Drosophila melanogaster larvae were subjected to 10 generations of selection on 6% ethanol at 17, 25, and 30 degrees C. For each temperature there was a significant (P less than 0.01) increase in the frequency of the Adh isoallele. Controls with no ethanol showed no change in the frequency of the AdhF isoallele. Larvae subjected to stronger selection on 8% ethanol confirmed the results. When adults of various ages were subjected to 16 and 32 degrees C, the ADHF isoenzyme retained its twofold advantage in activity over ADHS regardless of the temperature. The same result was obtained with larvae at 16 and 35 degrees C. Although some effect of temperature was demonstrated, it was concluded that the effect was not strong enough for temperature to be a selective factor under the conditions studied. However, ethanol is a strong selective factor for laboratory populations.
Decomposable negation normal form (DNNF) was developed primarily for knowledge compilation. Formulas in DNNF are linkless, in negation normal form (NNF), and have the property that atoms are not shared across conjunctions. Full dissolvents are linkless NNF formulas that do not in general have the latter property. However, many of the applications of DNNF can be obtained with full dissolvents. Two additional methods-regular tableaux and semantic factoring-are shown to produce equivalent DNNF. A class of formulas is presented on which earlier DNNF conversion techniques are necessarily exponential; path dissolution and semantic factoring handle these formulas in linear time.
Abstract. A class of formulas called factored negation normal form is introduced. They are closely related to BDDs, but there is a DPLL-like tableau procedure for computing them that operates in PSPACE.Ordered factored negation normal form provides a canonical representation for any boolean function. Reduction strategies are developed that provide a unique reduced factored negation normal form. These compilation techniques work well with negated form as input, and it is shown that any logical formula can be translated into negated form in linear time.
The goal of knowledge compilation is to enable fast queries. Prior approaches had the goal of small (i.e., polynomial in the size of the initial knowledge bases) compiled knowledge bases. Typically, query-response time is linear, so that the efficiency of querying the compiled knowledge base depends on its size. In this paper, a target for knowledge compilation called the ri-trie is introduced; it has the property that even if they are large they nevertheless admit fast queries. Specifically, a query can be processed in time linear in the size of the query regardless of the size of the compiled knowledge base.
A graphical representation of quantifier-free predicate calculus formulas in negation normal form and a new rule of inference that employs this representation are introduced. The new rule, path resolution, is an amalgamation of resolution and Prawitz analysis. The goal in the design of path resolution is to retain some of the advantages of both Prawitz analysis and resolution methods, and yet to avoid to some extent their disadvantages.Path resolution allows Prawitz analysis of an arbitrary subgraph of the graph representing a formula. If such a subgraph is not large enough to demonstrate a contradiction, a path resolvent of the subgraph may be generated with respect to the entire graph. This generalizes the notions of large inference present in hyperresolution, clash-resolution, NC-resolution, and UR-resolution. A class of subgraphs is described for which deletion of some of the links resolved upon preserves the spanning property.
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