Heuristic Function in AI (Artificial Intelligence) – Uses and Examples

A heuristic function is an important concept in artificial intelligence (AI) as it is used in problem-solving to ease more complex issues. It is beneficial in providing a basis for making a decision in the search algorithms by estimating the path that is most likely to be correct. This function helps to reduce the time and effort needed to find the best solution, making AI systems faster and more efficient in real-world applications.

In this blog post, we will learn what is a heuristic function and what is its role in search algorithms. You will know what kind of problem it tackles, and what its function is in AI.

What is the Heuristic Function?

The heuristic function allows search algorithms to work efficiently in Artificial Intelligence. It gives out the parameter of a specific node in terms of how close it is to attaining the goal. This parameter helps not to waste time on paths where it is less likely to reach the solution. The algorithm would instead invest time in those paths which have already factored in some form of progress. Rather than examining every option available, the heuristic function is influenced to only use paths that appear to have the highest chance of success given the current situation.

Heuristic functions are essential in various AI applications, especially in problem-solving tasks such as pathfinding, decision-making, and optimization. For example, in navigation systems, a heuristic function might estimate the remaining distance to the destination. Algorithms like A* and Greedy Best-First Search uses heuristic functions to guide their decision-making process. The quality of the heuristic function greatly impacts the efficiency of these algorithms. A good heuristic will provide accurate estimates, speeding up the search for the best solution. On the other hand, a poor heuristic might lead to less efficient searches or incorrect solutions, highlighting the importance of crafting effective heuristics for AI tasks.

A search algorithm is defined as a technique that is used to search the feasible solutions to a problem space in an organised manner. In Artificial Intelligence, search algorithms are fundamental in the accomplishment of the tasks, as they are the ones underlying the evaluation and the selection of possible options. Such algorithms often adhere to effective path-finding, problem-solving, and decision-making techniques.

Search algorithms can broadly be categorised as either uninformed (blind) or class with information, usually known as heuristics. Uninformed algorithms such as Breadth-First Search (BFS) and Depth-First Search (DFS) will try out all the avenues without any clue of the most favourable route. Informed algorithms such as A* and Greedy Best-First Search do search for paths but in addition, they also use information such as heuristics functions to improve their searching efficiency.

There are several determinants for the effectiveness of a search algorithm, more specifically the characteristic of the problem, the formulation of that algorithm, and the presence of available heuristics. In AI, choosing the right search algorithm is critical for ensuring optimal performance, especially when dealing with complex problems.

Heuristic Search Algorithms in AI

Heuristic search algorithms use heuristic functions to guide the search process toward the goal more efficiently. They help in finding solutions faster by estimating the best path to take.

A* Search Algorithm

The A* Search Algorithm combines the actual cost from the start and the estimated cost to the goal. It uses both to find the most efficient path.

• Cost Function: It calculates f(n)=g(n)+h(n)f(n), where:
  • g(n) is the actual cost from the start node to the current node.
  • h(n) is the estimated cost from the current node to the goal.
• Advantages:
  • Finds the least costly path to the goal.
  • Efficient when a good heuristic is available.
• Applications:
  • Pathfinding in maps and games.
  • Network routing and optimization.

Greedy Best-First Search

Greedy Best-First Search selects the path that seems best at the moment. It relies on the heuristic estimate to choose the next node.

• Heuristic Function: Considers only h(n), the estimated cost to reach the goal from the current node.
• Characteristics:
  • Faster than A* but may not find the optimal path.
  • Can get stuck if the heuristic is not accurate.
• Uses:
  • Solving puzzles and simple pathfinding.
  • Situations where speed is more important than perfection.

Hill Climbing Algorithm

The Hill Climbing Algorithm moves in the direction that increases value, aiming to reach the highest point.

• Process:
  • Start with an initial solution.
  • Evaluates neighbouring solutions.
  • Moves to the neighbour with the highest value.
• Limitations:
  • May stop at local maxima, not reaching the best overall solution.
  • Does not look ahead beyond immediate neighbours.
• Applications:
  • Optimization tasks.
  • Adjusting parameters in machine learning models.

Beam Search

Beam Search is an optimization that reduces memory usage by limiting the number of paths explored.

• Mechanism:
  • Keeps a fixed number of best paths at each level, known as the beam width.
  • Discards less promising paths to save resources.
• Benefits:
  • Balances between thorough search and resource limits.
  • Useful when memory or time is constrained.
• Usage:
  • Natural language processing.
  • Speech and image recognition.

Genetic Algorithms

Genetic Algorithms mimic natural selection to find optimal solutions over time.

• Key Concepts:
  • Population: A group of potential solutions.
  • Selection: Choosing the best solutions for the next generation.
  • Crossover and Mutation: Combining and altering solutions to create diversity.
• Advantages:
  • Effective for complex problems with large search spaces.
  • Can escape local maxima to find a better overall solution.
• Applications:
  • Scheduling and planning.
  • Evolving neural network architectures.

Properties of a Heuristic Search Algorithm

Heuristic search algorithms have certain properties that make them efficient in solving complex problems. These properties guide the search process toward better solutions.

• Optimality: The algorithm finds the best possible solution when a suitable heuristic is used.
• Completeness: It guarantees a solution will be found if one exists, as long as the search space is finite.
• Efficiency: Heuristic algorithms reduce the time needed to search by focusing on promising paths.
• Admissibility: The heuristic function does not overestimate the cost, ensuring the algorithm finds the optimal solution.
• Consistency (Monotonicity): The estimated cost from one node to the next is always less than or equal to the step cost, ensuring smooth progress toward the goal.

