## What is a Heuristic?

The term heuristic is derived from the Greek word heurisko, which means “I find, discover”. Heuristics can be mental shortcuts or approaches designed from previous experiences. The main aim of the heuristic approach is to find a good enough solution to solve the problem. Test and mistake, rule of thumb, or an educated estimate are all examples of heuristics. These methods are sufficient for achieving an immediate, brief aim. When a heuristic is used in practice, it works as predicted. It can, however, produce systematic errors. Trial and error is an important heuristic that may be used in a variety of situations, from cross-linking nuts and bolts to determining the values of given input variables in mathematical puzzles. Additional hypotheses, visual illustrations, simplification, and forward or backward reasoning are all common heuristics in mathematics.

## Heuristic in computer science

Heuristics is a problem-solving approach whose aim is to produce a final optimal solution within a particular time. Heuristic approaches and strategies aim for a quick solution that falls within an acceptable range of accuracy.
Heuristics are mainly used in Artificial intelligence (AI) and Machine learning (ML) when it is hard to solve a particular problem with a step-by-step algorithm. Since a heuristic approach focuses on speed rather than accuracy, optimization algorithms are often added to improve results. The repetitive outcomes are interdependent, and each level of complex neural network decides what to select or reject based on the desired solution.

Heuristic approaches use provided data rather than predefined solutions to resolve human and machine problems. Heuristic solutions are not necessarily precise, provable, or accurate, but they are usually better enough to resolve small-scale issues that are a part of more significant problems.

The term heuristic is sometimes used as a synonym for "short-cut" because this idea of problem-solving does not waste time on things that are not likely to produce acceptable results.

## Applications of heuristic

The concept of heuristic is widely used in AI that is, artificial intelligence and the computer simulation of thinking in situations where there are no standard algorithms.

Some applications of heuristic are given below:

• Travelling salesman problem (TSP): The TSP dilemma is utilized for finding the shortest path in a graph. The tour is constructed using a greedy heuristic to repeatedly fetch the sharpest edge and add it to the tour as long as it does not create a cycle.
• Searching: In AI, heuristic search is a technique, that iteratively finds a better solution than existing one. .
• Simpler problem: The heuristic method find solutions by using shortcuts. The problem solving is made easy with heuristic search by applying the approach of self-discovery algorithms.
• Antivirus software: In antivirus software, malware and viruses are detected. The heuristic method is used for detecting the code of suspicious or malicious activities.

## Criteria for heuristic

Following are the criteria that must be considered before using a heuristic approach.

• Optimality : When many solutions to a problem are already present, it is necessary to use a heuristic approach for the best solution. Optimality is the main computational goal. In contrast, heuristics are the algorithms by which those goals can be achieved.
• Completeness : When there are many solutions, the heuristic must be able to find all or most of them which yields to completeness.
• Accuracy and precision : Accuracy and precision describe the solution provided by the heuristic system as fault-free and 100 % accurate.
• Execution time : If the heuristic approaches do not solve a problem in a given time frame, it should be faster than other methods; otherwise, it will be considered overhead.

It may be challenging to handle and decide whether the conclusion found by the heuristic strategy is up to scratch or not because the theory intrinsic to heuristics is not very elaborate.

## History

Herbert A. Simon, a Nobel Laureate, initially introduced the concept of heuristics. His findings indicated that they functioned under restricted rationality when it came to problem-solving. `A situation in which people accept choices or judgment even after they are just “good enough” to fulfill their purpose. However, the problem that could be optimized is termed as Satisficing by Simon. Psychologists Daniel Kahneman and Amos Tversky developed the study of Heuristics later in the 1970s. Succeeding them, Rudolf Groner proposed a concept of “heuristic versus algorithmic thinking”, which can be negotiated with the help of a questionnaire, seeing and measuring all about heuristics from the ancient Greek to work in sensitive psychology together with artificial intelligence.

## Heuristic in judgment and decision making

Affect heuristic : The gut decision is a mental option that employs emotion to affect a decision by analyzing the dangers and rewards of anything about the bad and positive feelings that humans experience when exposed to stimuli.

Effort heuristic: Effort applied to the creation of the object determines the worth of an object. Things that take longer to bring out are considered more effective, whereas items that take less time are considered less beneficial.

Balance Heuristic: It is based on the decision-making process of problem-solving. This is used when a person weighs the negative and positive possibilities, allowing them to make an informed decision.

Common Sense Heuristic: This theory says people use the common sense heuristic at times when the future outcomes appear apparent.

Educated Guess Heuristic: When individuals agree with a judgment by testing the relevant information, they set aside information related to the problem.

Scarcity heuristic: The scarcity heuristic argues that the more difficult it is to obtain a thing, the higher its value must be. In many cases, use a components' obtainability, it's perceived wealthiest, to rapidly guess the status of quality and utility. This causes systematic cognitive biases or judgment errors.

Simulation heuristic: Simple mental ways in which humans assess the likelihood of an event occurring based on how easy it is to mentally see the instance or behavior occur. This theory claims that humans enthusiastically simulate everything around them to predict the likelihood of happenings by running mental simulations of the events using the same input values as the updated model.

Working backward heuristic: When a person believes they have solved an issue, they take information to discover how to get the solution they originally envisioned.

• Problem-solving
• Decision making
• Psychology
• Behavior study
• Cognitive theory, Cognitive bias
• Judgment theory

## Context and Applications

This topic is essential in the professional exams for both postgraduate and graduate courses, especially for:

• Bachelor of Technology in Computer Science
• Master of Technology in Computer Science
• Bachelor of Engineering
• Master of Computer Application

## Practice Problems

Q1. Heuristic is

1. An error
2. A protocol
3. A strategy
4. A rule of thumb

Explanation: The behavior appropriate for a specific situation is handled using a heuristic approach. It is a rule of thumb. That is, the strategies are derived from previous experience of problems.

Q2. According to Newell and Simon, a problem-solver:

1. Analyzes all possible solutions before beginning
2. Attempts to resolve differences between problem states
3. Works backwards from the goal state
4. Prioritizes sub-goals

Explanation: Newell and Simon created a theoretical framework of problem-solving. According to their views, a problem-solver Attempts to resolve differences between problem states.

Q3. A mental shortcut that uses emotions to make a decision is called

1. Affect heuristic
2. Cognitive bias
3. Involve heuristic
4. None of the above

Explanation: Decision-making is an important stage in the heuristic. The mental emotions that influence the heuristic are called affect heuristics. It plays a crucial role in decision-making.

Q4. What is the main task of a problem-solving agent?

1. Solve the given problem to reach the goal
2. To find out which sequence of actions to use
3. Both 1 and 2
4. None of the above

Explanation: The problem-solving agent solves the problem based on the goal and determines the sequence of actions involved in the problem-solving process.

Q5. Systematic judgments may lead to:

1. Accurate judgment
2. System Analysis
3. Cognitive Bias
4. None of the above

Explanation: Cognitive bias is based on the decision-making process. It influences what we think and makes decisions based on judgment. So, systematic conclusions sometimes lead to cognitive bias.

### Want more help with your computer science homework?

We've got you covered with step-by-step solutions to millions of textbook problems, subject matter experts on standby 24/7 when you're stumped, and more.
Check out a sample computer science Q&A solution here!

*Response times may vary by subject and question complexity. Median response time is 34 minutes for paid subscribers and may be longer for promotional offers.

### Search. Solve. Succeed!

Study smarter access to millions of step-by step textbook solutions, our Q&A library, and AI powered Math Solver. Plus, you get 30 questions to ask an expert each month.

Tagged in
EngineeringComputer Science