Adversarial Search
Week#4-Intelligent System (Education Purposes)
Adversarial search is a search, where we examine the problem which arises when we try to plan ahead of the world and other agents are planning against us.
https://www.javatpoint.com/ai-adversarial-search
- Perfect information: A game with the perfect information is that in which agents can look into the complete board. Agents have all the information about the game, and they can see each other moves also. Examples are Chess, Checkers, Go, etc.
- Imperfect information: If in a game agents do not have all information about the game and not aware of what’s going on, such type of games are called the game with imperfect information, such as tic-tac-toe, Battleship, blind, Bridge, etc.
- Deterministic games: Deterministic games are those games that follow a strict pattern and set of rules for the games, and there is no randomness associated with them. Examples are chess, Checkers, Go, tic-tac-toe, etc.
- Non-deterministic games: Non-deterministic are those games that have various unpredictable events and have a factor of chance or luck. This factor of chance or luck is introduced by either dice or cards. These are random, and each action response is not fixed. Such games are also called as stochastic games.
Example: Backgammon, Monopoly, Poker, etc.
Zero-Sum Game
In-game theory and economic theory, a zero-sum game is a mathematical representation of a situation in which each participant’s gain or loss of utility is exactly balanced by the losses or gains of the utility of the other participants.
Minimax Algorithm
Minimax is a kind of backtracking algorithm that is used in decision making and game theory to find the optimal move for a player, assuming that your opponent also plays optimally. It is widely used in two player turn-based games such as Tic-Tac-Toe, Backgammon, Mancala, Chess, etc.
https://www.geeksforgeeks.org/minimax-algorithm-in-game-theory-set-1-introduction/
Thank you
