Unit 1- Part 1.pptx about basic of Artificial intelligence

21CSC206T
Artificial Intelligence
References :
1. Stuart Russel and Peter Norvig, “Artificial Intelligence: A
Modern Approach”, Fourth Edition, Pearson Education, 2020.
2. Parag Kulkarni, Prachi Joshi, Artificial Intelligence –Building
Intelligent Systems, 1st ed., PHI learning, 2015.

Sl.No
Component Type Marks
1 Cycle Test-I
1. Written Test 10
2. Quiz/Puzzles 5
3. AWS online Course Completion
(Machine Learning Foundation)
10
2 Cycle Test-II
1. Written Test 10
2. Quiz/Puzzles 5
3. Hackerank - 5 Questions 10
3 Hackathon / Group
Activity
1. Global Challenge / Hackathons/Ideathons /
Makethons /Any AI Technical Competitions
including conference presentations/
Samsung Prism
5
2. Group Activity (Poster Presentation) 5
Total Marks 60
Assessment Plan

UNIT 1- PART 1
AI techniques, Problem solving with AI, AI Models, Data acquisition and
learning aspects in AI
Problem solving- Problem solving process, formulating problems

Introduction to AI
Real Worlds Examples
• Automatic Toll Collection Booth
• Loading Vehicles
• Costume suggestion

Artificial Intelligence
• AI helps in taking decisions with reduced human interventions
• Automated climate control in a car
• Self driving car
• AI holistically includes, learning, searching and problem solving.
• The purpose of AI is to make machine intelligent and enable the machine to solve the
problems .
• "AI is the study of how to make computers do things which, at the moment, people do better“
- Rick & Knight

Definition of AI
Systems that think
like humans
Systems that think
rationally
Systems that act
like humans
Systems that act
rationally
HUMAN RATIONAL
Artificial Intelligence is defined in several ways, few of them are categorized in two dimensions as shown below:
THOUGHT PROCESS
AND REASONING
BEHAVIOUR
• A system is rational if it does the “right thing,” given what it knows.
• A human-centered approach must be in part an empirical science, involving observations and hypotheses
about human behavior.
• A rationalist approach involves a combination of mathematics and engineering.

Acting Humanly - The Turing Test approach
• “The art of creating machines that perform functions that require intelligence when performed by people.” (Kurzweil)
• “The study of how to make computers do things at which, at the moment, people are better.” (Rich and Knight)
• The Turing Test, proposed by Alan Turing (1950), was designed to provide an operational definition of intelligence.
• A computer passes the test if a human interrogator, after posing some written questions, cannot tell whether the
written responses come from a person or from a computer.
The computer would need to possess the following capabilities:
• Natural language processing to enable it to communicate
successfully in English;
• Knowledge representation to store what it knows or
hears;
• Automated reasoning to use the stored information to
answer questions and to draw new conclusions;
• Machine learning to adapt to new circumstances and to
detect and extrapolate patterns.
• Computer vision to perceive objects (Seeing)
• Robotics to manipulate objects and move about (Acting)

Thinking Humanly -The cognitive modeling approach
• “The exciting new effort to make computers think . . . machines with minds, in the full and literal sense.” (Haugeland, 1985)
• “[The automation of] activities that we associate with human thinking, activities such as decision-making, problem solving, earning .
. .” (Bellman, 1978)
• Humans as observed from ‘inside’
• How do we know how humans think?
• Introspection vs. psychological experiments
COGNITIVE SCIENCE
The interdisciplinary field of cognitive science brings together computer models from AI and experimental techniques
from psychology to construct precise and testable theories of the human mind.

Thinking Rationally - The “laws of thought” approach
• “The study of mental faculties through the use of computational models.”(Charniak and McDermott, 1985)
• “The study of the computations that make it possible to perceive, reason, and act.” (Winston, 1992)
• Logic can’t express everything (e.g. uncertainty)
• Logical approach is often not feasible in terms of computation time (needs ‘guidance’)
• Not all intelligent behavior controlled by logic
Acting Rationally -The rational agent approach
• Rational behavior: doing the right thing
• The right thing: that which is expected to maximize goal achievement, given the available information
• An agent is just something that acts. Giving answers to questions is ‘acting’.
• A rational agent is one that acts so as to achieve the best outcome or, when there is uncertainty,
the best expected outcome.
• “Computational Intelligence is the study of the design of intelligent agents.” (Poole et al., 1998)
• “AI . . . is concerned with intelligent behavior in artifacts.” (Nilsson, 1998)

PEAS
• Performance Measure: Performance measure is the unit to define the success of an agent.
Performance varies with agents based on their different precepts.
• Environment: Environment is the surrounding of an agent at every instant. It keeps changing with
time if the agent is set in motion. There are 5 major types of environments:
• Fully Observable & Partially Observable
• Episodic & Sequential
• Static & Dynamic
• Discrete & Continuous
• Deterministic & Stochastic
• Actuator: An actuator is a part of the agent that delivers the output of action to the environment.
• Sensor: Sensors are the receptive parts of an agent that takes in the input for the agent.

