1. CSA2001
FOUNDAMENTALS IN AI and ML
Prepared by
Dr Komarasamy G
Senior Associate Professor,
School of Computing Science and Engineering,
VIT Bhopal University
2. Unit-1 Contents
Introduction – Definition - Future of Artificial Intelligence -
Characteristics of Intelligent Agents - Typical Intelligent Agents –
Problem Solving Approach to Typical AI problems.
Unit-1 Introduction to AI ML
3. AI Definitions
• The study of how to make programs/computers do things that people do
better
• The study of how to make computers solve problems which require
knowledge and intelligence
• The exciting new effort to make computers think … machines with minds
• The automation of activities that we associate with human thinking (e.g.,
decision-making, learning…)
• The art of creating machines that perform functions that require
intelligence when performed by people
• The study of mental faculties through the use of computational models
• A field of study that seeks to explain and emulate intelligent behavior in
terms of computational processes
• The branch of computer science that is concerned with the automation of
intelligent behavior
Thinking
machines or
machine
intelligence
Studying
cognitive
faculties
Problem Solving
and CS
Unit-1 Introduction to AI ML 3
4. What Is AI?
• AI as a field of study
• Computer Science
• Cognitive Science
• Psychology
• Philosophy
• Linguistics
• Neuroscience
• AI is part science, part engineering
• AI often must study other domains in order to implement systems
• e.g., medicine and medical practices for a medical diagnostic system, engineering and chemistry to
monitor a chemical processing plant
• AI is a belief that the brain is a form of biological computer and that the mind is
computational
• AI has had a concrete impact on society but unlike other areas of CS, the impact is often
• felt only tangentially (that is, people are not aware that system X has AI)
• felt years after the initial investment in the technology
Unit-1 Introduction to AI ML 4
6. What is Intelligence?
• Is there a “holistic” definition for intelligence?
• Here are some definitions:
• the ability to
• ; to understand and profit from experience
• a general mental capability that involves the ability to reason, plan, solve problems, think abstractly,
comprehend ideas and language, and learn
• is effectively perceiving, interpreting and responding to the environment
• None of these tells us what intelligence is, so instead, maybe we can enumerate a list of
elements that an intelligence must be able to perform:
• perceive, reason and infer, solve problems, learn and adapt, apply common sense, apply analogy,
recall, apply intuition, reach emotional states, achieve self-awareness
• Which of these are necessary for intelligence? Which are sufficient?
• Artificial Intelligence – should we define this in terms of human intelligence?
• does AI have to really be intelligent?
• what is the difference between being intelligent and demonstrating intelligent behavior?
Unit-1 Introduction to AI ML 6
27. Future Scope of Artificial Intelligence
• Cyber Security - Ensure in curbing hackers
• Face Recognition - launch of iPhone X
• Data Analysis – SAS, Tableau
• Transport - Tesla
• Various Jobs - Robotic Process Automation
• Emotion Bots - Cortana & Alexa
• Marketing & Advertising – Flipkart, Adohm
Unit-1 Introduction to AI ML 27
28. Agents
• An intelligent agent (IA) is an
entity that makes a decision,
that enables artificial
intelligence to be put into
action.
• It can also be described as a
software entity that conducts
operations in the place of
users or programs after
sensing the environment.
• It uses actuators to initiate
action in that environment.
Unit-1 Introduction to AI ML 28
An actuator is a device that uses a form of power to
convert a control signal into mechanical motion.
Industrial plants use actuators to operate valves, dampers,
fluid couplings, and other devices used in industrial
process control. The industrial actuator can use air,
hydraulic fluid, or electricity for motive power.
30. Examples of Agent
• An agent is anything that can perceive its environment
through sensors and acts upon that environment through effectors.
• A human agent has sensory organs such as eyes, ears, nose, tongue
and skin parallel to the sensors, and other organs such as hands, legs,
mouth, for effectors.
• A robotic agent replaces cameras and infrared range finders for the
sensors, and various motors and actuators for effectors.
• A software agent has encoded bit strings as its programs and actions.
