Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. Recently, artificial neural networks have been able to surpass many previous approaches in performance.
2. Artificial Intelligence
► Artificial Intelligence (AI) is a branch of computer science focused on
creating systems that can perform tasks that typically require human
intelligence.
► These tasks include things like learning, reasoning, problem-solving,
perception, language understanding, and decision-making.
► AI systems can be designed to analyze large amounts of data,
recognize patterns, adapt to new inputs, and improve over time.
► They utilize techniques such as machine learning, natural language
processing, computer vision, and robotics to achieve these capabilities.
4. AI Problems:-
1. Machine Learning Problems:-
► Classification: Assigning categories or labels to input data based on past
observations.
► Regression: Predicting continuous outcomes based on input variables.
► Clustering: Grouping similar data points together based on their
characteristics.
► Dimensionality Reduction: Reducing the number of input variables while
preserving important information.
► Anomaly Detection: Identifying unusual patterns or outliers in data.
5. AI Problems:-
2.Natural Language Processing (NLP) Problems:
► Named Entity Recognition (NER): Identifying entities such as names of people,
organizations, and locations in text.
► Sentiment Analysis: Determining the sentiment or opinion expressed in text data.
► Language Translation: Translating text from one language to another while
preserving meaning and context.
► Text Generation: Generating coherent and contextually relevant text, such as in
chatbots or language models.
► Question Answering: Understanding and answering questions posed in natural
language.
6. AI Problems:-
3.Computer Vision Problems:
► Object Detection: Identifying and localizing objects within an
image or video.
► Image Classification: Assigning labels to images based on their
contents.
► Semantic Segmentation: Assigning a label to each pixel in an
image, categorizing regions of the image into different classes.
► Image Generation: Generating new images based on given
constraints or input.
7. AI Problems:-
4 Reinforcement Learning Problems:
► Policy Learning: Learning a policy or strategy to maximize
cumulative rewards in a dynamic environment.
► Value Estimation: Estimating the value of different actions or
states in a given environment.
► Exploration vs. Exploitation: Balancing between exploring new
actions and exploiting known actions to optimize learning.
8. AI Problems:-
5.Robotics Problems:
► Navigation: Planning and executing paths for robots to navigate
in complex environments.
► Manipulation: Grasping and manipulating objects with dexterity
and precision.
► Localization and Mapping: Estimating the robot's position and
creating maps of its surroundings.
9. AI Problems:-
6.Ethical and Societal Problems:
► Bias and Fairness: Addressing biases in AI systems that may lead to unfair
treatment of certain groups.
► Privacy: Protecting sensitive information and ensuring user privacy in AI
systems.
► Explainability: Making AI systems interpretable and transparent, so users can
understand how decisions are made.
► Safety and Security: Ensuring the safety and security of AI systems to prevent
malicious use or unintended consequences.
10. Problem Formulation:-
► key steps involved in problem formulation in AI:
1) Problem Identification: The first step is to identify the problem
that needs to be solved. This may involve analyzing a real-world
situation or task and determining what aspects of it can be
automated or improved using AI techniques.
2) State Space Representation: Once the problem is identified, it
needs to be represented in terms of states, actions, and
transitions. The state space includes all possible configurations
or states that the problem can be in, and actions represent the
possible moves or changes that can be made from one state to
another.
11. Problem Formulation:-
3) Initial State: The problem formulation should specify the initial state
from which the solution process begins. This is the starting point of the
problem-solving process.
4) Goal State(s): Clearly define the goal or goals that the AI system is
trying to achieve. These are the states that the system is aiming to reach.
5) Actions: Specify the set of actions that the AI system can take in each
state. These actions should be relevant to the problem and should lead to
progress towards the goal states.
6) Transition Model: Define the rules or functions that describe how the
system's state changes when it takes an action. This transition model
determines the new state resulting from performing a particular action in a
given state.
12. Problem Formulation:-
7) Cost Function (if applicable): In some problems, there may be a
cost associated with each action. If so, a cost function should be defined
to quantify the cost of reaching each state. The goal may then be to
minimize the total cost of reaching the goal state.
8) Constraints: Identify any constraints or limitations on the
problem-solving process. These could include restrictions on the actions
that can be taken, limitations on resources such as time or memory, or
other external factors.
