VTU Module-1 | Introduction

18CS71 | ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING | Module-1 VTU Notes

Artificial Intelligence (AI):

Artificial Intelligence (AI) is a multidisciplinary field of computer science that focuses on creating intelligent machines capable of performing tasks that typically require human intelligence. These tasks include learning, reasoning, problem-solving, perception, language understanding, and even decision-making. AI aims to develop systems that can mimic cognitive functions, allowing them to adapt and improve over time.

Problems, Problem Spaces, and Search:

In AI, problem-solving is a fundamental aspect. Problems are defined as a gap between the current state and the desired goal. Problem spaces represent the possible states and actions that can be taken to move from one state to another. The process of finding a solution involves search algorithms, which explore the problem space to identify a sequence of actions leading to the desired outcome.

Heuristic Search Techniques:

Heuristic search techniques are strategies used in AI to navigate problem spaces more efficiently. Heuristics are rules of thumb or guiding principles that help algorithms prioritize certain paths over others during the search process. Unlike exhaustive searches, which explore every possible solution, heuristic search methods aim to reduce the search space and focus on the most promising options.

Common heuristic search techniques include:

1. Greedy Best-First Search: Selects the path that appears to be the most promising based on a heuristic evaluation function, without considering the entire solution space.
2. A Search Algorithm: Balances the cost of reaching a state with the estimated cost to reach the goal, incorporating both the actual cost and a heuristic evaluation.
3. Simulated Annealing: Inspired by the annealing process in metallurgy, this technique allows the algorithm to explore less promising paths with a decreasing probability over time, preventing it from getting stuck in local optima.
4. Genetic Algorithms: Inspired by natural selection, genetic algorithms use principles of evolution to iteratively generate and refine potential solutions to a problem.

Heuristic search techniques play a crucial role in optimizing AI algorithms, allowing them to efficiently navigate complex problem spaces and find near-optimal solutions in a reasonable amount of time. As AI continues to advance, the development and refinement of heuristic search methods remain essential for enhancing the problem-solving capabilities of intelligent systems.