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  • Reinforcement Learning: Solving complex problems using algorithms that learn through trial and error, making optimal decisions in dynamic environments.
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  • Genetic Algorithms: Applying evolutionary principles to optimization problems, finding optimal solutions through genetic representations and operators.
  • Expert Systems: Building AI systems that emulate human expertise and decision-making in specific domains.
  • Swarm Intelligence: Addressing problems inspired by the collective behavior of social insects, such as ant colony optimization and particle swarm optimization.
  • Natural Language Generation (NLG): Creating AI systems that can generate human-like language based on input data.
  • Knowledge Representation and Reasoning: Developing methods to represent and utilize knowledge for problem-solving.
  • Robotics: Integrating AI into robotic systems for autonomous decision-making and problem-solving.
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