9) Environment Modeling (if applicable): If the problem involves
interaction with an environment, the environment needs to be modeled
to understand how it responds to the system's actions.
13. Intelligent Agents:-
► Intelligent agents in artificial intelligence are autonomous entities
that perceive their environment, make decisions, and take actions
to achieve specific goals.
► They are typically designed to operate in dynamic and uncertain
environments, using various techniques such as machine learning,
expert systems, and decision-making algorithms to adapt and
improve their performance over time.
► Examples of intelligent agents include chatbots, autonomous
vehicles, and recommendation systems. They are crucial
components in many AI applications, performing tasks ranging
from simple data retrieval to complex problem-solving.
14. Types of Agents:-
► 1. Simple Reflex Agents: These agents base their actions solely on
the current percept, without considering past percepts or future
consequences. They follow predefined rules or mappings from
percepts to actions.
15. Types of Agents:-
► 2.Model-Based Reflex Agents: These agents maintain an internal
model of the environment and use it to make decisions. They
consider both the current percept and the internal model to
determine their actions.
16. Types of Agents:-
► 3. Goal-Based Agents: Goal-based agents have specific goals to
achieve. They consider the current state of the environment, their
goals, and possible actions to take in order to move closer to
achieving those goals. Planning algorithms are often used by
these agents.
17. Types of Agents:
4. Utility-Based Agents: Utility-based agents evaluate the expected
utility or value of different actions and choose the action with the
highest expected utility. They consider not only the goals but also
the preferences or utilities associated with different outcomes.
18. Types of Agents:
5. Learning Agents: Learning agents improve their performance over
time by learning from experience. They can use various learning
techniques such as reinforcement learning, supervised learning, or
unsupervised learning to adapt and improve their behavior.
19. Types of Agents:-
6 Hybrid Agents: Hybrid agents combine characteristics from
multiple types of agents. For example, a hybrid agent may
incorporate both goal-based reasoning and learning algorithms to
achieve its objectives more effectively.
20. Agent Environment
► In artificial intelligence, the concept of an agent-environment
framework is fundamental to understanding how autonomous
entities interact with their surroundings.
► The environment is everything that surrounds the agent and with
which the agent interacts. It includes physical entities, such as
objects in the world, as well as abstract elements like data or the
state of a game. The environment provides inputs to the agent
through sensors and receives outputs from the agent through
actuators.
21. Agent Environment
► The interaction between the agent and its environment can be described as a continual cycle:
► 1.Perception: The agent perceives the environment through its sensors. This involves collecting data or
information about the state of the environment.
► 2. Processing: The agent processes the perceptual information it receives, often using internal
knowledge or algorithms to interpret the data and make decisions.
► 3. Action: Based on its perception and processing, the agent selects and performs actions using its
actuators. These actions can influence the state of the environment.
► 4. Feedback: After taking action, the environment may change in response. This change is perceived
by the agent, completing the cycle, and potentially leading to a new perceptual input.
► This cyclic interaction forms the basis of the agent-environment framework in artificial intelligence.
Understanding this framework helps in designing and analyzing intelligent systems, as it provides a
structured way to model and reason about how agents operate within their environments to achieve
specific goals or tasks.
22. Types of environments
► 1. Fully Observable vs. Partially Observable: In a fully observable environment, the agent's sensors can perceive the complete state
of the environment at any given time. In contrast, a partially observable environment only provides the agent with incomplete or
limited information about the environment's state.
► 2. Deterministic vs. Stochastic: In a deterministic environment, the next state of the environment is completely determined by the
current state and the actions of the agent. In a stochastic environment, there is uncertainty or randomness in the outcome of actions,
leading to multiple possible outcomes for each action.
► 3. Episodic vs. Sequential: In an episodic environment, the agent's actions are divided into distinct episodes, with each episode
separated from others and having no influence on subsequent episodes. In a sequential environment, the agent's actions have
long-term consequences, and the current state depends on the agent's past actions.
► 4. Static vs. Dynamic: In a static environment, the environment does not change while the agent is deliberating. In a dynamic
environment, the environment can change independently of the agent's actions, requiring the agent to continuously adapt to new
information.
