Symbolic Logic and Logic Processing 

This chapter deals with an introduction to logic and knowledge. Logic plays an important role in the foundation of computer science and has two important roles to play in this area.

Artificial Intelligence (AI) is usually defined as the science of making computers do things that require intelligence when same things are done by humans. AI has some success in limited, or simplified, domains. In this chapter, we will learn about AI, its fields, applications etc. starting from the very basic term Intelligence.

Search is inherent to the problems and methods of artificial intelligence (AI). That is because AI problems are intrinsically complex, efforts to solve problems with computers which humans can routinely solve by employing innate cognitive abilities, pattern recognition, perception and expe- rience, invariably must turn to considerations of search. So, searching is one of the basic opera- tional task that best characterise artificial intelligence programs. Problems are defined in terms of states and their solutions correspond to goal states. Solving a problem requires to search through the different states until one or more goal states are found.

The Oxford English Dictionary defines knowledge as intellectual acquaintance with, or perception of, fact or truth. A representation is a way of describing certain information so that any reasoning system can adopt it for inferencing purposes. Knowledge Representation (KR) is the study of vari- ous ways in which knowledge can be picturized and how effectively it resembles the representa- tion of knowledge in human brain.

The oldest and simplest type of formal logic is the syllogism. Propositional logic, also called propositional calculus, statement calculus or sentential calculus or boolean logic, is a symbolic logic for manipulating propositions. It deals with manipulation of logical variables, which repre- sent propositions. The general meaning of calculus is a special system for manipulating symbols.

To a large extent, the way in which we organize information available to and generated by our intelligent agent will be dictated by the type of problem we are addressing. Often the best ways of representing knowledge for particular techniques are known. However, as with the problem of how to search, we need a lot of flexibility in the way we represent information. Therefore, it is worth looking at four general schemes for representing knowledge, namely Logic, Semantic Net- works, Production rules and Frames. Knowledge representation continues to be a much-researched topic in Al because of the realization early on that how information is arranged can often make or break an AI application. In the last chapter we discussed logic presentation in detail. Here, we will consider other KR techniques.

PROGRAMMING LANGUAGES IN ARTIFICIAL INTELLIGENCE (AI) are the major tool for exploring and building computer programs that can be used to simulate intelligent processes such as learning and reasoning. Although in the early days of computer language design the main use of computers was for performing calculations with numbers, it was also found out soon that strings of bits could represent not only numbers but also features of arbitrary objects. Operations on such features or SYMBOLS could be used to represent rules for creating, relating or manipulating symbols. It led to the notion of symbolic computation as an appropriate means for defining algorithms that processed information of any type, and thus can be used for simulating human intelligence.

This chapter presents an introduction to logic programming. It imparts a great help to students to learn about the history of logic programming. This chapter then introduces the basics of prolog which includes characteristics, varieties, interpreters and compilers and presents a basic program structure for prolog.

This chapter deals with syntax & semantics of Prolog programs. It will also uncover the Prolog’s features, basic Prolog Program, how it answers and executes queries. Then, various abstract data types of Prolog will be introduced.

In this chapter we will learn about operators, arithmetic and lists in Prolog. The three constitutes the most important portion of Prolog. Lists are considered as right hand of Prolog. The use of accumulators in lists will also be covered in this chapter. Various list manipulation predicates have been introduced as example to make clear the concept of lists. So, now we will start with operators in Prolog.

In this chapter, we will learn more on how Prolog resolves queries. We will also introduce a control mechanism (cuts) that allows for more efficient implementations. Besides conjunction we shall introduce negation and disjunction.

In this chapter we will learn how prolog communicates with files, how to process files, manipulate characters and a number of built-in predicates.

Prolog is the most popular logic programming language. Prolog is based on Horn Clause Logic and its semantics are based on the resolution + unification mechanisms.

When speaking about Prolog programs and the way goals are executed it should have become clear that there is a very strong connection between logic and Prolog. Not only is Prolog the program- ming language that is probably best suited for implementing applications that involve logical reasoning, but Prolog’s query resolution process itself is also based on a logical deduction system.

The inference mechanism which is the basis of most logic programming systems is called Resolution Principle, introduced by J. A. Robinson in mid-sixties. SLD-Resolution Principle is specialization of this rule that applies to definite programs. This principle makes possible to draw correct conclusions from the program and provides a foundation for logically sound operational semantics of definite programs. It also provides guideline for implementing interpreters and compilers for logic programming languages.

In this chapter we will learn various search and sorting techniques of Prolog. This is very important from the AI perspective as both searching and sorting is very important in AI.

Next we turn to more complex examples of Prolog programming. Prolog was originally invented as a programming language to write natural language applications, and thus Prolog is a very elegant language for expressing grammars. Prolog even has a built-in syntax especially created for writing grammars.

In this chapter we will learn above various coding standards which are required not only for Prolog but necessary for all languages. We will also learn about debugging and tracing in Prolog.

Prolog, due to its origin in automated theorem proving, is an unusual programming language. It leads to a different style of programming and a different view of programming. A number of elegant Prolog programs presented here speak for themselves. Both through the development of a programming methodology and ingenious implementations a great care was taken to overcome possible sources of inefficiency. On the programming level we already discussed cut and the differ- ence lists.

As complexity rises, precise statements lose meaning & meaningful statements lose precisions. —Lotfi A Zadeh. Fuzzy logic is a branch of logic specially designed for representing knowledge and human reason- ing so that it is amenable to processing by a computer. Thus, it is applicable in the fields of AI, knowledge; Engineering and Expert System