Problem Solving Techniques in AI. Commonly problem-solving refers to achieving the desired goal by solving all the hurdles during the goal achievement process. When it comes to the world of Computer Science, problem-solving is the set of Artificial Intelligence techniques. Problem-solving in AI is nowadays the most common method being used by Computer Scientists to resolve most problems.
In the world of AI, problems are can resolve by logical algorithms, differential equations, utilizing effective polynomial and executing all of them with modelling patterns. One problem can have multiple solutions by using different problem-solving techniques. In parallel, some problems have unique solutions and can be only resolved by one method. It depends on the nature of the problems that how many solutions are possible.
Existing Problems in Artificial Intelligence
Problem-solving in AI is becoming more popular day by day. Computer scientists are using Artificial Intelligence to automate their manual tasks in daily routine to save their effort and time. Games and puzzles are real-time examples of an existing problem. These can be resolved by using the most effective AI algorithms. Mathematical puzzles such as magic squares, crypto arithmetic. Logical puzzles such as N-Queens, Boolean formulas are being used in the most popular games such as Chess. Problem-solving in AI is being used to tackle these all problems and make their solutions most effectively.
Some of the popular problems that are solved by AI:
- Sudoku & Chess
- Water Jug Problem
- Tower of Hanoi
- N-Queen problem
Artificial Intelligence is assisting developers in making efficient software and developing the software as per the requirements. The dead-end problems are also can be solved now with the help of Artificial Intelligence algorithms. In problem-solving AI use some of the agents. Five agents are being used in AI for problem-solving Simple reflex, Goal-Based, Model-Based reflex, Utility-Based & Learning agents. These five agents help identify actions and states. But they often become useless when it comes to solving complex problems of the real world.
Problem Solving Techniques in AI
Artificial Intelligence is known for its effective algorithms to solve real-time problem solutions that exist in the real world. Here are some of the Problem-solving techniques that are commonly used to resolve problem-solving in AI.
Search Algorithms
Search algorithms are one of the common methods to solve problems in AI. The problem solving or rational agents with search algorithms are used to find the best optimal solution. This type of problem-solving technique is often called goal-based. It is used to find certain results and achieve the goal in a short time. In addition, search algorithms own time complexity, completeness and optimality as well.
Types of Searching Algorithms
There are two types of searching algorithms that are commonly used in AI
- Informed
- Uniformed
Informed Searching Algorithms
Informed search algorithms use basic knowledge regarding the problem domain and also grasp the information available for a specific problem. Then the information is used as a guideline to find an optimal solution. Moreover, this type of solution is more efficient uninformed search algorithms. In addition, there are two types of Informed search algorithms Greedy search and A * search. It ranks among the top problem solving techniques in AI.
Uninformed Searching Algorithms
Uninformed search algorithms don’t have basic knowledge regarding the problem domain and also don’t have any information related to a specific problem. Here you have traverse trees, identifying leaves and final nodes.
Uninformed search is known as blind search because there is no clue whatsoever for a specific problem. No initial information is there to achieve a specific goal. The uninformed search goes to every node until it finds the goal destination.
There are five types of Uninformed Searching Algorithms. Breadth-First Search, Depth First Search, Uniform Cost Search, Iterative Depth First Search, Bidirectional search.
Heuristics
The heuristic method helps to understand the nature of the problem and derive a solution based on the experimental method. The heuristic technique doesn’t provide the optimal solution to a specific problem but it attains immediate goals.
The developers use a heuristic approach when the traditional methods don’t find the optimal solution. Moreover, the heuristics provide a time-efficient solution with good accuracy. Heuristics combine with optimized algorithms to increase efficiency. This also ranks among the problem solving techniques in AI
Evolutionary Computation
The evolutionary Computation intelligence technique is stimulating from natural evolution. It uses the renowned concept of evolution. The principle of survival of the very fittest is adapted by Evolutionary Computation.
When it comes to Artificial Intelligence the Evolutionary Computation uses the trial and error technique to tackle the problems. The evolution theory is also used in genetic algorithms to create a favorable child.
Conclusion
Artificial Intelligence is the future and being used to make our lives more easy and comfortable. The continuous evolving in AI is happening and soon it will automate the most of manual operations.
Problem solving techniques in AI are being discussed and also the existing problems in Artificial Intelligence. The problem solving techniques in AI doing their part to tackle the problems. However, developers and data scientists use the AI algorithms to solve the complex problems.
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