► 5. Discrete vs. Continuous: In a discrete environment, there are a finite number of possible states and actions. In a continuous
environment, the state space and action space are continuous, making it more challenging to represent and navigate.
► 6. Single-Agent vs. Multi-Agent: In a single-agent environment, there is only one agent interacting with the environment. In a
multi-agent environment, there are multiple agents, each with its own goals and actions, interacting with each other and the
environment.
23. PEAS
► 1. Performance measure: This describes the criteria used to evaluate the success of the agent's behavior. It specifies
what the agent is trying to achieve or optimize. For example, in a vacuum cleaning agent, the performance measure
could be the cleanliness of the environment or the time taken to clean the entire area.
► 2. Environment: This defines the context in which the agent operates. It includes everything that the agent can
perceive and interact with. The environment can be described based on various factors such as its observability,
determinism, dynamics, and discrete or continuous nature. For instance, in the case of a self-driving car, the
environment includes the road, other vehicles, traffic signals, pedestrians, etc.
► 3. Actuators: These are the mechanisms or tools through which the agent can affect the environment. Actuators enable
the agent to take actions based on its perception of the environment. Examples of actuators include motors, arms,
wheels, etc. In the context of a robotic arm, the actuators could be the motors that control the movement of the arm's
joints.
► 4. Sensors: These are the means through which the agent perceives its environment. Sensors provide information
about the state of the environment, allowing the agent to make decisions and take appropriate actions. Sensors can
range from simple devices like cameras and microphones to more complex sensors like lidars and gyroscopes. In a
smart home environment, sensors could include temperature sensors, motion detectors, and cameras.
24. PEAS representation of an Agent
Example :- PEAS representation for an autonomous vacuum cleaner agent:-
► Performance Measure:
The performance measure could be defined as the cleanliness of the floor, measured by the
percentage of dirt and dust removed.
► Environment:
The environment consists of a room with furniture, walls, carpets, and other obstacles. It may also
include sources of dirt like crumbs, dust, and pet hair.
► Actuators:
The actuators for the vacuum cleaner agent include the wheels for movement, the vacuum suction
mechanism, and sensors for detecting obstacles.
► Sensors:
The sensors include bump sensors to detect collisions with furniture or walls, dirt sensors to detect
areas with dirt, and floor sensors to determine the type of surface (e.g., carpet, hardwood).
25. PEAS representation of an Agent
With this PEAS representation:
► Performance measure: Percentage of dirt and dust removed from
the floor.
► Environment: Room with furniture, walls, carpets, and dirt.
► Actuators: Wheels for movement, vacuum suction mechanism,
and sensors.
► Sensors: Bump sensors, dirt sensors, and floor sensors.
26. Reasoning and logic
► Reasoning and logic play a crucial role in artificial intelligence (AI) as they form the
basis for decision-making, problem-solving, and inference. Various forms of reasoning
and logic are utilized in AI systems to represent knowledge, draw conclusions, and
make predictions.
► Deductive Reasoning:
► Deductive reasoning involves drawing specific conclusions from general principles or
premises. It follows a "top-down" approach where conclusions logically follow from given
premises.
► Example: In a medical diagnosis system, if the system knows that "All humans are mortal"
and "Socrates is a human," it can deduce that "Socrates is mortal."
► Inductive Reasoning:
► Inductive reasoning involves generalizing specific observations to form general principles or
hypotheses. It follows a "bottom-up" approach where conclusions are inferred based on
observed patterns.
► Example: An AI system analyzing historical weather data may induce that "If it's cloudy,
there's a higher chance of rain" based on observations of past weather patterns.
27. Reasoning and logic
► Abductive Reasoning:
► Abductive reasoning involves inferring the best explanation or hypothesis for observed evidence. It is
used when multiple hypotheses could explain the same evidence, and the goal is to find the most
plausible explanation.
► Example: In a diagnostic system, given symptoms like fever and cough, the system might abduce that
the patient likely has a respiratory infection such as the flu.
► Logical Reasoning:
► Logical reasoning involves using formal rules of inference to derive conclusions from given premises
using symbolic representations such as propositional logic or predicate logic.
► Example: In a planning system, the AI might use logical inference rules to determine the sequence of
actions required to achieve a goal while adhering to constraints and preconditions.
► Probabilistic Reasoning:
► Probabilistic reasoning involves reasoning under uncertainty, where the AI assigns probabilities to
various hypotheses or outcomes based on available evidence.
► Example: In a spam email filter, the AI may use probabilistic reasoning to calculate the likelihood of
an email being spam based on features such as the presence of certain keywords or sender reputation.
28. Reasoning and logic :-Example
► Consider an AI-based diagnostic system for diagnosing diseases based on
patient symptoms. The system uses logical reasoning to infer possible diseases
from observed symptoms. For instance:
► Premise 1: If a patient has a fever and cough, the system infers using logical
rules that the patient might have either the flu or pneumonia.
► Premise 2: If the patient has a runny nose and sneezing, the system infers that
the patient might have allergies.
Based on these premises and additional information about the patient's symptoms,
the system deduces the most likely diagnosis using logical inference rules, helping
physicians in making informed decisions about patient care.
29. Propositional logic
► Propositional logic, also known as propositional calculus or sentential logic, is a branch of
formal logic that deals with propositions (statements) and their logical relationships. In
propositional logic, propositions are atomic units that can either be true or false. Logical
operators are used to combine propositions to form compound propositions, allowing for the
expression of more complex statements:-
basic components of propositional logic:
► Propositions: Atomic statements that can be either true or false. Propositions are usually
represented by capital letters such as P, Q, or R.
► Logical Connectives: Operators used to combine propositions to form compound propositions.
The main logical connectives in propositional logic are:
► Conjunction (∧∧): Represents logical AND. It is true only when both propositions are true.
► Disjunction (∨∨): Represents logical OR. It is true if at least one of the propositions is true.
► Negation (¬¬): Represents logical NOT. It negates the truth value of a proposition.
► Implication (→→): Represents logical implication. It asserts that if the antecedent is true, then the
consequent must also be true.
► Biconditional (↔↔): Represents logical equivalence. It is true if both propositions have the same
truth value.
► Truth Tables: Tables used to determine the truth value of compound propositions based on the
truth values of their component propositions.
30. Propositional logic:-Examples:-
► Let's consider a simple example to illustrate propositional logic:
P: It is raining.
Q: I will take an umbrella.
► We can express logical relationships between these propositions
using logical connectives:
► If it is raining (P), then I will take an umbrella (Q). This can be
represented as P→Q.
► If it is not raining (¬P), then I will not take an umbrella (¬Q).
This can be represented as →¬P→¬Q.
32. Propositional logic Examples:-
►
Certainly! Here are a few more examples of propositions and compound propositions in
propositional logic:
P: It is sunny.
Q: I will go for a picnic.
► Compound Propositions:
► P∧Q: It is sunny and I will go for a picnic.
► P∨Q: It is sunny or I will go for a picnic.
► ¬P: It is not sunny.
► P→Q: If it is sunny, then I will go for a picnic.
► P: The store is open. Q: I can buy groceries.
► Compound Propositions:
► P∧Q: The store is open and I can buy groceries.
► P∨Q: The store is open or I can buy groceries.
► ¬P: The store is closed.
► P→Q: If the store is open, then I can buy groceries.
33. Propositional logic Examples:-
P: It is Monday.
Q: I have a meeting.
► Compound Propositions:
► P∧Q: It is Monday and I have a meeting.
► P∨Q: It is Monday or I have a meeting.
► ¬P: It is not Monday.
► P→Q: If it is Monday, then I have a meeting.
► P: The traffic is heavy.
► Q: I will be late for work.
► Compound Propositions:
► P∧Q: The traffic is heavy and I will be late for work.
► P∨Q: The traffic is heavy or I will be late for work.
► ¬P: The traffic is not heavy.
► P→Q: If the traffic is heavy, then I will be late for work.
34. First-order logic (FOL)
► First-order logic (FOL), also known as predicate logic or first-order predicate calculus, is a
formal system used in artificial intelligence and mathematics to represent knowledge and make
inferences about the world. It extends propositional logic by introducing quantifiers and
variables, allowing for the representation of complex relationships and the expression of more
nuanced statements.
