“A heuristic technique, often called simply a heuristic, is any approach to problem solving, learning, or discovery that employs a practical method not guaranteed to be optimal or perfect, but sufficient for the immediate goals. Where finding an optimal solution is impossible or impractical, heuristic methods can be used to speed up the process of finding a satisfactory solution. Heuristics can be mental shortcuts that ease the cognitive load of making a decision. Examples of this method include using a rule of thumb, an educated guess, an intuitive judgement, guesstimate, stereotyping, profiling, or common sense.” (Source: Wikipedia)
“In computer science, a heuristic is a technique designed for solving a problem more quickly when classic methods are too slow, or for finding an approximate solution when classic methods fail to find any exact solution. This is achieved by trading optimality, completeness, accuracy, or precision for speed. In a way, it can be considered a shortcut.” (Source: Wikipedia)
The objective of a heuristic algorithm is to apply a rule of thumb approach to produce a solution in a reasonable time frame that is good enough for solving the problem at hand. There is no guarantee that the solution found will be the most accurate or optimal solution for the given problem. We often refer the solution as “good enough” in most cases.
Heuristic Algorithms can be found in:
Let’s investigate a few basic examples where a heuristic algorithm can be used:
Based on this approach, can you think of how a similar approach could be used for an algorithm to play:
- Othello (a.k.a. Reversi Game)
- A Battleship game?
It is hence essential to use a heuristic approach to quickly discard some moves which would most likely lead to a defeat while focusing on moves that would seem to be a good step towards a win!
Let’s consider the above scenario when investigating all the possible moves for this white pawn. Can the computer make a quick decision as to what would most likely be the best option?
Heuristic approaches do not always give you the best solution. Can you explain how this could be the case with this scenario?
For instance in a game of Top Trumps, if your algorithm knows the average value of each card for each category, it will be able to select the criteria which is most likely going to win the round.
Alternatively, a machine learning algorithm could play the game and record and update statistics after playing each card to progressively learn which criteria is more likely to win the round for each card in the deck.
e.g. When using Google search a user types: “Raspeberry Pi Hardware” We can deduct that in this case Raspberry has nothing to do with the piece of fruit, so there is no need to give results on healthy eating, cooking recipes or grocery stores…
However if the user searches for “Raspeberry Pie ingredients”, we can deduct that the user is searching for a recipe and is less likely to be interested in programming blogs or computer hardware online shops.
You can compare two different algorithms used to find the shortest route from two nodes of a graph: