May 21, 2016

Learning Artificial Intelligence – university course lecture notes

Filed under: 02 Artificial Intelligence, NIL - All Posts — myreaders @ 2:26 pm

Title :  Learning Artificial Intelligence – university course lecture notes

Ref  URL :

Access :  Free, no registration required.

Description :  Topics organized into 11 sections following academic curriculum – Introduction to AI, Problem Solving, Search & Control Strategies, Knowledge representation – logic & rules, Reasoning system, Game playing, Learning system, Expert system, Neural network, Genetic algorithm, Natural language processing, and Common sense. (Total 600 pages in 11 pdf files include text, graphics, examples, problems & solutions).

Objectives :  Free online self explanatory full course learning resources & teaching materials.

Who Should read :  Beginners, senior students, professionals and researchers.

Author :  RC Chakraborty, , Former Visiting Professor at JUET.

Conditions of Use:  Creative Commons Attribution-Noncommercial-No Derivative Works

2.5 [Ref. ]

Update on Dec. 20, 2015

Tags :  Artificial intelligence, Problem solving, Knowledge representation, Reasoning system, Game playing, Learning system, Expert system, Neural network, Genetic algorithm, Natural language processing, Common sense.

April 3, 2008

Artificial Intelligence – Knowledge Representation – issues, predicate logic, rules

Artificial Intelligence – Knowledge Representation, Issues, Predicate Logic, Rules

This is part of the courseware on Artificial Intelligence, by R C Chakraborty, at JUET. It consists eight hours of lectures. The topics are :

Knowledge Representation (KR) – introduction, knowledge progression, model, category, typology map, relationship, mapping, representation, schemes, relational, inheritable, inferential, declarative, procedural, attributes, relationship, granularity.

KR using Logic – predicate logic, propositional logic, statements, variables, symbols, connective, truth value, contingencies, tautologies, contradictions, antecedent, consequent, argument, expressions, quantifiers, formula, representing “IsA” and “Instance” relationships.

KR using Rules – declarative, procedural, meta rules, procedural verses declarative knowledge & language, logic programming characteristics, statement, language, syntax & terminology, data components, simple & structured data objects, program components clause, predicate, sentence, subject, queries, programming paradigms, model, computation, imperative model, functional model, logic model, reasoning, forward and backward chaining, conflict resolution, and control knowledge.

For complete course lecture slides move on to Website URL :


March 17, 2008

Artificial Intelligence Technologies, CSN-2008, JIET

Filed under: 02 Artificial Intelligence, NIL - All Posts — myreaders @ 10:17 am

Artificial Intelligence Technologies, CSN-2008, JIET

A paper presented in National Conference on Communication Systems and Networking, on March 15 -16, 2008, at Jaypee Institute of Engineering & Technology (JIET), Guna, by R C Chakraborty, Visiting Prof. JIET, Guna & Former Dir. DTRL & ISSA (DRDO). The Highlights of the presentation on Artificial intelligence technologies are :

(a) AI definitions – The term Artificial Intelligence(AI) was coined in 1950s. It is about creation of machines that perform tasks that, if performed by a human, would require intelligence. It is unique, sharing borders with mathematics, computer science, philosophy, psychology, biology, cognitive science and many others. There is no clear definition of Artificial Intelligence or even Intelligence. It can be described as an attempt to build machines that like humans can think, act, learn and use knowledge to solve problems on their own. The definitions of AI outlined in text books are concerned with reasoning and behavior. Its success is measured in terms of human performance and concept of intelligence called rationality.  

(b) AI goals – The definitions of AI gives four possible goals to pursue for intelligent an machine : that think like humans, that think rationally, that behave like humans, that behave rationally.

(c) AI Technology timeline – The Roots of AI actually began centuries ago, long before computers. The Roman Abacus, 5000 years ago, is a machine with memory. The Pascaline, 1652, is a calculating machines that mechanized arithmetic. The Differential Engine, 1849, is a mechanical calculating machine programmable to tabulate polynomial functions. Turing machines, 1936, an abstract symbol-manipulating device, adapted to simulate the logic, is first computer invented on paper only. The Von Neumann architecture, 1945, is computer design model, has a processing unit and a shared memory structure to hold both instructions and data. The ENIAC, 1946, called Electronic Numerical Integrator and Calculator is the first electronic general-purpose digital computer by Eckert & Mauchly. Thus, the development went on to finally a desktop, a laptop and more.

(d) AI events timeline – The concept of AI as a true scientific pursuit is a very young. In 1950, Turing test, by Alan Turing, is a measure of machine intelligence. The year, Norbert Wiener, observed link between human intelligence and machines, theorized intelligent behavior. In 1955, a program by Allen Newell and Herbert Simon, claimed that machines can contain minds just as human bodies do, proved 38 of the first 52 theorems in Principia Mathematica. In 1956, was the birth of AI, at Dartmouth Summer Research Conference on Artificial Intelligence, organised by John McCarthy regarded as the father of AI. Since then AI began to pick up momentum and the centers for AI research began forming at Carnegie Mellon and MIT and later in other institutions & laboratories.

(e) AI transition from Lab to real world – The impact of AI and the computer technology were felt. Foundations like “American Association for Artificial Intelligence started”. The demand for AI development, pushed the researchers to join 150 private companies. In 1986, AI based hardware, cost $425 million, sold to companies. In 1991, AI military systems put to test in war, during ‘Desert Strom’. Now the adaptation of AI is every where that need no specific mention.

(f) AI sub disciplines – A few as multi-agent systems, cognitive modeling and human interaction, commonsense reasoning , evolutionary computation, game playing and interactive entertainment, knowledge representation and reasoning, machine learning and data mining, planning and scheduling, robotics, search, semantic web, vision and perception.

(h) AI future in next quarter century – Trying to build a system that is equal or better than a human, on general tasks. The goal of AI is :

  1. Building machine on the model of man, a robot to have its childhood, learn language as a child does, gain knowledge by sensing the world through its own organs, and ultimately contemplate the whole domain of human thought.
  2. Building useful applications, restricted to a particular domain, specific tasks, e.g. an autonomous vehicle, speech recognition system and many more.

Conclusion – Despite the advances in the last 50 years, the original goals set by the first generation of AI visionaries have not been reached. The natural intelligence is far from being understood, while the artificial forms of intelligence is still very primitive. Simple tasks like object manipulation and recognition, which a 3-year-old baby can do, have not yet been realized artificially. (Ref : 50th Anniversary Summit of Artificial Intelligence, Centro Steno Franscini – Monte Verita Switzer Land, July 9-14, 2006).

For complete lecture slides move on to Website URL :


January 22, 2008

Artificial Intelligence – Problem Solving, Search and Control Strategies

Artificial Intelligence – Problem Solving, Search and Control Strategies

This is part of the courseware on Artificial Intelligence,  by R C Chakraborty,  at JUET.  It consists eight hours of lectures. The topics are :

General Problem Solving – problem definition, state space, state change, goal state, solution;

Search and Control Strategies – search space, search algorithm’s performance, complexity & cost functions, search strategies – heuristics, forward and backward chaining.

Exhaustive Searches – depth-first search Algorithm, breadth-first search algorithm, compare depth-first and breadth-first search.

Heuristic Search – types of heuristic search algorithms, characteristics of heuristic search compared with other search, and examples. Constraint Satisfaction Problems (CSPs) and Models – definition, properties and algorithms, models, generate and test, backtracking algorithm, and examples.

For complete course lecture slides move on to Website URL :


August 4, 2007

Artificial Intelligence – Course Lecture Slides

Artificial Intelligence –  Course  Lecture Slides

This course on Artificial Intelligence refers to even semester (Jan–May), B.Tech, 6th semester, 4 Credit, 42 hours course, by R C Chakraborty, Visiting Prof. JIET, Guna & Former Dir. DTRL & ISSA (DRDO). The course includes : Introduction to AI, Problem Solving, Search and Control Strategies, Knowledge Representation Issues, Predicate Logic & Rules, Reasoning System, Game Playing, Learning, Expert System, Fundamentals of Neural Networks, Fundamentals of Genetic Algorithms, Natural Language Processing, Common Sense. I offered this course for eight years (2006-13).

For complete course lecture slides move on to Website URL :

June 11, 2007

Courseware on Artificial Intelligence

Filed under: 02 Artificial Intelligence, NIL - All Posts — Tags: , — myreaders @ 11:03 am

 Artificial Intelligence – Course Content

Artificial Intelligence topics : Introduction, Problem solving, Search and control strategies, Knowledge representations issues, predicate logic, rules, Reasoning system – symbolic, statistical, Game playing, Learning systems, Expert systems, Fundamentals of neural networks, Fundamentals of genetic algorithms, Natural language processing, Common sense.

For complete course lecture slides move on to Website URL :


Course offered by R C Chakraborty, at JUET.

Blog at