Mutex Graphs and Multicliques: Reducing Grounding Size for Planning
This addresses a bottleneck in domain-independent planning for AI applications, though it is incremental as it builds on existing mutex constraint work.
The paper tackles the problem of large mutex constraints overwhelming solver efficiency by proposing a graph-theoretic technique using multicliques for compact representation, applied to ASP planning, resulting in substantially smaller grounding sizes compared to prior SAT methods.
We present an approach to representing large sets of mutual exclusions, also known as mutexes or mutex constraints. These are the types of constraints that specify the exclusion of some properties, events, processes, and so on. They are ubiquitous in many areas of applications. The size of these constraints for a given problem can be overwhelming enough to present a bottleneck for the solving efficiency of the underlying solver. In this paper, we propose a novel graph-theoretic technique based on multicliques for a compact representation of mutex constraints and apply it to domain-independent planning in ASP. As computing a minimum multiclique covering from a mutex graph is NP-hard, we propose an efficient approximation algorithm for multiclique covering and show experimentally that it generates substantially smaller grounding size for mutex constraints in ASP than the previously known work in SAT.