Connecting Width and Structure in Knowledge Compilation (Extended Version)
Antoine Amarilli, Mika\"el Monet, Pierre Senellart

TL;DR
This paper establishes a direct connection between the width parameters of knowledge compilation circuits and the size of their structured representations, providing both constructive upper bounds and exponential lower bounds.
Contribution
It introduces a linear-time, singly exponential upper bound for converting bounded-treewidth circuits to structured forms and proves exponential lower bounds for monotone DNF/CNF formulas, linking width to compilation complexity.
Findings
Bounded-treewidth circuits can be converted to d-SDNNF in linear time.
Exponential lower bounds for d-SDNNF and OBDD sizes based on width parameters.
Compilation complexity is characterized by pathwidth and treewidth for DNF and CNF formulas.
Abstract
Several query evaluation tasks can be done via knowledge compilation: the query result is compiled as a lineage circuit from which the answer can be determined. For such tasks, it is important to leverage some width parameters of the circuit, such as bounded treewidth or pathwidth, to convert the circuit to structured classes, e.g., deterministic structured NNFs (d-SDNNFs) or OBDDs. In this work, we show how to connect the width of circuits to the size of their structured representation, through upper and lower bounds. For the upper bound, we show how bounded-treewidth circuits can be converted to a d-SDNNF, in time linear in the circuit size. Our bound, unlike existing results, is constructive and only singly exponential in the treewidth. We show a related lower bound on monotone DNF or CNF formulas, assuming a constant bound on the arity (size of clauses) and degree (number of…
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Taxonomy
TopicsMachine Learning and Algorithms · Topic Modeling · Natural Language Processing Techniques
