Page 4 - DCAP310_INTRODUCTION_TO_ARTIFICIAL_INTELLIGENCE_AND_EXPERT_SYSTEMS

SYLLABUS
Introduction to Artificial Intelligence & Expert Systems

Objectives:
To enable the student to understand technicalities of intelligence.

To enable the student to understand techniques of capturing and generating knowledge.
To enable the student to understand knowledge representation methodologies.
To enable the student to learn Natural language processing.
To enable the student to learn Fuzzy Logic with their applications.

To enable the student to understand probabilistic reasoning.
To enable the student to learn technicalities of expert system.
To enable the student to understand Artificial intelligence language 'LISP' and 'Prolog'.

Sr. No. Description
1. Overview of AI: What is AI, Importance of AI, Early Work in AI, AI and Related Fields.
2. Knowledge: General Concepts, Introduction, Definition and Importance of Knowledge, Knowledge-based
Systems, Representation of Knowledge, Knowledge Organization, Knowledge Manipulation, Acquisition of
Knowledge.
3. LISP and Other AI Programming Languages: Introduction to LISP Syntax and Numeric Functions, Basic List
Manipulation Functions in LISP, Functions, Predicates, and Conditionals, Input, Outputs and Local Variables,
Iterations and Recursion, Property Lists and Arrays, PROLOG and Other AI Programming Languages.
4. Formalized Symbolic Logics: Introduction, Syntax and Semantics for Propositional Logic, Syntax and Semantics
for FOPL, Properties of WFFs, Conversion to Clausal Form, Inference Rules, The Resolution Principle,
Non-deductive Inference Methods, Representations using Rules Dealing with Inconsistencies and Uncertainties:
Truth Maintenance System, Predicated Completion and Circumscription, Modal and Temporal Logics, Fuzzy
Logic and Natural Language Computation.
5. Probabilistic Reasoning: Bayesian Probabilistic Inference, Possible World Representations, Dempster-Shafer
Theory, Ad-Hoc Methods, Heuristic Reasoning Methods. Structured Knowledge: Associative Networks, Frame
Structures, Conceptual Dependencies and Scripts. Object Oriented Representation: Overview of Object-Oriented
Systems, Object, Classes, Messages and Methods.
6. Search and Control Strategies: Preliminary Concepts, Examples of Search Problems, Uninformed or Blind
Search, Informed Search, Searching And-Or Graph.
7. Matching Techniques: Structures used in Matching, Measures for Matching, Matching Like Patterns, Partial
Matching, Fuzzy Matching Algorithms, The RETE Matching Algorithm. Knowledge Organization and
Management: Indexing and Retrieval Techniques, Integrating Knowledge in Memory.
8. Natural Language Processing: Overview of Linguistics, Grammars and Languages, Basic Parsing Techniques,
Semantic Analysis and Representation Structures, Natural Language Generalization, Natural Language Systems,
Recognition and Classification Process.
9. Expert System Architecture: Rule-Based Architecture, Nonproduction System Architectures, Dealing with
Uncertainty, Knowledge Acquisition and Validation.
10. Types of Learning, Knowledge Acquisition is Difficult, General Learning Model, Performance Measures,
Knowledge System Building Tools, Learning by Induction: Generalization and Specialization, Inductive Bias.
Analogical Reasoning and Learning, Explanation based Learning.