The concept of "probabilistic logic" known in artificial intelligence needs a more thorough substantiation. A new approach to constructing probabilistic logic based on the N-tuple algebra developed by the author is proposed. A brief introduction is given to the N-tuple algebra and its properties that provide efficient paralleling of algorithms for solving problems of logical analysis of systems in computer implementation are generalized. Methods for solving direct and inverse problems of probabilistic simulation of logical systems are considered.
In ITs, analysis of heterogeneous information often necessitates unification of presentation forms and processing procedures for such data. To solve this problem, one needs a universal structure, which allows reducing various data and knowledge models to a single mathematical model with unified analysis methods. Such a universal structure is the relation, which is mainly associated with relational algebra. However, relations can model as different, at first glance, mathematical objects as graphs, networks, artificial intelligence structures, predicates, logical formulas, etc. Representation and analysis of such structures and models requires for more expressive means and methods than relational algebra provides. So, with a view to developing a general theory of relations, the authors propose n-tuple algebra (NTA) that allows for formalizing a wide set of logical problems (deductive, abductive, and modified reasoning; modeling uncertainties; and so on). This paper considers matters of metrization and clustering for NTA objects with ordered domains of attributes.
This chapter examines the usage potential of n-tuple algebra (NTA) developed by the authors as a theoretical generalization of structures and methods applied in intelligence systems. NTA supports formalization of a wide set of logical problems (abductive and modified conclusions, modelling graphs, semantic networks, expert rules, etc.). This chapter mostly focuses on implementation of logical inference and defeasible reasoning by means of NTA. Logical inference procedures in NTA can include, besides the known logical calculus methods, new algebraic methods for checking correctness of a consequence or for finding corollaries to a given axiom system. Inference methods consider (above feasibility of certain substitutions) inner structure of knowledge to be processed, thus providing faster solving of standard logical analysis tasks. Matrix properties of NTA objects allow decreasing the complexity of intellectual procedures. As for making databases more intelligent, NTA can be considered as an extension of relational algebra to knowledge processing.
Information technologies for analysis and processing heterogeneous data often face the necessity to unify representation of such data. To solve this problem, it seems reasonable to search for a universal structure that would allow for reducing different formats of data and knowledge to a single mathematical model with unitized manipulation methods. The concept of relation looks very prospective in this sense. So, with a view to developing a general theory of relations, the authors propose n-tuple algebra (NTA) developed as a theoretical generalization of structures and methods applicable in intelligence systems. NTA allows for formalizing a wide set of logical problems (deductive, abductive and modified reasoning, modeling uncertainties, and so on).
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