How AI Systems Plan Under Uncertainty: From Lookahead to MCTS
How AI Systems Plan Under Uncertainty: From Lookahead to MCTS In real-world AI systems, decisions are rarely made with complete information. Whether it’s an autonomous system navigating uncertain terrain, a fraud detection engine evaluating risk, or a robotics system reacting to sensor noise—decisions must be made before the future is known . Instead of asking “What will happen?”, these systems ask: “What should I do, given what might happen?” Mathematically, we aim to find the Optimal Action (\(a^*\)) that maximizes expected utility (\(U\)) over future states: \[ a^* = \arg \max_{a \in A} \sum_{s' \in S} T(s, a, s') [R(s, a, s') + \gamma V(s')] \] The Architecture of Choice: Navigating the Search Space As an AI Enterprise Architect, I view planning not just as an algorithm, but as a runtime architecture decision . The challenge is balancing the "Curse of Dimensionality" with the need for ...