Heuristic Search in Artificial Intelligence

Heuristic Search in Artificial Intelligence

Heuristic search helps an AI agent make decisions while exploring a problem’s state space. Unlike algorithms such as merge sort or linear search, AI algorithms may need to choose among multiple possible paths rather than follow a fixed procedure.

Main Ideas

• A problem can be represented as a state space: states are connected by transitions, and the goal is to find a path from an initial state to a goal state.
• Each transition may have a cost, such as the time required to move between states.
• A straightforward approach would examine every possible route, add up its costs, and select the cheapest. However, exploring every route can take a long time.
• A heuristic is a calculated guess or shortcut, often informed by prior experience or knowledge. A heuristic function assigns values that help an agent decide which paths to explore.
• By prioritizing some paths and skipping others, heuristic search can reduce the amount of exploration and the time needed to find a solution.
• Heuristics vary by problem. They can make search more efficient, but they do not always lead to an optimal solution.

Illustrative Method

The example uses a simple rule: at each decision point, choose the available transition with the lowest value and ignore the alternatives.

1. Start at the current state and list the possible next transitions.
2. Compare their values using the chosen heuristic.
3. Select the path with the lowest value.
4. Avoid exploring the other paths and any branches that depend on them.
5. Repeat until reaching the goal state.

This rule can reduce exploration, but it may be greedy: choosing the lowest-cost next step does not necessarily produce the lowest total cost across the full route. The tutorial illustrates this with a graph in which a locally preferable choice leads to a more expensive overall route than another option. Heuristic search can therefore be efficient without guaranteeing an optimal answer.

Algorithms and Next Tutorial

The instructor mentions generate-and-test, minimax, and alpha-beta pruning as AI algorithms that can use heuristics to improve efficiency. The next tutorial is described as covering the eight-puzzle problem and the Hamming distance and Manhattan distance heuristics.

Speaker and Source

• One instructor/presenter, speaking for ThinkX Academy. No other speakers or sources are featured in the subtitles.