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Such patterns are codified by models of a two conjunctive form.
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Counting models for two conjunctive forms (2-CF), problem known as #2SAT, is a classic #P-complete problem.
The representation of the set of falsifying assignments of clauses via binary patterns has been useful in the design of algorithms for solving #FAL (counting the number of falsifying assignments of conjunctive forms (CF)).
The general idea is to be able to express a problem's formulation as a set of clauses or, equivalently, as a formula in conjunctive normal form (CNF), that is, as a conjunction of clauses.
A Conjunctive Normal Form (CNF) expression S consists of a conjunction (AND) of m clauses c1... c m.
We assume in the problem that a formula is represented in conjunctive normal form (CNF) (if it is the input of Algorithms 1 2) or in disjunctive normal form (DNF) (if it is the input of Algorithm 3).
In general, most SAT solvers are applied to Boolean decision problems that are expressed in conjunctive normal form (CNF).
Designing algorithms capable of efficiently constructing minimal models of Conjunctive Normal Form theories (CNFs) is an important task in AI.
The reasoning is done by implication tests on propositional formulas in conjunctive normal form (CNF) that capture the action sequence semantics.
One type of knowledge compilation occurs when the knowledge in question is represented by a Boolean formula in conjunctive normal form (CNF).
This paper describes an effective technique for relevant questioning in expert systems whose knowledge base is encoded in a propositional formula in conjunctive normal form.
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