By the end of Part 2, you should be able to: Formulate a Real-World Problem as a Search Problem To approach a real-world problem using search techniques, start by defining the problem in terms of states and transitions. For example, consider a logistics problem where you need to deliver packages to multiple locations. The states represent different configurations of the delivery process (e.g., packages delivered, routes taken), and transitions represent actions (e.g., choosing a route, delivering a package). The goal is to find the optimal sequence of actions that results in the most efficient delivery process. Formulate a Simple Game (Such as Tic-Tac-Toe) as a Game Tree Represent a simple game like tic-tac-toe as a game tree. Each node in the tree represents a possible game state (e.g., the arrangement of Xs and Os on the board). The root node is the initial game state, and each level of the tree represents a player's move. Branches between nodes show the possible transitions from one game state to another based on player actions. For tic-tac-toe, the tree starts with an empty board and branches out as each player makes a move until the game ends in a win, loss, or draw. Use the Minimax Principle to Find Optimal Moves in a Limited-Size Game Tree Apply the minimax principle to evaluate the game tree for tic-tac-toe. The minimax algorithm works by assuming that both players play optimally. For each terminal node (game end state), assign a value based on the outcome (e.g., +1 for a win, -1 for a loss, 0 for a draw). Then, propagate these values up the tree. For nodes representing Max’s turn, choose the move that maximizes the value, while for Min’s turn, choose the move that minimizes the value. This approach helps determine the optimal moves in a limited-size game tree by evaluating all possible outcomes and choosing the best strategy for each player.