This course
will introduce the basic principles in artificial intelligence. It will cover
simple
representation schemes, problem solving paradigms, constraint propagation, and search
strategies. Areas of application such as knowledge representation, natural language processing,
expert systems, vision and robotics will be explored. The Prolog programming language will also
be introduced.
representation schemes, problem solving paradigms, constraint propagation, and search
strategies. Areas of application such as knowledge representation, natural language processing,
expert systems, vision and robotics will be explored. The Prolog programming language will also
be introduced.
What is AI,
Foundations of AI, History of AI. Weak AI, Strong AI.Intelligent Agents: Agents
and Environments, The Nature of Environments, The Structure of Agents. Problem Solving by
Searching.Breadth-First Search, Depth-First Search, Depth-limited Search, Iterative Deepening,
Depth-first Search, Comparison of Uninformed Search Strategies. Informed Search and
Exploration.Constraint Satisfaction Problems.Reasoning and Knowledge
Representation.Inference in First-Order Logic.Introduction to Prolog Programming.Reasoning
Systems for Categories.Reasoning with Uncertainty & Probabilistic Reasoning.Representing
Knowledge in an Uncertain Domain.Learning from Observations.Knowledge in Learning.
Statistical Learning, Neural Networks.
and Environments, The Nature of Environments, The Structure of Agents. Problem Solving by
Searching.Breadth-First Search, Depth-First Search, Depth-limited Search, Iterative Deepening,
Depth-first Search, Comparison of Uninformed Search Strategies. Informed Search and
Exploration.Constraint Satisfaction Problems.Reasoning and Knowledge
Representation.Inference in First-Order Logic.Introduction to Prolog Programming.Reasoning
Systems for Categories.Reasoning with Uncertainty & Probabilistic Reasoning.Representing
Knowledge in an Uncertain Domain.Learning from Observations.Knowledge in Learning.
Statistical Learning, Neural Networks.
1.
Introduction:
What is AI, Foundations of AI, History of AI. Intelligent Agents: Agents
and Environments, The Nature of Environments, The Structure of Agents [TB: Ch. 1, 2]
and Environments, The Nature of Environments, The Structure of Agents [TB: Ch. 1, 2]
2.
Problem Solving
by Searching: Problem Solving Agents, Searching for Solutions,
Uninformed Search Strategies.
Uninformed Search Strategies.
3.
Breadth-First
Search, Depth-First Search, Depth-limited Search, Iterative Deepening,
Depth-first Search, Comparison of Uninformed Search Strategies. [TB: Ch. 3]
Depth-first Search, Comparison of Uninformed Search Strategies. [TB: Ch. 3]
4.
Informed
Search and Exploration: Informed (Heuristic) Search Strategies: Greedy Best-
first Search, A* Search, Heuristic Functions, Local Search Algorithms and Optimization
Problems. [TB: Ch. 4]
first Search, A* Search, Heuristic Functions, Local Search Algorithms and Optimization
Problems. [TB: Ch. 4]
 |
5.
Constraint
Satisfaction Problems: Backtracking Search for CSPs, Local Search for
CSPs. Adversarial Search: Games, Minimax Algorithm, Alpha-Beta Pruning. [TB: Ch.
5, 6]
CSPs. Adversarial Search: Games, Minimax Algorithm, Alpha-Beta Pruning. [TB: Ch.
5, 6]
6.
Reasoning and
Knowledge Representation: Introductions to Reasoning and Knowledge
Representation, Propositional Logic, First Order Logic: Syntax and Semantics of First-
Order Logic, Knowledge Engineering in First-Order Logic, [TB: Ch. 7, 8]
Representation, Propositional Logic, First Order Logic: Syntax and Semantics of First-
Order Logic, Knowledge Engineering in First-Order Logic, [TB: Ch. 7, 8]
7.
Inference in
First-Order Logic: Inference rules for quantifiers, A first-order inference
rule, Unification, Forward Chaining, Backward Chaining, A backward chaining
algorithm, Logic programming, The resolution inference rule [TB: Ch. 9]
rule, Unification, Forward Chaining, Backward Chaining, A backward chaining
algorithm, Logic programming, The resolution inference rule [TB: Ch. 9]
8.
Introduction
to Prolog Programming
9.
Reasoning
Systems for Categories, Semantic Nets and Description logics, Reasoning
with Default Information: Open and closed worlds, Negation as failure and stable model
semantic. Truth Maintenance Systems [TB: Ch. 10]
with Default Information: Open and closed worlds, Negation as failure and stable model
semantic. Truth Maintenance Systems [TB: Ch. 10]
10.
Reasoning with
Uncertainty & Probabilistic Reasoning : Acting Under Uncertainty,
Bayes' Rule and Its Use, [TB: Ch 13]
Bayes' Rule and Its Use, [TB: Ch 13]
11.
Representing
Knowledge in an Uncertain Domain, The Semantics of Bayesian
Networks. [TB: Ch. 14]
Networks. [TB: Ch. 14]
12.
Learning from
Observations: Forms of Learning , Inductive Learning,, Learning
Decision Trees [TB: Ch. 18]
Decision Trees [TB: Ch. 18]
13.
Knowledge in
Learning, Explanation-Based Learning, Inductive Logic Programming.
[TB: 19]
[TB: 19]
14.
Statistical
Learning, Neural Networks [TB: Ch. 20]
•
Artificial
Intelligence: A Modern Approach, by Russell and Norvig, Prentice Hall.
2ndEdition. ISBN-10: 0137903952
2ndEdition. ISBN-10: 0137903952
•
Artificial Intelligence:
A Systems Approach by M. Tim Jones, Jones and Bartlett
Publishers, Inc; 1stEdition (December 26, 2008). ISBN-10: 0763773379
Publishers, Inc; 1stEdition (December 26, 2008). ISBN-10: 0763773379
•
Artificial
Intelligence in the 21st Century by Stephen Lucci , Danny Kopec,
Mercury
Learning and Information (May 18, 2012). ISBN-10: 1936420236
Learning and Information (May 18, 2012). ISBN-10: 1936420236
Note: This
content is obtained from official documents of University of Sargodha and
applied on BS Computer Science for Main Campus, Sub
Campuses, and Affiliated Colleges.
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