School of Computing
Department of Computer Science and Engineering
ALGORITHMS FOR
INTELLIGENT
SYSTEMS AND ROBOTICS
(CSE18R292)
Student Name :
Register Number :
Branch / Section :
Year/Semester :
, SCHOOL OF COMPUTING
DEPARTMENT OF COMPUTER SCIENCE AND
ENGINEERING
BONAFIDE CERTIFICATE
Bonafide record of the work done by in partial
fulfillment of the requirements for the award of the degree of Bachelor of Technology in
Specialization of the Computer Science and Engineering, during the Academic year odd Semester
(2022-23)
Staff In-charge Head of the Department
Submitted to the practical Examination held at Kalasalingam Academy of Research and Education
(Deemed to be University), Krishnankoil on
REGISTER NUMBER
Internal Examiner External Examiner
, TABLE OF CONTENTS
S.No Date Name of the Experiments Page No. Signature
1 PEAS
2 DECISION TREE
3 BAYESIAN BELIEF NETWORK
4 DEPTH FIRST SEARCH
5 BREADTH FIRST SEARCH
6 UNINFORMED SEARCH ALGORITHM
7 MIN MAX SEARCH ALGORITHM
8 N QUEENS PROBLEM
9 WUMPUS WORLD PROBLEM
10 BAGGING AND BOOSTING
11 ROBO-DK INSTALLATION AND BASIC
COMMANDS
RESEARCH ARTICLE WITH PLAGIARISM
12
REPORT AND ACCEPTANCE MAIL
, Experiment:2 DecisionTree
Aim:
To implement a decision tree classification algorithm for a dust-picking robot using
the python programming language.
Description:
o Decision Tree is a Supervised learning technique that can be used for both classification
and Regression problems, but mostly it is preferred for solving Classification problems.
It is a tree-structured classifier, where internal nodes represent the features of a dataset,
branches represent the decision rules and each leaf node represents the outcome.
o In a Decision tree, there are two nodes, which are the Decision Node and Leaf Node.
Decision nodes are used to make any decision and have multiple branches, whereas Leaf
nodes are the output of those decisions and do not contain any further branches.
o The decisions or the test are performed on the basis of features of the given dataset.
o It is a graphical representation for getting all the possible solutions to a
problem/decision based on given conditions.
o It is called a decision tree because similar to a tree, it starts with the root node, which
expands on further branches and constructs a tree-like structure.
o In order to build a tree, we use the CART algorithm, which stands for Classification and
Regression Tree algorithm.
o A decision tree simply asks a question, and based on the answer (Yes/No), it further
split the tree into subtrees.
o Below diagram explains the general structure of a decision tree:
Department of Computer Science and Engineering
ALGORITHMS FOR
INTELLIGENT
SYSTEMS AND ROBOTICS
(CSE18R292)
Student Name :
Register Number :
Branch / Section :
Year/Semester :
, SCHOOL OF COMPUTING
DEPARTMENT OF COMPUTER SCIENCE AND
ENGINEERING
BONAFIDE CERTIFICATE
Bonafide record of the work done by in partial
fulfillment of the requirements for the award of the degree of Bachelor of Technology in
Specialization of the Computer Science and Engineering, during the Academic year odd Semester
(2022-23)
Staff In-charge Head of the Department
Submitted to the practical Examination held at Kalasalingam Academy of Research and Education
(Deemed to be University), Krishnankoil on
REGISTER NUMBER
Internal Examiner External Examiner
, TABLE OF CONTENTS
S.No Date Name of the Experiments Page No. Signature
1 PEAS
2 DECISION TREE
3 BAYESIAN BELIEF NETWORK
4 DEPTH FIRST SEARCH
5 BREADTH FIRST SEARCH
6 UNINFORMED SEARCH ALGORITHM
7 MIN MAX SEARCH ALGORITHM
8 N QUEENS PROBLEM
9 WUMPUS WORLD PROBLEM
10 BAGGING AND BOOSTING
11 ROBO-DK INSTALLATION AND BASIC
COMMANDS
RESEARCH ARTICLE WITH PLAGIARISM
12
REPORT AND ACCEPTANCE MAIL
, Experiment:2 DecisionTree
Aim:
To implement a decision tree classification algorithm for a dust-picking robot using
the python programming language.
Description:
o Decision Tree is a Supervised learning technique that can be used for both classification
and Regression problems, but mostly it is preferred for solving Classification problems.
It is a tree-structured classifier, where internal nodes represent the features of a dataset,
branches represent the decision rules and each leaf node represents the outcome.
o In a Decision tree, there are two nodes, which are the Decision Node and Leaf Node.
Decision nodes are used to make any decision and have multiple branches, whereas Leaf
nodes are the output of those decisions and do not contain any further branches.
o The decisions or the test are performed on the basis of features of the given dataset.
o It is a graphical representation for getting all the possible solutions to a
problem/decision based on given conditions.
o It is called a decision tree because similar to a tree, it starts with the root node, which
expands on further branches and constructs a tree-like structure.
o In order to build a tree, we use the CART algorithm, which stands for Classification and
Regression Tree algorithm.
o A decision tree simply asks a question, and based on the answer (Yes/No), it further
split the tree into subtrees.
o Below diagram explains the general structure of a decision tree:
