Key Techniques and Algorithms in Agentic AI 🧠⚙️
Implementing agentic capabilities relies on a suite of advanced techniques:
- Reinforcement Learning (RL): Enables agents to learn optimal policies through trial-and-error interactions with the environment.
- Planning and Search Algorithms: Such as A*, Monte Carlo Tree Search (MCTS), and PDDL planners, used for decision-making in complex scenarios.
- Knowledge Representation & Reasoning (KR&R): Ontologies, semantic networks, and logic systems that allow agents to understand and infer information.
- Uncertainty Modeling: Techniques like Bayesian networks and POMDPs to manage incomplete or noisy data.
For example, an autonomous drone might use RL to adapt flight strategies, A* for navigation, and Bayesian methods for obstacle detection under uncertain conditions.