Effective Logic ComputationA powerful new approach to solving propositional logic problems in the design of expert systems Effective Logic Computation describes breakthrough mathematical methods for computation in propositional logic. Offering a highly robust and versatile alternative to the production rule- or neural net-based approaches commonly used in the design of expert systems, Dr. Truemper’s combinatorial decomposition-based approach has produced a compiler that uniquely yields solution algorithms for both logic satisfiability problems and logic minimization problems. Also unique to the compiler is computation of a performance guarantee for each solution algorithm. Effective Logic Computation provides detailed algorithms for all steps carried out by the compiler. Much of the mathematics described in this book has been implemented in the Leibniz System, a commercially available software system for logic programming and a leading tool for building expert systems. This book’s companion volume, Design of Intelligent Computer Systems, is in preparation and will offer detailed coverage of software implementation and use, including a complete version of the Leibniz System. Effective Logic Computation is an indispensable working resource for computer scientists and applied mathematicians involved in the design of logic programming software, researchers in artificial intelligence, and operations researchers. |
Contents
Introduction | 1 |
Basic Concepts | 22 |
Some Matroid Theory | 69 |
Copyright | |
17 other sections not shown
Common terms and phrases
2SAT Algorithm ANALYSIS 13.4.4 Algorithm SELECT TYPE Algorithm SOLVE analysis algorithm augmented sum B1 and B2 bipartite graph Boolean closed bound Chapter clause closed subregion decomposition closed sum CNF system column index set column node column scaling column submatrix components Bı Computer contains corresponding declare define deletion di³ given matrix H₁ Heuristic hidden nearly negative inequality integer integer programs k-separation Lemma linear sum matrix over IB matrix/vector pairs matroid method minimal MINSAT instances monotone decomposition monotone sum nonempty nonzero entries optimal Output P₁ partition polynomial Proof propositional logic range(A resp row index set row node SAT and MINSAT SAT central SAT instance SAT or MINSAT SAT semicentral satisfiability problem Section semicentral classes solution algorithm solution vector solve the SAT Step subgraph subrange subrange(A subroutine subset subvector SUM SAT Suppose Theorem variables vector Y₁