Recently, a Symbolic Pattern Planning (SPP) approach was proposed for numeric planning where a pattern (i.e., a finite sequence of actions) suggests a causal order between actions. The pattern is then encoded in a SMT formula whose models correspond to valid plans. If the suggestion by the pattern is inaccurate and no valid plan can be found, the pattern is extended until it contains the causal order of actions in a valid plan, making the approach complete. In this paper, we extend the SPP approach to the temporal planning with Intermediate Conditions and Effects (ICEs) fragment, where $(i)$ actions are durative (and thus can overlap over time) and have conditions/effects which can be checked/applied at any time during an action's execution, and $(ii)$ one can specify plan's conditions/effects that must be checked/applied at specific times during the plan execution. Experimental results show that our SPP planner Patty $(i)$ outperforms all other planners in the literature in the majority of temporal domains without ICEs, $(ii)$ obtains comparable results with the SoTA search planner for ICS in literature domains with ICEs, and $(iii)$ outperforms the same planner in a novel domain based on a real-world application.

翻译：近期，一种符号模式规划（SPP）方法被提出用于数值规划，其中模式（即有限动作序列）暗示了动作间的因果顺序。该模式随后被编码为SMT公式，其模型对应于有效规划。若模式所暗示的因果顺序不准确且无法找到有效规划，则扩展该模式直至其包含有效规划中的动作因果顺序，从而保证方法的完备性。本文将该SPP方法扩展至具有中间条件与效应（ICEs）的时序规划片段，其中：$(i)$ 动作为持续型（可随时间重叠执行），且其条件/效应可在动作执行期间的任意时刻被检测/应用；$(ii)$ 可指定规划的条件/效应，并要求在规划执行过程中的特定时刻进行检测/应用。实验结果表明，我们的SPP规划器Patty：$(i)$ 在多数无ICEs的时序领域中优于现有文献中的所有其他规划器；$(ii)$ 在含ICEs的文献领域中获得与最先进的ICS搜索规划器相当的结果；$(iii)$ 在基于实际应用的新领域中优于该规划器。