Symmetry-Driven Decision Diagrams for Knowledge Compilation
Anicet Bart, Koriche Frédéric, Jean-Marie Lagniez, Pierre Marquis · Frontiers in artificial intelligence and applications · 2014
In this paper, symmetries are exploited for achieving significant space savings in a knowledge compilation perspective. More precisely, the languages FBDD and DDG of decision diagrams are extended to the languages Sym-FBDDX,Yand Sym-DDGX,Yof symmetry-driven decision diagrams, where X is a set of “symmetry-free” variables and Y is a set of “top” variables. Both the time efficiency and the space efficiency of Sym-FBDDX,Yand Sym-DDGX,Yare analyzed, in order to put those languages in the knowledge compilation map for propositional representations. It turns out that each of Sym-FBDDX,Yand Sym-DDGX,Ysatisfies CT (the model counting query). We prove that no propositional language over a set X∪Y of variables, satisfying both CO (the consistency query) and CD (the conditioning transformation), is at least as succinct as any of Sym-FBDDX,Yand Sym-DDGX,Yunless the polynomial hierarchy collapses. The price to be paid is that only a restricted form of conditioning and a restricted form of forgetting are offered by Sym-FBDDX,Yand Sym-DDGX,Y. Nevertheless, this proves sufficient for a number of applications, including configuration and planning. We describe a compiler targeting Sym-FBDDX,Yand Sym-DDGX,Yand give some experimental results on planning domains, highlighting the practical significance of these languages.