learned from relational databases by iteratively optimizing a pseudo-likelihood measure. Optionally, additional clauses are learned using inductive logic programming techniques. Experiments with a real-world database and knowledge base in a university domain illustrate the promise of this approach.

This paper defines aggregates in the temporal query language TQuel and provides their rormal semantics in the tuple relational calculus. A rormal semantics (or Que! aggregates is defined in the process. Multiple aggregates; aggregates appearing in the where, when, valid, and as-or clauses; nested

When logic programming is based on the proof theory of intuitionistic logic, it is natural to allow implications in goals and in the bodies of clauses. Attempting to prove a goal of the form D ⊃ G from the context (set of formulas) Γ leads to an attempt to prove the goal G in the extended context Γ

. Constructions are form-function pairings, and the idea is that grammar is "constructions all the way down " (p. 18). In other words, clause-level syntactic constructions, phrases, collocations, words, and morphemes are all analyzed in a similar, "construction " fashion. In addition

This paper examines the relative performance of additive and multiplicative clause weighting schemes for propositional satisfiability testing. Starting with one of the most recently developed multiplicative algorithms (SAPS), an experimental study was constructed to isolate the effects

Efficient implementations of DPLL with the addition of clause learning are the fastest complete Boolean satisfiability solvers and can handle many significant real-world problems, such as verification, planning and design. Despite its importance, little is known of the ultimate strengths

Efficient implementations of DPLL with the addition of clause learning are the fastest complete satisfiability solvers and can handle many significant real-world problems, such as verification, planning, and design. Despite its importance, little is known of the ultimate strengths and limitations

based QBF solver that employs extended binary clause reasoning (hyper-binary resolution) to infer new binary clauses both before and during search. These binary clauses are used to discover additional forced literals, as well as to perform equality reduction. Both of these transformations simplify

The past decade has seen clause learning as the most successful algorithm for SAT instances arising from real-world applications. This practical success is accompanied by theoretical results showing clause learning as equivalent in power to resolution. There exist, however, problems

and suggest various settings how clause sharing and inprocessing can be combined. Our formalization models most of the re-cent implemented portfolio systems and we indicate possibilities to im-prove these. A particular improvement is a novel way to combine clause addition techniques – like blocked clause