AI problem-solving means arriving at the best possible solution to a particular problem from the very beginning and coming up with the best method of solving the problem whereby the best method involves using several steps to arrive at the best solution. This requires developing an agent that is capable of conducting operations like searching for solutions, assessment of paths, and finding efficiencies.
In AI, problem-solving is often likened to solving a puzzle, where you start with incomplete information, use available tools (algorithms), and find the most efficient way to reach the desired solution. In simpler terms, it’s about getting from point A to point B, but sometimes, the route is not obvious.
The Core of Problem Solving in AI
AI systems can approach problem-solving in several ways, but at its core, it involves the following key elements:
- Initial State: The starting point of the problem. This could be any scenario or configuration, such as a robot’s position in a room or an unsolved puzzle.
- Goal State: The desired end state that solves the problem.
- Actions: The steps or moves the AI can take to transition from one state to another.
- Path Cost: The cost of each action, which could be time, energy, or resources, and the goal is to minimize it while finding the solution.
Decision-making in AI applications majorly works under several steps outlining how to solve the problem and find the best solution from the existing ones.
What Are the 4 Main Problems AI Can Solve?
AI can address so many issues that are real-life issues. Below are the four primary categories in which AI is used for problem-solving:
1. Decision Making
AI can solve problems related to decision-making by evaluating different options and determining the best course of action. For instance, in self-driving cars, actual choices such as route choice, velocity choice, and evasion of the obstacle are made by AI specifically for the utilization of the trip.
2. Optimization Problems
Optimization is about finding the best solution from a set of possible solutions. AI helps in problems like route planning (e.g., Google Maps optimizing traffic routes), supply chain management, and portfolio management.
3. Classification and Prediction
Recorded performances can be categorized and new performance estimates can be made based on past trends in AI models. For instance, in the medical domain, AI systems may forecast further developments of a given disease or classify images from a medical point of view, to identify specific pathologies.
4. Natural Language Processing (NLP)
NLP uses AI to interpret, understand, and generate human language. Chatbots and voice assistants like Siri and Alexa rely on problem-solving in NLP to respond to user queries in a natural and meaningful way.
Types of Problem Solving in AI
AI can be divided into categories of how the problems can be solved and each of these approaches has its strengths and weaknesses. These methods include:
1. Uninformed Search Algorithms
These algorithms explore the solution space without any additional information about which path might lead to the goal. One popular uninformed search algorithm is Breadth-First Search (BFS). The algorithm works all the possible solutions step by step on each level to make it understand that it needs to achieve the goal with the least number of steps. However, it can be computationally expensive and slow.
Example: Depth-first search (DFS) is another uninformed search technique where the algorithm explores as deeply as possible along each branch before backtracking.
2. Informed Search Algorithms
Informed search algorithms (also called heuristic search) use domain-specific knowledge to find solutions faster. These algorithms also use the heuristic function to approximate the distance of a state to the goal state. This significantly reduces the computational effort compared to uninformed search techniques.
Example: A Search Algorithm* is one of the most commonly used informed search algorithms. The method is developed by the ‘DCF’ algorithm which explores the optimum path like both BFS and DFS yet it implements heuristics of the minimum path to the goal.
3. Path Costing and Evaluation
While solving problems there’s always a need to find out whether a certain solution is effecting a certain amount of change through path costing. AI systems not only seek solutions but also evaluate the cost associated with each action taken to ensure the solution is optimal.
Example: In navigation problems, the cost might be the distance traveled, time spent, or energy consumed. AI can then select the path that minimizes these costs.
4. Goal Test
A goal test is an essential function in AI problem-solving. It helps determine whether the current state of the system has reached the desired goal state.
Example: In a puzzle-solving AI, the goal test checks if the puzzle configuration matches the solved state.
Problem-Solving Agents: How AI Finds Solutions
In AI, problem-solving agents are intended to provide a means by which solutions can be sought and found automatically. These agents operate based on several algorithms that help one determine which state of the system to take over the other. Here we need to remember that the agent aims to minimize cost whereas the solution, will take the path that has less cost involved.
A good example of a problem-solving agent could be a robotic system moving through a maze. The robot will constantly assess potential trajectories, perform a check for obstacles and, if required, utilize various strategies like DFS or A* to determine the best way.
Solving Problems in AI: A Step-by-Step Process
Here’s a simple example to understand how AI solves problems using different search techniques:
Example: Solving a Maze
- Initial State: The robot starts at the entrance of the maze.
- Goal State: The robot’s goal is to reach the exit of the maze.
- Actions: The robot can move up, down, left, or right at each step.
- Path Cost: Each move has a cost, which could be the time taken, energy spent, or the number of moves.
The AI would begin by using a search algorithm, like DFS, to explore the maze. It might backtrack if it hits a dead-end, or it might use an informed search algorithm like A* if it knows the location of the exit and uses that knowledge to optimize its path.
Current Trends and Insights on Problem Solving in AI in Pakistan
Pakistan they are using artificial intelligence slowly and progressing in various fields including healthcare, logistics, and education sectors. As per the latest sources, the AI market in Pakistan has been predicted to reach 23.6% CAGR in the period from 2023 to 2028. However, AI problem-solving methods are still not very popular amongst these local agencies and there is a rising demand for AI specialists and systems that will permit the local enterprises to solve the real-world issues efficiently.
AI’s Role in Pakistan’s Industry
- Healthcare: AI is increasingly used in medical diagnosis, prediction of diseases, and robotic surgeries.
- Agriculture: AI is helping farmers optimize crop yields and reduce costs through precision farming.
- Education: AI solutions such as personalized learning platforms are revolutionizing the education sector in Pakistan.
Conclusion
Approach for Problem solving in Artificial Intelligence is essential for creating Intelligent Agents with the ability to decide as well as solve issue in the information processing system in an optimum manner. Starting with a naive approach including search algorithms like DFS and continuing through informed methods like A* algorithms, AI can solve many problems of decision-making, optimization, and classification. The students of Pakistan and AI agencies should have complete knowledge of these concepts for forming better systems in contributing to the industries of healthcare education and so on.
It remains enormous as well as unbounded, and the demand for efficient problem-solving approaches remains more acute with the development of technologies.