Here are the key components of first-order logic:
► Constants: Represent specific objects or entities in the domain of discourse. For example,
"John" could be a constant representing a person.
► Variables: Represent unspecified objects in the domain of discourse. Variables are
placeholders that can be replaced by constants or other terms. Common variable names include
x, y, z, etc.
► Predicates: Represent properties or relations that can be true or false of objects in the domain
of discourse. Predicates are symbols that take arguments (constants, variables, or other terms).
For example IsHuman(x) could represent the predicate "x is human."
► Functions: Represent operations that return values. Functions take arguments (constants,
variables, or other terms) and produce a result. For example, Add(x,y) could represent the
function "addition."
35. First-order logic (FOL)
► Quantifiers: Specify the scope of variables in logical statements. The two
main quantifiers in first-order logic are:
Universal Quantifier (∀∀): Asserts that a statement holds for all objects in the
domain of discourse. For example, ∀x IsHuman(x) means "everyone is human."
Existential Quantifier (∃∃): Asserts that there exists at least one object in the
domain of discourse for which a statement holds. For example,∃x IsHuman(x)
means "there exists a human."
37. Using First Order Logic(FOL)
► First-order logic (FOL) is extensively used in artificial intelligence (AI) for
representing knowledge, specifying rules, defining relationships, and making
inferences. Here are several ways in which FOL is applied in AI:
► Knowledge Representation: FOL provides a structured and formal way to
represent knowledge about the world. AI systems use FOL to encode facts,
rules, and relationships within a domain. For example, in a medical diagnosis
system, FOL can represent knowledge about diseases, symptoms, and
treatments.
► Inference and Reasoning: FOL allows AI systems to perform logical
inference and reasoning. By applying deduction, induction, abduction, and
other forms of logical reasoning, AI systems can draw conclusions, make
predictions, and answer queries based on the encoded knowledge. For instance,
in a planning system, FOL can be used to infer the sequence of actions required
to achieve a goal while satisfying various constraints.
38. Using First Order Logic(FOL)
► Expert Systems: Expert systems are AI systems that emulate the
decision-making ability of human experts in specific domains. FOL is often
used to encode the expertise and reasoning patterns of human experts into a
knowledge base. Expert systems use FOL to reason about the problem domain
and provide recommendations or solutions.
► Natural Language Processing (NLP): FOL plays a crucial role in natural
language understanding and generation tasks in NLP. Semantic parsing
techniques are used to translate natural language sentences into FOL
expressions, allowing AI systems to understand the meaning of text and extract
relevant information. Conversely, FOL can be used to generate natural
language responses based on logical inferences.
39. Using First Order Logic(FOL)
► Automated Reasoning: FOL serves as the foundation for automated reasoning systems, which
aim to automatically derive new knowledge from existing knowledge bases. Automated theorem
proving and model checking techniques use FOL to verify the correctness of logical statements,
prove theorems, or search for counterexamples.
► Database Querying: FOL is used in database systems to formulate queries and retrieve
information from databases. Query languages such as SQL (Structured Query Language) are
based on FOL principles, allowing users to express complex queries involving logical conditions,
conjunctions, disjunctions, and quantifiers.
► Semantic Web: FOL is employed in the Semantic Web to represent and reason about the meaning
of web resources. Semantic web languages like RDF (Resource Description Framework) and
OWL (Web Ontology Language) are based on FOL, enabling the creation of ontologies and the
exchange of structured data on the web.
In summary, first-order logic is a fundamental tool in artificial intelligence, facilitating knowledge
representation, reasoning, inference, and various other AI tasks across different domains. Its
expressive power and formal semantics make it a versatile framework for building intelligent
systems.
40. Inference in First Order logic
► The process of deriving new logical conclusions from existing statements or premises
using logical rules of deduction. First-order logic, also known as predicate logic,
provides a formal framework for representing relationships between objects and making
deductions about them.
► How inference works in first-order logic within AI:
1. Representation of Knowledge: First-order logic allows AI systems to represent
knowledge in terms of predicates, functions, constants, and variables. Predicates
represent properties or relations, functions represent operations, constants represent
specific objects, and variables allow for generalization.
