Artificial Intelligence Search and Knowledge Systems

Posted on Jul 14, 2026 in Computer Engineering

Heuristic Search and Hill Climbing Algorithms

A Heuristic Search is a search technique that uses heuristic information (rules or estimates) to find a solution faster than uninformed search methods.

A heuristic function is written as: h(n) = Estimated cost from node n to the goal.

Hill Climbing is a heuristic search algorithm that continuously moves toward the neighboring state with the highest value (better solution) until no better neighbor exists.

It is called “hill climbing” because it always tries to climb toward the highest point. The process follows these steps:

  1. Start with an initial solution.
  2. Evaluate neighboring solutions.
  3. Move to the best neighbor.
  4. Repeat until no improvement is possible.

Semantic Networks for Knowledge Representation

A Semantic Net is a structured knowledge representation technique in Artificial Intelligence in which knowledge is represented as a graph. The nodes represent objects or concepts, and the links represent relationships between them, such as IS-A, HAS-A, or PART-OF. It helps AI systems organize, store, and infer knowledge efficiently. For example: Animal → IS-A → Bird → Can Fly.

Well-Formed Formulas (WFF) in Propositional Logic

A Well-Formed Formula (WFF) follows specific construction rules:

  • Every propositional variable (P, Q, R, etc.) is a WFF.
  • If A is a WFF, then ¬A (NOT A) is also a WFF.
  • If A and B are WFFs, then the following are also WFFs:
    • A ∧ B (AND)
    • A ∨ B (OR)
    • A → B (IMPLIES)
    • A ↔ B (IF AND ONLY IF)

Parentheses are used to avoid ambiguity.

Expert Systems: Components and Workflow

An Expert System is an Artificial Intelligence (AI) program that mimics the decision-making ability of a human expert. It uses a knowledge base and an inference engine to solve problems and provide advice or decisions in a specific domain.

Core Components

  1. Knowledge Base
  2. Inference Engine
  3. User Interface

System Working Process

  1. The user enters a problem or query.
  2. The system checks the knowledge base.
  3. The inference engine applies logical rules.
  4. The system finds the best solution or recommendation.
  5. The result is displayed to the user.

Bayesian Networks and Probabilistic Models

A Bayesian Network (BN) is a probabilistic graphical model used in Artificial Intelligence to represent uncertain knowledge. It consists of nodes (random variables) and directed edges (arrows) that show the probabilistic dependency between variables.

Fundamentals of Robotics and Engineering

Robotics is the branch of Artificial Intelligence and engineering that deals with the design, construction, programming, operation, and maintenance of robots to perform tasks automatically or with minimal human intervention.

The basic aspects of robotics are sensing, actuation, control system, programming (intelligence), power supply, and communication. These components enable a robot to perceive its environment, make decisions, move, and perform tasks efficiently.

Knowledge Representation Techniques

Knowledge Representation (KR) is the process of representing knowledge in a form that a computer can understand, store, and use for reasoning and decision-making.

Types of Knowledge Representation

  1. Logical Representation
  2. Semantic Network
  3. Frames
  4. Production Rules (IF–THEN)

State Space Search and Problem Solving

State Space Search is a problem-solving technique in Artificial Intelligence in which all possible states of a problem are represented as a graph or tree. The AI system searches from the initial state to the goal state by applying operators (actions).

Applications

  • Robot navigation
  • Puzzle solving (8-puzzle)