How to Win at Games with AI: A Beginner’s Guide to Adversarial Search 🏆
What is game playing in AI?
Game playing is one of the most interesting and challenging applications of artificial intelligence (AI). It involves designing an agent that can play a game against another agent (human or computer) and try to win or at least not lose.
Some examples of games that AI agents can play are chess, checkers, tic-tac-toe, go, poker, and many more. These games have some common characteristics that make them suitable for AI:
- They are formal: they have well-defined rules, states, actions, and outcomes.
- They are discrete: they have a finite number of states and actions, and the game progresses in discrete steps.
- They are deterministic: the outcome of an action is fully determined by the state and the action, and there is no randomness involved.
- They are zero-sum: the utility (or score) of one agent is the negative of the utility of the other agent, and the total utility is zero. This means that one agent’s gain is the other agent’s loss, and vice versa.
What is adversarial search?
Adversarial search is a type of search algorithm that is used to find the best action for an agent in a game. Unlike other search algorithms, such as depth-first search or breadth-first search, adversarial search has to consider the actions and reactions of the opponent, as well as the possible outcomes of each move.
The goal of adversarial search is to find an optimal strategy for the agent, which is a sequence of actions that maximizes the agent’s utility, regardless of what the opponent does. An optimal strategy is also called a minimax strategy, because it tries to minimize the maximum possible loss (or maximize the minimum possible gain) for the agent.
How does adversarial search work?
One of the most common and simple methods of adversarial search is the minimax algorithm. The minimax algorithm works as follows:
- It assumes that both agents are rational and play optimally.
- It builds a game tree, which is a tree that represents all the possible states and actions of the game, starting from the current state as the root node.
- It assigns a utility value to each leaf node of the game tree, which is the final score or outcome of the game for the agent.
- It propagates the utility values from the leaf nodes to the root node, using the following rules:
- If the node is a max node, which means it is the agent’s turn to move, then the node’s utility value is the maximum of its children’s utility values.
- If the node is a min node, which means it is the opponent’s turn to move, then the node’s utility value is the minimum of its children’s utility values.
- It selects the action that corresponds to the child node with the highest utility value for the root node.
Here is an example of the minimax algorithm applied to a simple game of tic-tac-toe, where X is the agent and O is the opponent:
!Minimax example
The utility values are +1 for a win for X, -1 for a win for O, and 0 for a draw. The root node is a max node, and the algorithm chooses the action that leads to the node with utility value +1, which is placing X in the center.
Why is game playing important for AI?
Game playing is important for AI for several reasons:
- It is a testbed for AI techniques and methods, such as search, knowledge representation, reasoning, learning, and decision making.
- It is a benchmark for AI performance and intelligence, as it allows us to compare and evaluate different AI agents and systems.
- It is a domain for AI applications and innovations, as it can inspire and enable new and useful AI solutions for real-world problems.
Game playing is also fun and engaging, and can help us understand and appreciate the power and limitations of AI. 🎮
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