Artificial Intelligence Tutorial

Introduction to Artificial Intelligence Intelligent Agents

Search Algorithms

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Knowledge, Reasoning and Planning

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Uncertain Knowledge and Reasoning

Quantifying Uncertainty Probabilistic Reasoning Hidden Markov Models Dynamic Bayesian Networks Utility Functions in Artificial Intelligence


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Vacuum Cleaner Problem in AI

In this article, we are going to see one of the important problems in Artificial Intelligence, i.e., the Vacuum Cleaner Problem. It is a well-known search problem in Artificial Intelligence and Machine Learning domains.

Let’s briefly discuss AI (Artificial Intelligence).

Artificial Intelligence is a subfield of computer science and engineering which develop smart machines that can carry out challenging tasks and learn according to their experience. Machine learning, deep learning, natural language processing, and computer vision are just a few of the methods used in artificial intelligence (AI) to develop algorithms and models that let machines learn from their experiences, spot patterns, and make decisions. Its main goal is to develop autonomous systems that can change according to their surroundings and become more effective and efficient in their jobs. Different sectors of industries like, robotics, healthcare, finance, transportation, entertainment, etc., are using Artificial Intelligence as per their requirement.

Now, come to the topic of the article, i.e., Vacuum cleaners using Artificial Intelligence. It is a problem which is an important robotics and AI problem that asks how to create an autonomous machine or agent (a robot or program) that can move inside a room in all directions on the floor, clean the dirt present on the floor effectively, and then go back to its desired place.

In this problem, the main aim is to design a machine, or we can say it is a robot, which has different types of sensors and actuators that helps the robot to move in a room, recognise and avoid obstacles, and clean the dirt present on the surface. The objective is to choose the robot's best course of action so that it may clean the entire area while using the least possible amount of time and energy.

Let’s understand it with an example. Consider a robot vacuum cleaner that needs to clean the dirt present on the floor area in a rectangular room with different types of obstacles like furniture, carpets, and other equipment. So, the robot has sensors that enable it to recognise barriers, gauge distance, and determine its position within the space. Additionally, the robot has four directions of movement: up, down, left, and right. This robot must choose the best route to clean the room without skipping any spots or going over previously cleaned areas in order to accurate cleaning of the room. In addition, it must avoid hitting obstacles and move back to its starting point.

There are different ways to solve this problem. Let’s talk about all the approaches by which we can solve it.

The application of search algorithms is one of the most widely used methods for resolving the Vacuum Cleaner Problem. With the help of computer programs called search algorithms, methodically scan a search space in quest of the best answer. The search space for the vacuum cleaner problem comprises all potential routes that the robot could follow to clean the entire area.

A* Algorithm

One of the most popular search methods for solving a vacuum cleaner problem is A* (pronounced A-star) algorithm. Heuristics are used in the A* algorithm to direct its exploration of the search space. The fundamental principle of A* is to search the space and choose the most promising node to expand first. The priority of a node is decided by the total cost of the path from the starting node to the current node and the predicted cost of the remaining path from the current node to the goal node. It maintains a priority queue of the nodes that still need to be investigated. The algorithm may first explore the most promising paths since the heuristics calculate the cost of getting to the goal state from each node in the search space.

Mapping Algorithm

Using mapping algorithms is another popular method of resolving the Vacuum Cleaner Problem. It is a computational technique for drawing a map of a real-world environment. It is the maps of the environment that robots operate in are made using mapping techniques in the context of robotics and artificial intelligence. These maps allow robots to navigate their surroundings more reliably and effectively. Map-making algorithms provide a map of the area that needs to be cleaned based on the regions that have already been cleaned. Using this information, the robot may then travel the area more efficiently, avoiding areas that have already been cleaned.

This issue can also be resolved by Learning techniques for the cleaning robots to perform their tasks more effectively and without interference using machine learning and computer vision. Some well-known businesses have produced their own autonomous vacuum cleaners, such as the Xiaomi Robot Vacuum-Mop 2i.


In this article, we got to know how Artificial Intelligence creates a big impact on our environments. The vacuum cleaner problem in AI aims to develop autonomous machines that can successfully traverse and clean a room on their own using different approaches like searches algorithm, ML, and computer vision.