1.
Seminar on Game Playing in AI by: Neelamani Samal 0501213052 01/10/11 JITM,Parlakhemundi
2.
Definition…. Game Game playing is a search problem defined by: 1. Initial state 2. Successor function 3. Goal test 4. Path cost / utility / payoff function 01/10/11 JITM,Parlakhemundi
3.
Types of Games Perfect Information Game: In which player knows all the possible moves of himself and opponent and their results. E.g. Chess. Imperfect Information Game: In which player does not know all the possible moves of the opponent. E.g. Bridge since all the cards are not visible to player. 01/10/11 JITM,Parlakhemundi
4.
Characteristics of game playing Unpredictable Opponent. Time Constraints. 01/10/11 JITM,Parlakhemundi
5.
Typical structure of the game in AI 2- person game Players alternate moves Zero-sum game: one player’s loss is the other’s gain Perfect information: both players have access to complete information about the state of the game. No information is hidden from either player. No chance (e.g. using dice) involved E.g. Tic- Tac- Toe, Checkers, Chess 01/10/11 JITM,Parlakhemundi
6.
Game Tree Tic – Tac – Toe Game Tree 01/10/11 JITM,Parlakhemumndi
9.
MINIMAX.. 2 players.. MIN and MAX. Utility of MAX = - (Utility of MIN). Utility of game = Utility of MAX. MIN tries to decrease utility of game. MAX tries to increase utility of game. 01/10/11 JITM,Parlakhemundi
11.
Properties of MINIMAX Complete: Yes, if tree is finite Optimal: Yes, against an optimal opponent. Time: O(b d ) (depth- first exploration) Space: O(bd) (depth- first exploration) b: Branching Factor d: Depth of Search Tree Time constraints does not allow the tree to be fully explored. How to get the utility values without exploring search tree up to leaves? 01/10/11 JITM,Parlakhemundi
12.
Evaluation Function Evaluation function or static evaluator is used to evaluate the ‘goodness’ of a game position. The zero-sum assumption allows us to use a single evaluation function to describe the goodness of a position with respect to both players. E.g. f(n) is the evaluation function of the position ‘n’. Then, – f(n) >> 0: position n is good for me and bad for you – f(n) << 0: position n is bad for me and good for you – f(n) near 0: position n is a neutral position 01/10/11 JITM,Parlakhemundi
13.
Alpha Beta Pruning At each MAX node n, alpha(n) = maximum value found so far At each MIN node n, beta(n) = minimum value found so far 01/10/11 JITM,Parlakhemundi
18.
Effectiveness of Alpha Beta Pruning. Worst-Case branches are ordered so that no pruning takes place alpha-beta gives no improvement over exhaustive search Best-Case each player’s best move is the left-most alternative (i.e., evaluated first) In practice often get O(b (d/2) ) rather than O(b d ) e.g., in chess go from b ~ 35 to b ~ 6 this permits much deeper search in the same amount of time makes computer chess competitive with humans! 01/10/11 JITM,Parlakhemundi
19.
Iterative Deepening Search(IDS). IDS runs alpha-beta search with an increasing depth-limit The “inner” loop is a full alpha-beta search with a specified depth limit m When the clock runs out we use the solution found at the previous depth limit 01/10/11 JITM,Parlakhemundi
20.
Applications Entertainment Economics Military Etc… 01/10/11 JITM,Parlakhemundi
21.
Conclusion Game theory remained the most interesting part of AI from the birth of AI. Game theory is very vast and interesting topic. It mainly deals with working in the constrained areas to get the desired results. They illustrate several important points about Artificial Intelligence like perfection can not be attained but we can approximate to it. 01/10/11 JITM,Parlakhemundi
Be the first to comment