AI agents commonly rely on a mix of machine learning, search/optimization, and planning/reasoning algorithms. These algorithms enable agents to learn from data, make decisions, and solve complex tasks. The choice of algorithm depends on the problem type, such as classification, pathfinding, or sequential decision-making.
Machine learning algorithms form the backbone of many AI agents. Supervised learning methods like neural networks (e.g., CNNs for image recognition) and decision trees are used for tasks requiring pattern recognition or prediction. Unsupervised techniques such as k-means clustering or principal component analysis (PCA) help in discovering hidden structures in unlabeled data. Reinforcement learning (RL) algorithms like Q-learning or Deep Q-Networks (DQNs) are critical for agents that learn through trial and error, such as game-playing AIs (e.g., AlphaGo) or robotics control systems. For example, a delivery route optimizer might use RL to adapt to traffic patterns in real time.
Search and optimization algorithms are essential for solving problems where the agent must find the best path or configuration. A* and Dijkstra’s algorithms handle pathfinding in navigation systems, balancing efficiency and accuracy. Genetic algorithms mimic natural selection to optimize parameters in design or scheduling problems, such as finding the shortest delivery routes for a fleet of vehicles. Gradient descent and its variants (e.g., Adam) are foundational for training neural networks by minimizing prediction errors. These algorithms often work behind the scenes, like tuning a recommendation system’s parameters to maximize user engagement.
Planning and reasoning algorithms enable agents to make sequences of decisions in dynamic environments. Monte Carlo Tree Search (MCTS), used in AlphaGo, evaluates possible moves in games by simulating outcomes. STRIPS-like planners break down complex tasks into actionable steps, useful in robotics assembly lines. Probabilistic reasoning tools like Bayesian networks help agents handle uncertainty, such as diagnosing faults in a manufacturing system. For instance, a self-driving car might combine MCTS for trajectory planning with Bayesian networks to assess collision risks, ensuring safe navigation through unpredictable traffic. These algorithms emphasize logical structure and adaptability to changing conditions.