incrementally in a best-first manner. Second, the committed jump enhancement commits a sequence of actions to be executed at the following time steps. To assess these two enhancements, we developed a real-time planning algorithm in which action selection can be driven by a goal-agenda, and committed jumps can be done. Experimental results, performed on classical planning problems, show that agenda-planning and committed jumps are clear advantages in the real-time context. Used simultaneously, they enable the planner to be several orders of magnitude faster and solution plans to be shorter.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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