Artificial Intelligence: Search Methods for Problem Solving

Start Date
01/07/2018

End Date
08/09/2018

No. of
Enrollments
1439 students

No course
syllabus uploaded

Created by

Deepak Khemani
IIT Madras
4
out of 5
Based on 1 rating
5 star 0
4 star 1
3 star 0
2 star 0
1 star 0
Course Language
English
Course Type
Scheduled
Video transcripts
Course Category
Engineering
Learning Path
Post graduate
Course Length
0 Hours
Weekly time commitments
0 Hours
Course Completion
Exam Date
To be announced
Credits
0

32

Tutorials

0

Test

0

Assignment

0

Article

0

Weekly Reading list

Overview

For an autonomous agent to behave in an intelligent manner it must be able to solve problems. This means it should be able to arrive at decisions that transform a given situation into a desired or goal situation. The agent should be able to imagine the consequence of its decisions to be able to identify the ones that work. In this first course on AI we study a wide variety of search methods that agents can employ for problem solving. 

In a follow up course – AI: Knowledge Representation and Reasoning– we will go into the details of how an agent can represent its world and reason with what it knows. These two courses should lay a strong foundation for artificial intelligence, which the student can build upon. A third short course – AI: Constraint Satisfaction Problems – presents a slightly different formalism for problem solving, one in which the search and reasoning processes mentioned above can operate together.
 

INTENDED AUDIENCE 

This is a first course on Artificial Intelligence. While the intended audience is both UG and PG students studying Computer Science, in fact anyone comfortable with talking about algorithms should be able to do the course.



INDUSTRY SUPPORT – LIST OF COMPANIES/INDUSTRY THAT WILL RECOGNIZE/VALUE THIS ONLINE COURSE 

Any industry that is involved in development of AI applications. This not only includes software companies (like Microsoft, Google, and Facebook) but also manufacturing companies like Ford and General Electric, and retail companies like Amazon and Flipkart.

 

PRE-REQUISITES 
Exposure to data structures and programming and an ability to discuss algorithms is the only pre-requisite.



COURSE SYLLABUS 

Overview and Historical Perspective, Turing Test, Physical Symbol Systems and the scope of Symbolic AI, Agents. 
State Space Search, Heuristic Search, Solution Space Search, Stochastic Local Search, Population Based Methods. 
Optimal Solutions, Algorithm A*, Admissibility of A*, Space saving variations of A*. 
Problem Decomposition, Algorithm AO*, Rule Based Expert Systems, Rete Algorithm. 
Game Playing: Algorithms Minimax, AlphaBeta, SSS* 
Planning: Forward/Backward Search, Goal Stack Planning, Sussman’s Anomaly, Plan Space Planning, Algorithm Graphplan. 

Text Book (Chapters 1-8): Deepak Khemani, A First Course in Artificial Intelligence, McGraw Hill (India), 2013

To access the content, please enroll in the course.

Faculty

Deepak Khemani


Deepak Khemani is Professor at Department of Computer Science and Engineering, IIT Madras. He completed his B.Tech. (1980) in Mechanical Engineering, and M.Tech. (1983) and PhD. (1989) in Computer Science from IIT Bombay, and has been with IIT Madras since then. In between he spent a year at Tata Research Development and Design Centre, Pune and another at the then youngest IIT at Mandi. He has had shorter stays at several Computing departments in Europe.

 

Prof Khemani’s long-term goals are to build articulate problem solving systems using AI that can interact with human beings. His research interests include Memory Based Reasoning, Knowledge Representation and Reasoning, Planning and Constraint Satisfaction, Qualitative Reasoning, and Natural Language Processing.

FAQs

No FAQ has been added to this course yet.

Download App

Download SWAYAM applications from popular app stores