Summary
This lecture introduces fundamental AI search algorithms, differentiating between uninformed strategies like Depth-First Search (DFS) and Breadth-First Search (BFS), informed strategies such as Greedy Best-First Search and A\* Search, and adversarial approaches like Minimax, while outlining their mechanics, trade-offs, and optimal conditions.
Key Takeaways
- AI Search Defined: Artificial intelligence often solves problems by "searching" for solutions through a set of possible states, encompassing tasks from finding driving directions to playing games. 0:50
- Search Problem Components: A search problem is defined by an agent perceiving an environment, states (configurations of the agent in its environment), an initial state, actions (choices in a state), a transition model (what state results from an action), a goal test (if a state is a solution), and path cost (numerical expense of a path). 4:40
- General Search Algorithm & Uninformed Strategies: A general search algorithm maintains a "frontier" of states to explore and an "explored set" of visited states; Depth-First Search (DFS) uses a stack (last-in, first-out) to explore the deepest nodes first, is complete for finite state spaces but not guaranteed optimal; Breadth-First Search (BFS) uses a queue (first-in, first-out) to explore the shallowest nodes first, is complete and guaranteed optimal for unit-cost paths. 29:06
- Informed Search & Greedy Best-First: Informed search utilizes problem-specific knowledge via a heuristic function (h(n)) to estimate proximity to the goal, unlike uninformed search; Greedy Best-First Search (GBFS) expands the node estimated to be closest to the goal (e.g., using Manhattan distance in a maze), but is not guaranteed to find the optimal solution. 54:35
- A* Search Algorithm: A* Search optimizes for optimality by expanding the node with the lowest value of g(n) + h(n), where g(n) is the cost to reach the node and h(n) is the heuristic estimate to the goal; A* is optimal if its heuristic is admissible (never overestimates the true cost) and consistent (heuristic cost from node N to goal is less than or equal to step cost to N' plus heuristic cost from N' to goal). 1:05:10
- Adversarial Search with Minimax: For two-player, deterministic games, Minimax algorithm enables an AI to make optimal decisions by recursively considering all possible future moves, assigning a utility value to terminal states (e.g., -1 for opponent win, 0 for tie, 1 for AI win), with a "max player" aiming to maximize this score and a "min player" aiming to minimize it. 1:16:11
- Minimax Optimizations: To improve Minimax efficiency, Alpha-Beta Pruning eliminates branches of the search tree that cannot possibly influence the final decision by keeping track of the best (alpha) and worst (beta) scores found so far; for computationally intractable games like chess, Depth-Limited Minimax stops searching after a certain number of moves and uses an evaluation function to estimate the utility of non-terminal game states. 1:43:15