Deterministic and non-deterministic
An environment is deterministic if the next state of the environment is solely determined by the current state of
the environment and the actions selected by the agents. An inaccessible environment might appear to be non-
deterministic since the agent has no way of sensing part of the environment and the result of its actions on it. We
have to take into consideration the point of view of the agent when determining whether an environment is
deterministic or not since the agent might have limited perception capabilities.
Episodic and non-episodic
In an episodic environment, the agent's experience is divided into episodes which consist of a percept sequence and an
action. Since episodes are independent of one another and the agent doesn't need to know the effect of its actions.
Static and dynamic
An environment is dynamic if it changes while an agent is in the process of responding to a percept sequence. It is static
if it does not change while the agent is deciding on an action i.e the agent does not to keep in touch with time. An
environment is semidynamic if it does not change with timebut he agent's performace score does.
Discrete and continuous
If the number of percepts and actions in the environment is limited and distinct then the environment is said to be
discrete.eg A chess board

Unit 1- Part 1.pptx about basic of Artificial intelligence

Agent
Performance
Measure
Environment Actuator Sensor
Hospital
Management
System
Patient’s health,
Admission
process,
Payment
Hospital,
Doctors, Patients
Prescription,
Diagnosis, Scan
report
Symptoms,
Patient’s
response
Automated Car
Drive
The comfortable
trip, Safety,
Maximum
Distance
Roads, Traffic,
Vehicles
Steering wheel,
Accelerator,
Brake, Mirror
Camera, GPS,
Odometer
Subject Tutoring
Maximize scores,
Improvement is
students
Classroom, Desk,
Chair, Board,
Staff, Students
Smart displays,
Corrections
Eyes, Ears,
Notebooks
Part-picking
robot
Percentage of
parts in correct
bins
Conveyor belt
with parts; bins
Jointed arms and
hand
Camera, joint
angle sensors
AI rational agent examples

AI TECHNIQUES
• AI deals with practical problems, identification and authentication problem, interdependent and
cross-domain problems, and classification problems.
Need for AI techniques:
• Analysis of voluminous and large amount of data from multi-domain
• Characterization of miscellaneous data and mapping of this data with reference to built-in knowledge and
building the knowledge further
• Dealing with the constantly changing scenarios and situations
• Data Analytics [ Collecting till decision making]
• Knowledge building based on limited relevant data from huge pool of irrelevant data
• The main objective of AI techniques is to capture knowledge based on data and information.
• AI techniques need to handle different problems that can be categorized as following:
 Structured problems : Defined goal state
 Unstructured problems : Goal state not known
 Linear problems : Based on dependent variables (Linear classification)
 Non linear problems : No dependency between variables

Problem Solving with AI
Well structured problems
A well structured problems yield a right answer or right interference when
appropriate algorithm is applied.
Examples:
1. Solving quadratic equation
2. Calculating speed of ball when it reached the batsman
Ill structured problems
• An ill-structured problem do not yield a particular answer.
• Examples: (Real world problems)
• Challenging due to lack of defined steps and lack of well defined
criterion to evaluate the outcome

AI Models
• Dunker introduced ‘maze hypothesis’, where the creative and intelligent tasks are modelled like a set of maze of
paths from an initial node to a certain or resultant node.
• All problems cannot be solved using maze-approach, which lead to Logic Theory Machines.
• Applied to general problem solving like Chess, where there is controlled environment with given situation and goal.
Semiotic models:
• Based on sign process, signification or communication.
Eg: Associating Thumbs-up gesture with positivity.
Statistical models:
• Representation and formalization of relationships through statistical
techniques.
• Uses probabilistic approaches

Data Acquisition and Learning Aspects in AI
Knowledge Discovery – Data Mining and Machine Learning
• Information : Pattern underlying data
• Data : Recorded facts
• Data mining and Knowledge discovery : Extraction of meaningful information
• Data mining: Data cleaning, preprocessing, identifying and interpreting the patterns, understanding the
applications and generating the target data with consolidated patterns.
• Machine Learning : Making machine intelligible based on past experience.
Computational Learning Theory (COLT)
• Formal mathematical models defined to analyze the efficiency and complexity in terms of computation,
prediction and feasibility of algorithm.
• Applied in machine learning, pattern recognition, statistics and so on.

Neural and Evolutionary Computation
• Evolutionary Computation enabled to speed up data mining.
• Neural computing involves stimulating the neural behavior of human to enable machine to learn.
• Artificial Neural Network (ANN) is configured for applications like pattern recognition or classification.
Intelligent agent and multi-agent systems
• Agent: Software program
• Intelligent agent : flexible in terms of actions to get desired outcomes. It is goal directed, reacts with environment
and acts accordingly.
• Complex tasks and decision making demand combination of more than one percept where group of intelligent
agents required to solve the problem - Multi agent System (MAS)
Multi-perspective integrated intelligence
• Utilizing and exploiting knowledge form different perspective to build an intelligent system.
• Information collected from different perspectives is used for final decision-making.
• The collected information can be continuous or discrete.

