Artificial Intelligence: Search Algorithms

Artificial intelligence (AI) is transforming various fields, from facial recognition to game playing. A core aspect of AI is its ability to search for solutions to complex problems. This article explores AI search techniques, which enable computers to navigate mazes, solve puzzles, and play games.

Defining the Search Problem

AI search problems consist of several key components:

Agent: An entity that perceives and acts within its environment, such as a robot or a software program.
State: The agent’s configuration and environment at a given time.
Initial State: The starting point of the search.
Actions: Possible moves the agent can make in a given state.
Transition Model: Defines how actions change the state.
State Space: All possible states reachable from the initial state.
Goal Test: Determines whether a state is the goal.
Path Cost: A numerical measure of the cost to move through different states.
Solution: A sequence of actions that leads to the goal.
Optimal Solution: The solution with the lowest path cost.

The Role of Nodes in Search

AI search algorithms track key information using nodes, which store:

State: The state represented by the node.
Parent: The node that generated the current node (for backtracking).
Action: The action taken to reach this node.
Path Cost: The cost incurred from the initial state to this node.

The Search Algorithm: Step-by-Step

Start with a frontier containing the initial state.
Repeat the following steps:
- If the frontier is empty, there is no solution.
- Remove a node from the frontier.
- If it contains the goal state, the search ends.
- Expand the node by considering all valid actions and add the new nodes to the frontier.
To prevent infinite loops, maintain an explored set of visited nodes and only add unvisited nodes to the frontier.

Types of Search Algorithms

Different strategies exist for exploring the search space.

1 Uninformed Search Algorithms

Depth-First Search (DFS): Explores as deeply as possible before backtracking (LIFO stack structure).
Breadth-First Search (BFS): Explores all nodes at the current level before moving deeper (FIFO queue structure).

2 Informed Search Algorithms

Greedy Best-First Search (GBFS): Uses a heuristic function h(n) to estimate the distance to the goal.
A Search*: Uses g(n) + h(n), where:
- g(n) = Cost from the initial state to node n.
- h(n) = Estimated cost from n to the goal.
- For A* to be optimal, the heuristic must be admissible (never overestimates cost) and consistent.

3 Adversarial Search

The Minimax AlgorithmIn games involving two opposing players, Minimax determines the best moves by:

Assigning numerical values to game states.
Evaluating actions for MAX (who seeks the highest score) and MIN (who minimises MAX’s score).
Using a player function, actions, a transition model, and a utility function to simulate game play.

4 Optimisation: Alpha-Beta Pruning

To improve efficiency, alpha-beta pruning reduces the number of nodes evaluated by ignoring unpromising branches, allowing faster decision-making.

5 Evaluation Functions

For complex games where exploring all possible moves is impractical, Minimax uses an evaluation function to estimate utility based on non-terminal game states.

Conclusion

Search algorithms are essential in AI, enabling intelligent systems to navigate problems, solve puzzles, and play competitive games. Understanding different search strategies allows us to develop AI that can reason efficiently and make optimal decisions.