2019 AAAI AAAI 2019

Robustness Envelopes for Temporal Plans

Abstract

Abstract To achieve practical execution, planners must produce temporal plans with some degree of run-time adaptability. Such plans can be expressed as Simple Temporal Networks (STN), that constrain the timing of action activations, and implicitly represent the space of choices for the plan executor.A first problem is to verify that all the executor choices allowed by the STN plan will be successful, i.e. the plan is valid. An even more important problem is to assess the effect of discrepancies between the model used for planning and the execution environment.We propose an approach to compute the “robustness envelope” (i.e., alternative action durations or resource consumption rates) of a given STN plan, for which the plan remains valid. Plans can have boolean and numeric variables as well as discrete and continuous change. We leverage Satisfiability Modulo Theories (SMT) to make the approach formal and practical.

🚀 Conference Pioneer — AAAI 2019
🌉 Interdisciplinary Bridge — Artificial Intelligence and Mathematics & Optimization
🧭 Keyword Pioneer — simple temporal network
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Deep Learning, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics