Artificial Intelligence with PHP
  • Getting Started
    • Introduction
    • Audience
    • How to Read This Book
    • Glossary
    • Contributors
    • Resources
    • Changelog
  • Artificial Intelligence
    • Introduction
    • Overview of AI
      • History of AI
      • How Does AI Work?
      • Structure of AI
      • Will AI Take Over the World?
      • Types of AI
        • Limited Memory AI
        • Reactive AI
        • Theory of Mind AI
        • Self-Aware AI
    • AI Capabilities in PHP
      • Introduction to LLM Agents PHP SDK
      • Overview of AI Libraries in PHP
    • AI Agents
      • Introduction to AI Agents
      • Structure of AI Agent
      • Components of AI Agents
      • Types of AI Agents
      • AI Agent Architecture
      • AI Agent Environment
      • Application of Agents in AI
      • Challenges in AI Agent Development
      • Future of AI Agents
      • Turing Test in AI
      • LLM AI Agents
        • Introduction to LLM AI Agents
        • Implementation in PHP
          • Sales Analyst Agent
          • Site Status Checker Agent
    • Theoretical Foundations of AI
      • Introduction to Theoretical Foundations of AI
      • Problem Solving in AI
        • Introduction
        • Types of Search Algorithms
          • Comparison of Search Algorithms
          • Informed (Heuristic) Search
            • Global Search
              • Beam Search
              • Greedy Search
              • Iterative Deepening A* Search
              • A* Search
                • A* Graph Search
                • A* Graph vs A* Tree Search
                • A* Tree Search
            • Local Search
              • Hill Climbing Algorithm
                • Introduction
                • Best Practices and Optimization
                • Practical Applications
                • Implementation in PHP
              • Simulated Annealing Search
              • Local Beam Search
              • Genetic Algorithms
              • Tabu Search
          • Uninformed (Blind) Search
            • Global Search
              • Bidirectional Search (BDS)
              • Breadth-First Search (BFS)
              • Depth-First Search (DFS)
              • Iterative Deepening Depth-First Search (IDDFS)
              • Uniform Cost Search (UCS)
            • Local Search
              • Depth-Limited Search (DLS)
              • Random Walk Search (RWS)
          • Adversarial Search
          • Means-Ends Analysis
      • Knowledge & Uncertainty in AI
        • Knowledge-Based Agents
        • Knowledge Representation
          • Introduction
          • Approaches to KR in AI
          • The KR Cycle in AI
          • Types of Knowledge in AI
          • KR Techniques
            • Logical Representation
            • Semantic Network Representation
            • Frame Representation
            • Production Rules
        • Reasoning in AI
        • Uncertain Knowledge Representation
        • The Wumpus World
        • Applications and Challenges
      • Cybernetics and AI
      • Philosophical and Ethical Foundations of AI
    • Mathematics for AI
      • Computational Theory in AI
      • Logic and Reasoning
        • Classification of Logics
        • Formal Logic
          • Propositional Logic
            • Basics of Propositional Logic
            • Implementation in PHP
          • Predicate Logic
            • Basics of Predicate Logic
            • Implementation in PHP
          • Second-order and Higher-order Logic
          • Modal Logic
          • Temporal Logic
        • Informal Logic
        • Semi-formal Logic
      • Set Theory and Discrete Mathematics
      • Decision Making in AI
    • Key Application of AI
      • AI in Astronomy
      • AI in Agriculture
      • AI in Automotive Industry
      • AI in Data Security
      • AI in Dating
      • AI in E-commerce
      • AI in Education
      • AI in Entertainment
      • AI in Finance
      • AI in Gaming
      • AI in Healthcare
      • AI in Robotics
      • AI in Social Media
      • AI in Software Development
      • AI in Adult Entertainment
      • AI in Criminal Justice
      • AI in Criminal World
      • AI in Military Domain
      • AI in Terrorist Activities
      • AI in Transforming Our World
      • AI in Travel and Transport
    • Practice
  • Machine Learning
    • Introduction
    • Overview of ML
      • History of ML
        • Origins and Early Concepts
        • 19th Century
        • 20th Century
        • 21st Century
