How AI searches for the perfect answer

Have you ever wondered how an AI finds the fastest route on a map, or why it can defeat human champions at complex games like Chess? The secret lies in sophisticated strategies known as search algorithms. When an AI faces a challenge, it doesn't just guess; it systematically explores a world of possibilities until it fids the goal.

Navigating the search space

Every problem an AI attempts to solve—from scheduling classes to finding the optimal path—exists within a search space. This space contains every single state or combination possible for the given problem.

The main challenge for AI is that these search spaces can be incredibly vast. For complex games like Checkers, the number of possible states is so enormous that attempting to examine every single option—a method known as Brute-Force Search—is computationally impossible. To solve these problems efficiently, AI must employ smarter search techniques.

Uninformed searching: The basic explorer

When an AI has no external knowledge or "hints" about the best direction to go, it uses uninformed search methods, such as:

• Breadth-first search: This method explores the search space level by level. It checks all possible steps one step away, then all possible steps two steps away, and so on, working outward in layers.

• Depth-first search: This is a focused strategy where the algorithm picks one path and explores it as deep as possible until it hits a dead end. Only then does it backtrack to try a different, unexplored path.

Getting smart: The power of heuristics

Since uninformed search can still be too slow for massive search spaces, AI relies on informed search by using heuristics. A heuristic is essentially a smart "rule of thumb" or an educated guess used to estimate how close a particular path is to the final solution.

One simple informed search that uses heuristics is the Greedy search. A Greedy search always chooses the path that looks the best according to the heuristic at the current moment, meaning it prioritises what appears closest to the goal, even if that choice might not lead to the overall shortest path.

The Gold standard: The A* algorithm

The most popular and effective search algorithm is often the A-star (A*) algorithm.

A* is brilliant because it finds the perfect balance by taking two factors into account for every path:

1. Cost (or time): The effort already spent or required to reach the next step.

2. Heuristic estimate: The "smart guess" about the remaining distance from the current point to the final goal.

By adding the accumulated cost and the estimated future cost together, A* reliably finds the best possible path without exhaustively trying every option.

The two main problem categories

AI search strategies are typically applied to two major types of challenges:

1. Optimisation problems: The goal here is to find the single best solution. Examples include finding the fastest delivery route or maximising the profit from a production process.

2. Constraint satisfaction problems: In these cases, the AI only needs to find a solution that satisfies a specific set of rules or constraints. Solving a Sudoku puzzle or creating a fault-free class schedule are classic examples of constraint satisfaction problems.

Understanding these search mechanics shows us that AI isn't magic; it’s a systematic, highly efficient detective, using logic and smart guesses to navigate complicated realities.

Analogy: Think of an AI search algorithm like a kid looking for their lost dog in a new neighborhood.

• Brute Force is ringing every single doorbell on every street until you find the dog.

• A-star is asking a neighbor if they saw which direction the dog went (the heuristic) and also thinking about which streets are easiest to run down (the cost), so you can find the dog quickly and without wasting effort.

Keep exploring!

Ribbit, ribbit,

//Frobo