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32. Agent Terminology
• Performance Measure of Agent − It is the criteria, which determines
how successful an agent is.
• Behavior of Agent − It is the action that agent performs after any
given sequence of percepts.
• Percept − It is agent’s perceptual inputs at a given instance.
• Percept Sequence − It is the history of all that an agent has perceived
till date.
• Agent Function − It is a map from the precept sequence to an action.
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33. What is an Intelligent Agent?
• Intelligent agent refers to an autonomous entity
• Directing its activity towards achieving goals, upon an environment using
observation through sensors and consequent actuators.
Unit-1 Introduction to AI ML 33
34. The Structure of Intelligent Agents
Agent’s structure can be viewed as −
• Agent = Architecture + Agent Program
• Architecture = the machinery that an agent executes on.
• Agent Program = an implementation of an agent function.
• To understand the structure of Intelligent Agents, we should be familiar
with Architecture and Agent Program.
• Architecture is the machinery that the agent executes on. It is a device
with sensors and actuators, for example : a robotic car, a camera, a PC.
• Agent program is an implementation of an agent function.
• An agent function is a map from the percept sequence(history of all that
an agent has perceived till date) to an action.
Unit-1 Introduction to AI ML 34
35. Characteristics of Intelligent Agents
Intelligent agents have the following distinguishing characteristics:
• They have some level of autonomy that allows them to perform certain tasks
on their own.
• They have a learning ability that enables them to learn even as tasks are
carried out.
• They can interact with other entities such as agents, humans, and systems.
• New rules can be accommodated by intelligent agents incrementally.
• They exhibit goal-oriented habits.
• They are knowledge-based. They use knowledge regarding communications,
processes, and entities.
Unit-1 Introduction to AI ML 35
36. • Agents can be grouped into five classes based on their degree of perceived
intelligence and capability.
• All these agents can improve their performance and generate better action
over the time.
These are given below:
• Simple Reflex Agent
• Model-based reflex agent
• Goal-based agents
• Utility-based agent
• Learning agent
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Unit-1 Introduction to AI ML
Typical Intelligent Agents
37. Simplex Agent
• The Simple reflex agents are the simplest agents. These agents take
decisions on the basis of the current percepts and ignore the rest of the
percept history.
• These agents only succeed in the fully observable environment.
• The Simple reflex agent does not consider any part of percepts history
during their decision and action process.
• The Simple reflex agent works on Condition-action rule, which means it
maps the current state to action. Such as a Room Cleaner agent, it works
only if there is dirt in the room.
• Problems for the simple reflex agent design approach:
• They have very limited intelligence
• They do not have knowledge of non-perceptual parts of the current
state
• Mostly too big to generate and to store.
• Not adaptive to changes in the environment.
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Unit-1 Introduction to AI ML
39. Model-based reflex agent
• The Model-based agent can work in a partially observable environment and track
the situation.
• A model-based agent has two important factors:
• Model: It is knowledge about "how things happen in the world," so it is called a
Model-based agent.
• Internal State: It is a representation of the current state based on percept
history.
• These agents have the model, "which is knowledge of the world" and based on the
model they perform actions.
• Updating the agent state requires information about:
• How the world evolves
• How the agent's action affects the world.
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Unit-1 Introduction to AI ML
40. function MODEL-BASED-REFLEX-AGENT(percept ) returns an action
persistent:
state, the agent’s current conception of the world state model , a description of how the
next state depends on current state and action rules, a set of condition–action rules
action, the most recent action, initially none
state←UPDATE-STATE(state, action, percept ,model )
rule←RULE-MATCH(state, rules)
action ←rule.ACTION
return action
• A model-based reflex agent. It keeps track of the current state of the world, using an
internal model. It then chooses an action in the same way as the reflex agent.
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Model-based reflex agent
Unit-1 Introduction to AI ML
42. Goal-based agents
• The knowledge of the current state environment is not always
sufficient to decide for an agent to what to do.
• The agent needs to know its goal which describes desirable
situations.
• Goal-based agents expand the capabilities of the model-based
agent by having the "goal" information.
