Dislog - a system for reasoning in disjunctive deductive databases

DisLoG is a system for reasoning in disjunctive -deductive databases. It seeks to combine features of disjunctive logic programming, such as the support for incomplete information, with those of deductive databases, such as all-result inference capabilities. Severa} basic operators are provided for logical and non-monotonic reasoning: The logical consequence operator derives all 'Iogically implied disjunctive clauses from a disjunctive logic program. The nonmonotonic operators are semantically founded on generalizations of the wellknown closed-world-assumption. Reasoning in disjunctive deductive databases is very complex, even for small examples. Many different optimization techniques are integrated in D1sLoG to speed up the application performance. The main techniques rely on a clause tree data structure allowing for an efficient and transparent evaluation_ '¡he operators of D1sLoG can be loaded from a library into a PROLOG application. D1s L0G itself is implemented as a meta-interpreter in SICSTUS-PR0L0G

DisLog - A System for Reasoning in Disjunctive Deductive Databases

DisLog is a system for reasoning in disjunctive deductive databases. It seeks to combine features of disjunctive logic programming, such as the support for incomplete information, with those of deductive databases, such as all--result inference capabilities. Several basic operators are provided for logical and non--monotonic reasoning: The logical consequence operator derives all logically implied disjunctive clauses from a disjunctive logic program. The non-- monotonic operators are semantically founded on generalizations of the well-- known closed--world--assumption. Reasoning in disjunctive deductive databases is very complex, even for small examples. Many different optimization techniques are integrated in DisLog to speed up the application performance. The main techniques rely on a clause tree data structure allowing for an efficient and transparent evaluation. The operators of DisLog can be loaded from a library into a Prolog application. DisLog itself is implemented as a meta--i..

Increased robustness of Bayesian networks through probability intervals

AbstractWe present an extension of Bayesian networks to probability intervals, aiming at a more realistic and flexible modeling of applications with uncertain and imprecise knowledge. Within the logical framework of causal programs we provide a model-theoretic foundation for a formal treatment of consistency and of logical consequences. A set of local inference rules is developed, which is proved to be sound and—in the absence of loops—also to be complete; i.e., tightest probability bounds can be computed incrementally by bounds propagation. These inference rules can be evaluated very efficiently in linear time and space. An important feature of this approach is that sensitivity analyses can be carried out systematically, unveiling portions of the network that are prone to chaotic behavior. Such investigations can be employed for improving network design towards more robust and reliable decision analysis

On Cautious Probabilistic Inference and Default Detachment

Conditional probabilities are one promising and widely used approach to model uncertainty in information systems. This paper discusses the DUCK-calculus, which is founded on the cautious approach to uncertain probabilistic inference. Based on a set of sound inference rules derived probabilistic information is gained by local bounds propagation techniques. Precision being always a central point of criticism to such systems, we demonstrate that DUCK need not necessarily suffer from these problems. We can show that the popular Bayesian networks are subsumed by DUCK, implying that precise probabilities can be deduced by local propagation techniques, even in the multiply connected case. A comparative study with INFERNO and with inference techniques based on global operation-research techniques yields quite favorable results for our approach. Since conditional probabilities are also suited to model nonmonotonic situations by considering different contexts, we investigate the problems of maximal and relevant contexts, needed to draw default conclusions about individuals

The Differential Fixpoint Operator with Subsumption

Declarative languages for deductive and object-oriented databases require some high-level mechanism for specifying semantic control knowledge. This paper proposes user-supplied subsumption information as a paradigm to specify desired, prefered or useful deductions at the meta level. For this purpose we augment logic programming by subsumption relations and succeed to extend the classical theorems for least models, fixpoints and bottom-up evaluation accordingly. Moreover, we can provide a differential fixpoint operator for efficient query evaluation. This operator discards subsumed tuples on the fly. We also exemplify the ease of use of this programming methodology. In particular, we demonstrate how heuristic AI search procedures can be integrated into logic programming in this way