Accelerating combinatorial filter reduction through constraints [article]

Yulin Zhang, Hazhar Rahmani, Dylan A. Shell, Jason M. O'Kane
2020 arXiv   pre-print
Reduction of combinatorial filters involves compressing state representations that robots use. Such optimization arises in automating the construction of minimalist robots. But exact combinatorial filter reduction is an NP-complete problem and all current techniques are either inexact or formalized with exponentially many constraints. This paper proposes a new formalization needing only a polynomial number of constraints, and characterizes these constraints in three different forms: nonlinear,
more » ... inear, and conjunctive normal form. Empirical results show that constraints in conjunctive normal form capture the problem most effectively, leading to a method that outperforms the others. Further examination indicates that a substantial proportion of constraints remain inactive during iterative filter reduction. To leverage this observation, we introduce just-in-time generation of such constraints, which yields improvements in efficiency and has the potential to minimize large filters.
arXiv:2011.03471v1 fatcat:mevttxoxq5dz5nmids2il6rtke