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Like machine learning, what are other approaches in AI ?

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By Team EasyExamNotes

Machine learning is a powerful approach in AI, but it’s not the only one.

Here are some other important approaches:

1. Symbolic AI: This approach focuses on using symbols and logic rules to represent knowledge and reasoning. Symbolic AI systems are often good at tasks that require explicit reasoning, such as solving puzzles or proving theorems. However, they can be difficult to scale to complex real-world problems.

2. Rule-based AI: This approach involves creating a set of rules that an AI system can follow to make decisions. Rule-based systems are often used in expert systems, which are computer programs that are designed to emulate the decision-making ability of a human expert in a particular domain. However, rule-based systems can be brittle and difficult to maintain, as they can become very complex as the number of rules grows.

3. Evolutionary computation: This approach is inspired by the process of natural selection. It involves creating a population of candidate solutions and then iteratively improving them through a process of selection, mutation, and crossover. Evolutionary computation can be used to solve a wide range of problems, including optimization problems and machine learning tasks.

4. Robotics:This field is concerned with the design, construction, operation, and application of robots. Robotics can be seen as a branch of AI, as robots are often equipped with AI systems that allow them to sense their environment, make decisions, and take actions.

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