Logic and Knowledge Representation: Introduction for Systems Analysts

Effective analysis, design and development of present-day computer-based information systems requires a firm grasp of mathematical reasoning. These systems variously encompass relational database logic programming languages, knowledge-based and expert systems, and development tools, all of which require the application of logic and mathematical modelling for their proper derivation of utilization. The trick is to present these techniques in the relevant context and establish their actual usefulness and means of deployment. This book therefore introduces the need for logic, explains the many and varied tools employed in formal logic, and then illustrates how they are manipulated in the modelling of rules, data and particularly knowledge representation required as the elements of a full system. The whole treatment is illustrated with examples and is concerned with comprehensibility and relevance rather than with pure rigour.

Table of Contents:
Part 1 The logical systems analyst: objectives; the logical communicator; the rationale of data structure; what's under the bonnet?. Part 2 Two-valued logics - a simple start: the story so far; sample two-valued systems; the common connectives - and, or, not; an introduction to logical inference; the algebraic representation of logic; tabular and graphic representations of logic; moving between representations - an example. Part 3 Some basic rules of logic: elementary manipulation procedures; algebraic simplification of expressions; the algebra of inference; other connectives; tautology and contradiction. Part 4 Logical mapping: the techniques and tools of systems analysis; K-maps as analytical techniques; decision tables as analytical techniques; tableaux for logic; tree-structured logic charts; networks, graphs and matrices. Part 5 Sets, lists and relations: the meaning of sets; the logic of sets; the quantification and enumeration of sets; lists; relations. Part 6 Decision logic: representations of decision logic; decision tables in use; decision trees in use. Part 7 Logical reasoning: inference and argument; rules of inference; proofs and methods of proving; clausal forms. Part 8 Predicate logic: formulating predicate logic; working with predicate logic; some practical considerations; prolog M - a language for predicate logic. Part 9 Knowledge representation: understanding what is meant by knowledge representation; data modelling; the frames approach; rule-based approaches; semantic networks. Part 10 Further knowledge representation demands upon logic: the problems with time; the problems with uncertainty; the problems with truth.