        • Coming Years
      • Key Terms and Principles
      • Machine Learning Life Cycle
      • Problems and Challenges
    • ML Capabilities in PHP
      • Overview of ML Libraries in PHP
      • Configuring an Environment for PHP
        • Direct Installation
        • Using Docker
        • Additional Notes
      • Introduction to PHP-ML
      • Introduction to Rubix ML
    • Mathematics for ML
      • Linear Algebra
        • Scalars
          • Definition and Operations
          • Scalars with PHP
        • Vectors
          • Definition and Operations
          • Vectors in Machine Learning
          • Vectors with PHP
        • Matrices
          • Definition and Types
          • Matrix Operations
          • Determinant of a Matrix
          • Inverse Matrices
          • Cofactor Matrices
          • Adjugate Matrices
          • Matrices in Machine Learning
          • Matrices with PHP
        • Tensors
          • Definition of Tensors
          • Tensor Properties
            • Tensor Types
            • Tensor Dimension
            • Tensor Rank
            • Tensor Shape
          • Tensor Operations
          • Practical Applications
          • Tensors in Machine Learning
          • Tensors with PHP
        • Linear Transformations
          • Introduction
          • LT with PHP
          • LT Role in Neural Networks
        • Eigenvalues and Eigenvectors
        • Norms and Distances
        • Linear Algebra in Optimization
      • Calculus
      • Probability and Statistics
      • Information Theory
      • Optimization Techniques
      • Graph Theory and Networks
      • Discrete Mathematics and Combinatorics
      • Advanced Topics
    • Data Fundamentals
      • Data Types and Formats
        • Data Types
        • Structured Data Formats
        • Unstructured Data Formats
        • Implementation with PHP
      • General Data Processing
        • Introduction
        • Storage and Management
          • Data Security and Privacy
          • Data Serialization and Deserialization in PHP
          • Data Versioning and Management
          • Database Systems for AI
          • Efficient Data Storage Techniques
          • Optimizing Data Retrieval for AI Algorithms
          • Big Data Considerations
            • Introduction
            • Big Data Techniques in PHP
      • ML Data Processing
        • Introduction
        • Types of Data in ML
        • Stages of Data Processing
          • Data Acquisition
            • Data Collection
            • Ethical Considerations in Data Preparation
          • Data Cleaning
            • Data Cleaning Examples
            • Data Cleaning Types
            • Implementation with PHP
          • Data Transformation
            • Data Transformation Examples
            • Data Transformation Types
            • Implementation with PHP ?..
          • Data Integration
          • Data Reduction
          • Data Validation and Testing
            • Data Splitting and Sampling
          • Data Representation
            • Data Structures in PHP
            • Data Visualization Techniques
          • Typical Problems with Data
    • ML Algorithms
      • Classification of ML Algorithms
        • By Methods Used
        • By Learning Types
        • By Tasks Resolved
        • By Feature Types
        • By Model Depth
      • Supervised Learning
        • Regression
          • Linear Regression
            • Types of Linear Regression
            • Finding Best Fit Line
            • Gradient Descent
            • Assumptions of Linear Regression
            • Evaluation Metrics for Linear Regression
            • How It Works by Math
            • Implementation in PHP
              • Multiple Linear Regression
              • Simple Linear Regression
          • Polynomial Regression
            • Introduction
            • Implementation in PHP
          • Support Vector Regression
        • Classification
        • Recommendation Systems
          • Matrix Factorization
          • User-Based Collaborative Filtering
      • Unsupervised Learning
        • Clustering
        • Dimension Reduction
        • Search and Optimization
        • Recommendation Systems
          • Item-Based Collaborative Filtering
          • Popularity-Based Recommendations
      • Semi-Supervised Learning
        • Regression
        • Classification
        • Clustering
      • Reinforcement Learning
      • Distributed Learning
    • Integrating ML into Web
      • Open-Source Projects
      • Introduction to EasyAI-PHP
    • Key Applications of ML
    • Practice
  • Neural Networks
    • Introduction
    • Overview of NN
      • History of NN
      • Basic Components of NN
        • Activation Functions
        • Connections and Weights
        • Inputs
        • Layers
        • Neurons
      • Problems and Challenges
      • How NN Works
    • NN Capabilities in PHP
    • Mathematics for NN
    • Types of NN
      • Classification of NN Types
      • Linear vs Non-Linear Problems in NN
      • Basic NN
        • Simple Perceptron
        • Implementation in PHP
          • Simple Perceptron with Libraries
          • Simple Perceptron with Pure PHP
      • NN with Hidden Layers
      • Deep Learning
      • Bayesian Neural Networks
      • Convolutional Neural Networks (CNN)
      • Recurrent Neural Networks (RNN)
    • Integrating NN into Web
    • Key Applications of NN
    • Practice
  • Natural Language Processing
    • Introduction
    • Overview of NLP
      • History of NLP
        • Ancient Times
        • Medieval Period
        • 15th-16th Century
        • 17th-18th Century
        • 19th Century
        • 20th Century
        • 21st Century
        • Coming Years
      • NLP and Text
      • Key Concepts in NLP
      • Common Challenges in NLP
      • Machine Learning Role in NLP
    • NLP Capabilities in PHP
      • Overview of NLP Libraries in PHP
      • Challenges in NLP with PHP
    • Mathematics for NLP
    • NLP Techniques
      • Basic Text Processing with PHP
      • NLP Workflow
      • Popular Tools and Frameworks for NLP
      • Techniques and Algorithms in NLP
        • Basic NLP Techniques
        • Advanced NLP Techniques
      • Advanced NLP Topics
    • Integrating NLP into Web
    • Key Applications of NLP
    • Practice
  • Computer Vision
    • Introduction
  • Overview of CV
    • History of CV
    • Common Use Cases
  • CV Capabilities in PHP
  • Mathematics for CV
  • CV Techniques
  • Integrating CV into Web
  • Key Applications of CV
  • Practice
  • Robotics
    • Introduction
  • Overview of Robotics
    • History and Evolution of Robotics
    • Core Components
      • Sensors (Perception)
      • Actuators (Action)
      • Controllers (Processing and Logic)
    • The Role of AI in Robotics
      • Object Detection and Recognition
      • Path Planning and Navigation
      • Decision Making and Learning
  • Robotics Capabilities in PHP
  • Mathematics for Robotics
  • Building Robotics
  • Integration Robotics into Web
  • Key Applications of Robotics
  • Practice
  • Expert Systems
    • Introduction
    • Overview of ES
      • History of ES
        • Origins and Early ES
        • Milestones in the Evolution of ES
        • Expert Systems in Modern AI
      • Core Components and Architecture
      • Challenges and Limitations
      • Future Trends
    • ES Capabilities in PHP
    • Mathematics for ES
    • Building ES
      • Knowledge Representation Approaches
      • Inference Mechanisms
      • Best Practices for Knowledge Base Design and Inference
    • Integration ES into Web
    • Key Applications of ES
    • Practice
  • Cognitive Computing
    • Introduction
    • Overview of CC
      • History of CC
      • Differences Between CC and AI
    • CC Compatibilities in PHP
    • Mathematics for CC
    • Building CC
      • Practical Implementation
    • Integration CC into Web
    • Key Applications of CC
    • Practice
  • AI Ethics and Safety
    • Introduction
    • Overview of AI Ethics
      • Core Principles of AI Ethics
      • Responsible AI Development
      • Looking Ahead: Ethical AI Governance
    • Building Ethics & Safety AI
      • Fairness, Bias, and Transparency
        • Bias in AI Models
        • Model Transparency and Explainability
        • Auditing, Testing, and Continuous Monitoring
      • Privacy and Security in AI
        • Data Privacy and Consent
        • Safety Mechanisms in AI Integration
        • Preventing and Handling AI Misuse
      • Ensuring AI Accountability
        • Ethical AI in Decision Making
        • Regulations & Compliance
        • AI Risk Assessment
    • Key Applications of AI Ethics
    • Practice
  • Epilog
    • Summing-up
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On this page
  • Propositions
  • Syntax of Propositional Logic
  • Logical Connectives
  • Conclusion
  1. Artificial Intelligence
  2. Mathematics for AI
  3. Logic and Reasoning
  4. Formal Logic
  5. Propositional Logic

