Synthesis of timeline-based planning strategies avoiding determinization
This work addresses the computational efficiency issue in timeline-based planning for AI and automated systems, representing an incremental improvement by focusing on a specific fragment to enable direct strategy synthesis.
The paper tackles the problem of synthesizing planning strategies in qualitative timeline-based planning by identifying a fragment that can be directly mapped to deterministic finite automata, avoiding the costly determinization step required with nondeterministic automata, and they also identify a maximal subset of Allen's relations fitting into this deterministic fragment.
Qualitative timeline-based planning models domains as sets of independent, but interacting, components whose behaviors over time, the timelines, are governed by sets of qualitative temporal constraints (ordering relations), called synchronization rules. Its plan-existence problem has been shown to be PSPACE-complete; in particular, PSPACE-membership has been proved via reduction to the nonemptiness problem for nondeterministic finite automata. However, nondeterministic automata cannot be directly used to synthesize planning strategies as a costly determinization step is needed. In this paper, we identify a fragment of qualitative timeline-based planning whose plan-existence problem can be directly mapped into the nonemptiness problem of deterministic finite automata, which can then synthesize strategies. In addition, we identify a maximal subset of Allen's relations that fits into such a deterministic fragment.