2. Logical Rules: Inference in first-order logic relies on logical rules such as modus
ponens, universal instantiation, existential instantiation, and resolution. These rules
dictate how new statements can be derived from existing ones.
3. Unification: Before inference can occur, variables in different statements must be
unified, i.e., instantiated with appropriate constants or other variables, so that they can
be properly matched.
41. Inference in First Order logic
1. Application of Rules: Using the rules of inference, the AI system applies logical deductions to the
existing knowledge base. For example, modus ponens states that if you have a statement "If A then
B" and you have "A," you can infer "B."
2. Resolution: Resolution is a key inference rule used in first-order logic for proving the validity of
statements. It involves finding contradictions between clauses and resolving them to derive new
conclusions.
3. Forward Chaining and Backward Chaining: These are common inference strategies in AI.
Forward chaining starts with the known facts and applies inference rules repeatedly until the
desired conclusion is reached. Backward chaining starts with the goal to be proven and works
backward through the implications until it finds support from known facts.
4. Soundness and Completeness: An inference process is considered sound if it always produces
true conclusions when starting from true premises. Completeness means that if a conclusion can be
logically derived from the premises, the inference process will eventually find it.
42. Example:-
► Consider the following premises:
1. All humans are mortal. ( ∀x Human(x) → Mortal(x) )
2. Socrates is human. ( Human(Socrates) )
► Now, let's infer a conclusion using these premises:
► Premise 1: All humans are mortal. Premise 2: Socrates is human.
► Now, we want to infer whether Socrates is mortal using these premises. We'll use the rule of Universal
Instantiation to make this inference.
► Universal Instantiation allows us to instantiate a universally quantified statement for a specific individual. In
this case, we'll instantiate the variable x with the constant Socrates.
► So, from Premise 1 ( ∀x Human(x) → Mortal(x) ) and Premise 2 ( Human(Socrates) ), we can derive:
► Mortal(Socrates)
► This conclusion asserts that Socrates is mortal, and we've derived it from the given premises using first-order
logic inference.
► In this example, inference in first-order logic allows us to deduce new information (that Socrates is mortal) from
existing knowledge (that all humans are mortal and that Socrates is human) using logical rules.
43. forward chaining and backward chaining:-
► Forward chaining and backward chaining are two common strategies used in
artificial intelligence for automated reasoning, particularly in the context of expert
systems and rule-based systems.
► Forward Chaining:
► Forward chaining starts with the available data (facts) and applies inference rules to
derive new conclusions until the desired goal is reached.
► Example: Medical Diagnosis System
► Suppose we have a medical diagnosis system with the following rules:
1. If a patient has a fever and cough, they might have the flu.
2. If a patient has a runny nose and sneezing, they might have a cold.
3. If a patient has the flu, they should take antiviral medication.
4. If a patient has a cold, they should take over-the-counter cold medicine.
44. Forward Chaining:
► Let's say the system starts with the fact that a patient has a fever and cough.
Using forward chaining, it applies the rules to derive further conclusions:
1. Fever ∧ Cough → Flu
2. Flu → Antiviral Medication
► So, the system infers that the patient might have the flu and should take
antiviral medication.
45. Backward Chaining::-
► Backward chaining starts with a goal (desired conclusion) and works backward through the inference rules to
determine if the goal can be satisfied by the available data (facts).
► Example: Planning a Trip
► Suppose we want to plan a trip to the beach, but we need to make sure the weather is suitable. We have the following
rules:
1. If the weather forecast predicts sunny skies, we'll go to the beach.
2. If it's raining, we'll stay home.
► We start with the goal of going to the beach. Then, using backward chaining, we check if we can satisfy this goal:
► Goal: Go to the beach
1. Go to the beach ← Sunny skies
► Since the goal depends on having sunny skies, we need to check if the weather forecast predicts sunny skies. If it
does, we can conclude that we'll go to the beach. Otherwise, we'll stay home.
► In both strategies, the system iterates through the available data and inference rules to reach conclusions. Forward
chaining starts with the data and works toward the goal, while backward chaining starts with the goal and works
backward to the data. These strategies are fundamental in various AI applications, including expert systems,
automated planning, and decision-making systems.
