Inference deductive

Deductive Inference Module Provides prediction of biological activity based on the rules stored in the knowledge base. 

Besides the statistical prediction of biological activity based on the rules, the deductive inference module provides explanation of predictions. It produces a three-dimensional graphic display of biophores found in the analyzed structures and allows superimposition of compounds with the same biophore. Visualization is performed via integration with the Insight II environment from Accekys Inc. [21], a molecular modeling environment with a powerful graphical interface. 

While causality can be conclusively established between a particular exposure as an entity and a particular disease as an entity, it is not possible to conclusively establish such a link between an individual exposure and a particular disease of a certain individual. It is possible, however, to deductively infer that a specific individual s illness more likely than not was caused by the specified exposure. Lagiou et al. (2005) describe several criteria for evaluating whether the disease in an individual could have been caused by a specified exposure. 

We have said that scientific systems consist of sets of hypotheses forming a deductive system, but what are deductive systems and how are they formulated It is useful to contrast deduction with the concept of induction which we introduced earlier. In a deductive inference we are given both a hypothesis considered... 

Premises P are used with a hypothesis H to infer a conclusion C which is compared with experimental evidence E. If C and E disagree, then the hypothesis is rejected, but if C and E agree, then the hypothesis is not rejected but it is also not proven. The hypothesis was previously accepted either because it is known to be true or because it is known to be reasonably true. The argument between inductive inference and deductive inference then rests upon the debate as to the nature of the way in which a hypothesis is thought to be confirmed, corroborated or made probable. 

Deductive inference is the process of obtaining new sentences (called theorems) from a set (called theory) of given sentences (called axioms), by application of rules of deductive inference. Theorems are also called logical consequences of the theory. We assume that the reader is familiar with deductive inference, and thus just repeat the basic definition. 

Note that deductive inference is always sound. Typical rules of deductive inference are modusponens, universal instantiation, resolution, mathematical induction, and so on. The branch of artificial intelligence research that is bent on automating deductive inference is called automated theorem proving. 

Since deductive inference is generally a familiar notion, we do not survey it here. We rather directly propose a taxonomy of the different ways of applying deductive inference to program synthesis from axiomatic specifications. Thus, Sections 2.2.1 to 2.2.3 respectively contain general introductions to transformational synthesis, proofs-as-programs synthesis, and schema-guided synthesis. 

In Chapter 2, we survey the use of deductive inference in automatic programming. Axiomatic specifications are expected to be complete and non-ambiguous, but are usually also quite lengthy and artificial. Deductive synthesis from axiomatic specifications can be classified into transformational synthesis, proofs-as-programs synthesis (or constructive synthesis), and schema-guided synthesis. We survey the achievements of deductive synthesis of LISP functions and Prolog predicates. 

In general causes and effect are observed or measured, and they are known, but the relationship translator between them is not known in many engineering, scientific, economic and social events. It is, therefore, very important to be able to identify some of the features of the relationship through deductions, inferences, linguistically reasoning and perhaps at the end by well-established and convenient mathematical deterministic or uncertainty methodologies. 

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