Online Searching Agent in Unknown Environments

Dr T RAVICHANDRAN, Professor, Department of Artificial Intelligence and Data science, Akshaya College of Engineering and Technology.
Online searching AI agents are those that operate in environments where the complete information about the environment is not known in advance. These agents must learn and adapt as they explore, making decisions based on their current knowledge and observations.
Key Challenges and Strategies
Exploration vs. Exploitation: The agent must balance between exploring new areas (to gather information) and exploiting known areas (to maximize reward). This is often addressed using techniques like ε-greedy or softmax exploration.
Uncertainty Handling: The agent must deal with uncertainty about the environment. This can be done using probabilistic models or techniques like Bayesian inference.
Learning from Experience: The agent must learn from its interactions with the environment. This can involve reinforcement learning, where the agent learns to maximize a reward signal, or other machine learning techniques.
Common Techniques
Reinforcement Learning: This is a powerful framework for training agents to make decisions in unknown environments. The agent learns to maximize a reward signal by interacting with the environment.
Monte Carlo Tree Search (MCTS): This is a probabilistic search algorithm that can be used to make decisions in complex environments. MCTS simulates possible future outcomes and chooses the action that leads to the highest expected reward.
Bayesian Optimization: This is a method for optimizing black-box functions, which can be useful for tuning hyperparameters in machine learning models or optimizing the behavior of AI agents.
Imitation Learning: This involves training an agent to mimic the behavior of a human expert. This can be useful for tasks where it is difficult to define a reward function.
Applications
Autonomous Vehicles: Self-driving cars must learn to navigate complex and dynamic environments.
Robotics: Robots can use online search to explore and map unknown environments.
Game AI: Game agents can use online search to develop strategies and tactics.
Recommendation Systems: Online recommendation systems can use online search to discover new items that users might like.
Future Directions
Deep Reinforcement Learning: Combining deep learning with reinforcement learning has led to significant advances in AI.
Multi-Agent Systems: Developing AI agents that can collaborate or compete with other agents.
Safety and Ethics: Ensuring that AI agents are safe and ethical, especially in critical applications like autonomous vehicles.