• They choose an action, so that they can achieve the goal.
• These agents may have to consider a long sequence of possible
actions before deciding whether the goal is achieved or not.
Such considerations of different scenario are called searching
and planning, which makes an agent proactive.
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Unit-1 Introduction to AI ML
44. Utility-based agents
• These agents are similar to the goal-based agent but provide an extra component
of utility measurement which makes them different by providing a measure of
success at a given state.
• Utility-based agent act based not only goals but also the best way to achieve the
goal.
• The Utility-based agent is useful when there are multiple possible alternatives, and
an agent has to choose in order to perform the best action.
• The utility function maps each state to a real number to check how efficiently each
action achieves the goals.
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Unit-1 Introduction to AI ML
45. • A model-based, utility-based agent. It uses a model of the world, along with a utility
function that measures its preferences among states of the world.
• Then it chooses the action that leads to the best expected utility, where expected
utility is computed by averaging over all possible outcome states, weighted by the
probability of the outcome
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Utility-based agents
Unit-1 Introduction to AI ML
47. Learning Agents
• A learning agent in AI is the type of agent which can learn from its past experiences, or
it has learning capabilities.
• It starts to act with basic knowledge and then able to act and adapt automatically
through learning.
• A learning agent has mainly four conceptual components, which are:
• Learning element: It is responsible for making improvements by learning from
environment
• Critic: Learning element takes feedback from critic which describes that how well
the agent is doing with respect to a fixed performance standard.
• Performance element: It is responsible for selecting external action
• Problem generator: This component is responsible for suggesting actions that will
lead to new and informative experiences.
• Hence, learning agents are able to learn, analyze performance, and look for new ways
to improve the performance.
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Unit-1 Introduction to AI ML
50. Problem SolvingApproach to Typical AI problems
■ The reflex agent of AI directly maps states into action.
■ Whenever these agents fail to operate in an environment where the state of
mapping is too large and not easily performed by the agent, then the stated
problem dissolves and sent to a problem-solving domain which breaks the
large stored problem into the smaller storage area and resolves one by
one.
■ The final integrated action will be the desired outcomes.
■ On the basis of the problem and their working domain, different types of
problem-solving agent defined and use at an atomic level without any
internal state visible with a problem-solving algorithm.
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51. Problem SolvingApproach to Typical AI problems
■ The problem-solving agent performs precisely by defining problems and
several solutions.
■ So we can say that problem solving is a part of artificial intelligence that
encompasses a number of techniques such as a tree, B-tree, heuristic
algorithms to solve a problem.
■ We can also say that a problem-solving agent is a result-driven agent and
always focuses on satisfying the goals.
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52. ■ Steps problem-solving in AI: The problem of AI is directly associated with the
nature of humans and their activities. So we need a number of finite steps to solve
a problem which makes human easy works.
■ These are the following steps which require to solve a problem :
■ Goal Formulation: This one is the first and simple step in problem-solving. It
organizes finite steps to formulate a target/goals which require some action to
achieve the goal. Today the formulation of the goal is based on AI agents.
■ Problem formulation: It is one of the core steps of problem-solving which
decides what action should be taken to achieve the formulated goal. In AI this core
part is dependent upon software agent which consisted of the following
components to formulate the associated problem.
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Problem Solving Approach to Typical AI problems
53. ■ Components to formulate the associated problem:
■ Initial State: This state requires an initial state for the problem which starts the AI
agent towards a specified goal. In this state new methods also initialize problem
domain solving by a specific class.
■ Action: This stage of problem formulation works with function with a specific
class taken from the initial state and all possible actions done in this stage.
■ Transition: This stage of problem formulation integrates the actual action done by
the previous action stage and collects the final stage to forward it to their next
stage.
■ Goal test: This stage determines that the specified goal achieved by the integrated
transition model or not, whenever the goal achieves stop the action and forward
into the next stage to determines the cost to achieve the goal.
■ Path costing: This component of problem-solving numerical assigned what will
be the cost to achieve the goal. It requires all hardware software and human
working cost.
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Problem SolvingApproach to Typical AI problems