Problem solving in AI
• Problem solving-process of generating solutions for the given situation
• Problem is defined,
• in a context
• has well defined objective
• solution has set of activities
•Uses previous knowledge and domain knowledge
•Primary objective-problem identification

•Types of problem solving
• General purpose: Means-ends analysis
• present situation is compared with the goal to detect the difference
• select action that reduces the difference
• Ex: select the mode of transport
• Special purpose-modelled for the specific problem, which have specific features
• Ex: classify legal document reference to particular case
Problem solving technique involves
• problem definition - problem formulation
• problem analysis and representation
• planning
• execution
• evaluating solution
• consolidating gains - Goals
Problem solving in AI
Goal formulation
Search and execute

Formulating Problems
• Well-defined problems and solutions - A problem is really a collection of information that the agent will use to
decide what to do.
• Elements of a problem:
• 1. The initial state that the agent knows itself to be in.
• 2. The set of possible actions available to the agent.
• operator is used to denote the description of an action to reach a state.
• state space-the set of all states reachable from the initial state by any sequence of actions.
• A path in the state space is simply any sequence of actions leading from one state to another.
• 3. The goal test, which the agent can apply to a single state description to determine if it is a goal state.
• 4. A path cost function is a function that assigns a cost to a path.
• 5. The output of a search algorithm is a solution, that is, a path from the initial state to a state that satisfies
the goal test.

• Measuring problem-solving performance
• Solution is obtained or not
• Obtained solution is good solution or not(with a low path cost)
• Search cost-associated with the time and memory required to find a solution.
• total cost of the search is the sum of the path cost and the search cost
• Choosing states and actions
• To decide a better solution, determine the measurement of path cost function The process of
removing detail from a representation is called abstraction
Formulating Problems
Example

Problem: To reach from initial state to final state with minimum number of moves
Illustration Algorithm
A well defined problem is described in terms of

PROBLEM TYPES AND CHARACTERISTICS
• Problem types
• Single-state problem / Deterministic or Observable
• Multiple-state problem/ Non observable
• Contingency problem/Non-deterministic or partially observable
• Exploration problem /Unknown state space
Problem Characteristics:
To choose an appropriate method for a particular Problem:
• Is the problem decomposable?
• Can solution steps be ignored or undone?
• Is the universe predictable?
• Is a good solution absolute or relative?
• Is the solution a state or a path?
• What is the role of knowledge?
• Does the task require human interaction?
‐

Istheproblemdecomposableintosmallsub-problemswhichareeasytosolve?
Can the problem be broken down into smaller problems to be solved independently?
The decomposable problem can be solved easily.
Can solution steps be ignored or undone?
• In the Theorem Proving problem, a lemma that has been proved can be ignored for the next steps.
• Such problems are called Ignorable problems.
• In the 8-Puzzle, Moves can be undone and backtracked.
• Such problems are called Recoverable problems.
• In Playing Chess, moves can be retracted (withdraw).
• Such problems are called Irrecoverable problems.
• Ignorable problems can be solved using a simple control structure that never backtracks.
• Recoverable problems can be solved using backtracking.
• Irrecoverable problems can be solved by recoverable style methods via planning.

Istheuniverseoftheproblemispredictable?
• In Playing Bridge, We cannot know exactly where all the cards are or what the other players will do on their
turns.
• Uncertain outcome!
• For certain-outcome problems, planning can be used to generate a sequence of operators that is
guaranteed to lead to a solution.
• For uncertain-outcome problems, a sequence of generated operators can only have a good probability of
leading to a solution. Plan revision is made as the plan is carried out and the necessary feedback is provided.
Is a good solution to the problem is absolute or relative?
• The Travelling Salesman Problem, we have to try all paths to find the shortest one.
• Any path problem can be solved using heuristics that suggest good paths to explore.
• For best-path problems, a much more exhaustive search will be performed.

Isthesolutiontotheproblemastateorapath
28
• The Water Jug Problem, the path that leads to the goal must be reported.
• A path-solution problem can be reformulated as a state-solution problem by describing a state as
a partial path to a solution. The question is whether that is natural or not.

Doesthetaskofsolvingaproblemrequirehumaninteraction?
30
• The solitary problem, in which there is no intermediate communication and no demand for an
explanation of the reasoning process.
• The conversational problem, in which intermediate communication is to provide either
additional assistance to the computer or additional information to the user.
Role of knowledge

SEARCH
Search is a general algorithm that helps in finding the path in state space
The path may lead to the solution or dead end.
Forward search(data directed)
Starts search from initial state towards goal state.
Ex: locating a city from current location
Backward search(goal directed)
Search stars from goal state towards a solvable initial state.
Ex: start from target city

SEARCH
Strategies to explore the states
Informed search – No guarantee for solution but high probability of getting solution
 -heuristic approach is used to control the flow of solution path
 -heuristic approach is a technique based on common sense, rule of thumb, educated guesses or intuitive judgment
Uninformed search – generates all possible states in the state space and checks for the
goal state.
 time consuming due to large state space used where error in the algorithm has severe consequences
Parameters for search evaluation
 completeness: Guaranteed to find a solution within finite time
 space and time complexity: memory required and time factor needed
 optimality and admissibility: correctness of the solution

Unit 1- Part 1.pptx about basic of Artificial intelligence

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