Common Problem Types for Heuristic Functions

Heuristic functions are widely adopted in the solution of numerous problems which are more prevalent in huge spaces and where an efficient solution is required. Below are some typical problem types where heuristic functions prove useful.

Pathfinding Problems

Pathfinding has been a major area in the application of heuristic functions, especially in the case of AI. Heuristics help fill the gap of finding the shortest or the most efficient method of traversing between two locations on given premises.

• Example: Navigation systems, where the heuristic might be the straight-line distance between two locations.
• Algorithms Used: A* Search and Greedy Best-First Search.
• Challenges: However, in such a complex environment, the presence of many obstacles or some unknown factors makes estimating the best path difficult.

In such cases, a good heuristic as in heuristic A* reduces unnecessary explorations of tree branches and drives the search towards the goal effectively.

Puzzle Solving

Heuristic functions also come in handy while working on intricate puzzles, where the solver is given a state which is somewhere on the route to the solution, and they need to estimate how far they are from the solution.

• Example: The classic 8-puzzle or 15-puzzle, where the heuristic might be the number of misplaced tiles or the Manhattan distance (total number of moves to place each tile in the correct position).
• Algorithms Used: A*, IDA*(Iterative Deepening A*)
• Challenges: Finding an admissible heuristic is one of the difficulties which is required so that the algorithm can always reach the optimal solution.

For these puzzles, the right heuristic can be utilised effectively, exploring the number of moves which purely increase the distance from the goal state.

Game Playing

In the case of chess, tic-tac-toe and other games, heuristic functions are used to assess the value of non-terminal game states and to choose a move within the position that is most advantageous.

• Example: In games like chess, the heuristic could suggest an estimate concerning the board positioning in favour of any of the two players.
• Algorithms Used: Minimax with Alpha-Beta Pruning.
• Challenges: Moves in the game may have long-term consequences that are sometimes hard to take into consideration, thus a good heuristic is required for optimal decisions.

Here, a good heuristic helps the AI to eliminate useless moves and concentrate on a few moves with prospective chances of leading to victory even if it is not possible to search the whole game tree.

Resource Allocation and Scheduling

Heuristic functions are also effective for resource allocation and scheduling issues which include appropriate assignment of tasks or resources to a number of people or units.

• Example: Dividing and distributing work among machines in order to minimise make-span.
• Algorithms Used: Genetic Algorithms, Simulated Annealing.
• Challenges: These types of problems tend to have a wide array of complex constraints. It helps to keep a balance between exploration and exploitation.

The right heuristic allows for the more effective utilisation of resources, reducing the time to completion of the project or enhancing the quality of the time schedule.

Optimization Problems

In this case, a set of various solutions is identified, and the aim is to determine the optimum solution.

• Example: Travelling Salesman Problem (TSP), where the heuristic might estimate the shortest path connecting a set of cities.
• Algorithms Used: Genetic Algorithms, Simulated Annealing, Tabu Search.
• Challenges: These types of problems tend to have many solution candidates and as such, it is impractical to try and exhaustively search all of these.

Careful selection of the right heuristic will help to reduce the complexity of any of the above problems, where time and resources will be spent more judiciously.

Path Finding with Heuristic Functions

Heuristic functions play a crucial role in pathfinding by estimating the cost from a given node to the goal. They help reduce the number of explored paths, making the search faster and more efficient. One popular algorithm that uses heuristic functions is the A* algorithm. It combines both the actual cost to reach a node and the estimated cost to reach the goal, which allows it to prioritise paths that are more likely to lead to an optimal solution. The efficiency of this method largely depends on how accurate the heuristic function is.

In a typical pathfinding problem, such as navigating from one point to another in a grid or map, the heuristic function might be the Euclidean or Manhattan distance between two points. The A* algorithm uses this heuristic to select the most promising nodes for exploration. The actual cost is tracked along the way, and the algorithm updates this as it explores different paths. This combination ensures that the algorithm not only finds a path but also finds the shortest path in most cases.

Step-by-Step Algorithm

1. Initialize Open Set: Start by adding the initial node to the priority queue (open set). The queue is ordered by the total estimated cost f(n)=g(n)+h(n), where g(n) is the actual cost to reach the node, and h(n) is the heuristic estimate from the node to the goal.
2. Track Costs and Path: Use dictionaries to store the cost to reach each node from the start and to keep track of the path taken to reach that node.
3. Loop Until Open Set is Empty: Continue exploring the most promising nodes (lowest f(n)) in the priority queue.
4. Check for Goal: If the current node is the goal, reconstruct the path by backtracking through the came_from dictionary.
5. Explore Neighbours: For each node, check its neighbouring nodes (right, left, up, down in a grid). Add valid neighbours (within bounds and not obstacles) to the open set if they provide a better path.
6. Update Costs and Path: For each neighbour, calculate the tentative cost g(n). If this path to the neighbour is better than any previously known path, update the cost and record the path.
7. Repeat: Continue exploring until the goal is found or the open set is empty, indicating no path exists.
8. Return Path or Failure: If the goal is reached, return the reconstructed path. If the open set is empty and the goal is not reached, return failure.

Python Code

Conclusion

Heuristic functions are very important when it comes to determining which techniques should be used to implement a given search, especially efficient ones. It helps to reduce the time and resources involved in the resolution of complex structures. In navigation systems to search for a target, decision-making in games, and allocation of resources to different activities, heuristics enhance the performance of AI systems.

Looking to the future, as AI continues to grow, the invention of better and better heuristic functions is going to be important in addressing even more complex challenges. Operation with heuristics will always accompany the development of sectors such as computer vision, robotics, machine learning, or scheduling as an ascendant of their performance.