Basics of Propositional Logic

Propositions

A proposition is a declarative statement that can be either true (T) or false (F), but not both. Examples include:

  • "It is Sunday."

  • "The Sun rises from the West." (False proposition)

  • "3 + 3 = 7." (False proposition)

  • "5 is a prime number." (True proposition)

Propositional logic is based on symbolic variables representing logical statements, typically denoted as A, B, C, P, Q, R, etc.

Syntax of Propositional Logic

The syntax of propositional logic defines the allowable sentences for knowledge representation. There are two types of propositions:

  • Atomic Propositions Atomic propositions are simple propositions consisting of a single proposition symbol. These are sentences that must be either true or false.

    Examples:

    • "2 + 2 = 4" is an atomic proposition as it is a true fact.

    • "The Sun is cold" is also a proposition as it is a false fact.

  • Compound Propositions Compound propositions are constructed by combining simpler or atomic propositions using parentheses and logical connectives. Examples:

    • "It is raining today, and the street is wet."

    • "Ankit is a doctor, and his clinic is in Mumbai."

Logical Connectives

Logical connectives are used to connect two simpler propositions or represent a sentence logically. We can create compound propositions with the help of logical connectives. There are mainly five connectives, which are given as follows:

  • Negation (¬P) – A sentence such as ¬P is called the negation of P. A literal can be either a positive literal or a negative literal.

  • Conjunction (P ∧ Q) – A sentence that has the ∧ connective, such as P ∧ Q, is called a conjunction.

    Example: "Rohan is intelligent and hardworking." It can be written as:

    • P = Rohan is intelligent

    • Q = Rohan is hardworking

    • Representation: P∧ Q

  • Disjunction (P ∨ Q) – A sentence that has the ∨ connective, such as P ∨ Q, is called disjunction, where P and Q are the propositions.

    Example: "Ritika is a doctor or an engineer."

    • P = Ritika is a doctor

    • Q = Ritika is an engineer

    • Representation: P ∨ Q

  • Implication (P → Q) – A sentence such as P → Q is called an implication. Implications are also known as if-then rules.

    Example: "If it is raining, then the street is wet."

    • P = It is raining

    • Q = The street is wet

    • Representation: P → Q

  • Biconditional (P ⇔ Q) – A sentence such as P ⇔ Q is a biconditional sentence.

    Example: "If I am breathing, then I am alive."

    • P = I am breathing

    • Q = I am alive

    • Representation: P ⇔ Q

Summarized Table for Propositional Logic Connectives

Logical Connective
Symbol
Meaning
Example

Negation

¬P

The opposite of P

¬(It is raining) = It is not raining

Conjunction

P ∧ Q

Both P and Q must be true

It is raining ∧ It is cold

Disjunction

P ∨ Q

At least one of P or Q must be true

It is raining ∨ It is cold

Implication

P → Q

If P is true, then Q must also be true

If it rains → The street is wet

Biconditional

P ⇔ Q

P is true if and only if Q is true

I am breathing ⇔ I am alive

Truth Tables for All Logical Connectives

Negation (¬P)

P
¬P

True

False

False

True

Conjunction (P ∧ Q)

P
Q
P ∧ Q

True

True

True

True

False

False

False

True

False

False

False

False

Disjunction (P ∨ Q)

P
Q
P ∨ Q

True

True

True

True

False

True

False

True

True

False

False

False

Implication (P → Q)

P
Q
P → Q

True

True

True

True

False

False

False

True

True

False

False

True

Biconditional (P ⇔ Q)

P
Q
P ⇔ Q

True

True

True

True

False

False

False

True

False

False

False

True

Truth Table with Three Propositions

We can build a proposition composing three propositions P, Q, and R. This truth table consists of 8 rows (2³ combinations of truth values).

P
Q
R
P ∧ (Q ∨ R)

True

True

True

True

True

True

False

True

True

False

True

True

True

False

False

False

False

True

True

False

False

True

False

False

False

False

True

False

False

False

False

False

Precedence of Connectives

Like arithmetic operators, logical connectives have a precedence order that should be followed while evaluating a propositional statement:

  1. Parenthesis (Highest precedence)

  2. Negation (¬)

  3. Conjunction (∧)

  4. Disjunction (∨)

  5. Implication (→)

  6. Biconditional (↔) (Lowest precedence)

For better clarity, parentheses should be used to ensure the correct interpretation of statements.

Logical Equivalence and Properties of Operators

Logical equivalence is a property where two propositions yield the same truth values across all possible scenarios. If two propositions A and B are logically equivalent, they are written as A ⇔ B.

Some essential properties of propositional logic include:

  • Commutativity: P ∧ Q = Q ∧ P, P ∨ Q = Q ∨ P

  • Associativity: (P ∧ Q) ∧ R = P ∧ (Q ∧ R)

  • Identity element: P ∧ True = P, P ∨ True = True

  • Distributivity: P ∧ (Q ∨ R) = (P ∧ Q) ∨ (P ∧ R)

  • De Morgan’s Laws: ¬(P ∧ Q) = ¬P ∨ ¬Q, ¬(P ∨ Q) = ¬P ∧ ¬Q

  • Double Negation: ¬(¬P) = P

Conclusion

Propositional logic provides a structured way to represent and process information in AI. Its ability to model real-world scenarios using logical statements makes it a valuable tool for reasoning, decision-making, and automation. While simple, propositional logic forms the foundation for more advanced logical systems used in AI and machine learning, enabling more sophisticated forms of knowledge representation and reasoning.

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