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Turn-Based Games Héctor Muñoz-Avila sources: Wikipedia.org Russell & Norvig AI Book; Chapter 5 (and slides)

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Presentation on theme: "Turn-Based Games Héctor Muñoz-Avila sources: Wikipedia.org Russell & Norvig AI Book; Chapter 5 (and slides)"— Presentation transcript:

1 Turn-Based Games Héctor Muñoz-Avila sources: Wikipedia.org Russell & Norvig AI Book; Chapter 5 (and slides) Jonathan Schaeffer’s AAW 05 presentation My own

2 Turn-Based Strategy Games Early strategy games was dominated by turn- based games Derivate from board games Chess The Battle for Normandy (1982) Nato Division Commanders (1985) Turn-based strategy: game flow is partitioned in turns or rounds. Turns separate analysis by the player from actions “harvest, build, destroy” in turns Two classes: Simultaneous Mini-turns

3 Turn-Based Games Continues to be A Popular Game Genre At least 3 sub-styles are very popular: –“Civilization”-style games Civilization IV came out last week –Fantasy-style (RPG) Heroes of Might and Magic series –Poker games Poker Academy

4 Some Historical Highlights 1952 Turing design a chess algorithm. Around the same time Claude Shannon also develop a chess program 1956 Maniac versus Human 1970 Hamurabi. A game about building an economy for a kingdom The Battle for Normandy (1982) 1987 Pirates! 1990 Civilization 1995 HoMM 1996 Civilization II The best game ever? … 2005 Civilization IV 2006 HoMM V

5 Side-tracking: Game Design: Contradicting Principles Principle: All actions can be done from a single screen. Classical example: Civ IVCiv IV But: HoMM uses two interfaces: HoMM IVHoMM IV

6 Coming back: How to Construct Good AI? Idea: Lets just use A* and define a good heuristic for the game  Search space: a bipartite tree  After all didn’t we use it with the 9-puzzle game? Problems with this idea:  Adversarial: we need to consider possible moves of our opponent (s)  Time limit: (think Chess)

7 Types of Adversarial TBGs (from AI perspective) Perfect information Imperfect information Deterministic Chance Chess, Go, rock- paper-scissors Battleships, Stratego Backgammon, monopoly Civilization, HoMM Bridge, Poker

8 Game tree (2-player, deterministic, turns) Concepts: State: node in search space Operator: valid move Terminal test: game over Utility function: value for outcome of the game MAX: 1 st player, maximizing its own utility MIN: 2 nd player, minimizing Max’s utility

9 Minimax Finding perfect play for deterministic games Idea: choose move to position with highest minimax value = best achievable payoff against best play E.g., 2-play game:

10 Minimax algorithm

11 Properties of minimax Complete? Optimal? Time complexity? –b: branching factor –m: # moves in a game Yes (if tree is finite) Yes (against an optimal opponent) O(b m ) For chess, b ≈ 35, m ≈100 for "reasonable" games Therefore, exact solution is infeasible

12 Minimax algorithm with Imperfect Decisions evaluationFunction(state) Cutoff-test(state)

13 Evaluation Function –Is an estimate of the actual utility –Typically represented as a linear function: EF(state) = w 1 f 1 (state) + w 2 f 2 (state) + … + w n f n (state) –Example: Chess weight: Piece  Number  (w 1 ) Pawn  1  (w 2 ) Knight  3  (w 3 ) Bishop  3  (w 4 ) Rook  5  (w 5 ) Queen  9 Function; state  Number  f 1 = #(pawns,w)  #(pawns,b)  f 2 = #(knight,w)  #(knight,b)  f 3 = #(bishop,w)  #(bishop,b)  f 4 = #(rook,w)  #(rook,b)  f 5 = #(knight,w)  #(knight,b)

14 Example: Evaluation Function “all things been equal” White moves, Who is winning? Is this consistent with Evaluation function? Black Yes!

15 Evaluation Function (2) Obviously, the quality of the AI player depends on the evaluation function Conditions for evaluation functions:  If n is a terminal node,  Computing EF should not take long  EF should reflect chances of winning EF(n) = Utility(n) If EF(state) > 3 then is almost-certain that blacks win

16 Cutting Off Search When to cutoff minimax expansion? Potential problem with cutting off search: Horizon problem Solution:  Fixed depth limit  Iterative deepening until times runs out  Decision made by opponent is damaging but cannot be “seen” because of cutoff  Quiescent: states that are unlikely to exhibit wild swings in the values of the evaluation functions

17 Example: Horizon Problem “all things been equal” White moves, Who is winning? Is this consistent with Evaluation function? Black No!

18 α-β pruning: Motivation A good program may search 1000 positions per second In a chess tournament, a player gets 150 seconds per move Therefore, the program can explore 150,000 positions per move With a branching factor of 34, this will mean a look ahead of 3 or 4 moves Facts:  4-turns ≈ human novice  8-turns ≈ typical PC, human master  12-turns ≈ Deep Blue, Kasparov How to look ahead more than 4 turns? Use α-β pruning

19 Example: Finding perfect play for deterministic games Idea: choose move to position with highest minimax value = best achievable payoff against best play E.g., 2-play game:

20 α-β pruning

21 α-β pruning example

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25 Principle of α-β Prunning α is the value of the best (i.e., highest- value) choice found so far at any choice point along the path for max  If v  α, max will avoid it oTherefore, prune that branch β is the lowest-value found so far at any choice point along the path for min  If v  α, min will avoid it oTherefore, prune that branch

26 The α-β algorithm

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28 Properties of α-β Pruning preserves completeness and optimality of original minimax algorithm Good move ordering improves effectiveness of pruning With "perfect ordering," time complexity = O(b m/2 ) Therefore, doubles depth of search Used in PC games today (9 moves look-ahead, Grand Master level)

29 Deterministic games in practice Checkers: Chinook ended 40-year-reign of human world champion Marion Tinsley in Used a precomputed endgame database defining perfect play for all positions involving 8 or fewer pieces on the board, a total of 444 billion positions. Chess: Deep Blue defeated human world champion Garry Kasparov in a six-game match in Deep Blue searches 200 million positions per second, 24 processors, q uiescent identified with help of human grand masters Othello: human champions refuse to compete against computers, who are too good. Go: human champions refuse to compete against computers, who are too bad. In go, b > 300, so most programs use pattern knowledge bases to suggest plausible moves.

30 Additional Notes The next 5 slides are form David W. Aha (NRL) presentation at Lehigh University in Fall’04

31 Example Game: FreeCiv (Chance, adversarial, imperfect information game) Civilization II  (MicroProse) Civilization II  (1996-): 850K+ copies sold –PC Gamer: Game of the Year Award winner –Many other awards Civilization  series (1991-): Introduced the civilization-based game genre Civilization II  (1996-): 850K+ copies sold –PC Gamer: Game of the Year Award winner –Many other awards Civilization  series (1991-): Introduced the civilization-based game genre FreeCiv (Civ II clone) Open source freeware Discrete strategy game Goal: Defeat opponents, or build a spaceship Resource management –Economy, diplomacy, science, cities, buildings, world wonders –Units (e.g., for combat) Up to 7 opponent civs Partial observability Open source freeware Discrete strategy game Goal: Defeat opponents, or build a spaceship Resource management –Economy, diplomacy, science, cities, buildings, world wonders –Units (e.g., for combat) Up to 7 opponent civs Partial observability

32 FreeCiv Scenario General description Game initialization: Your only unit, a “settler”, is placed randomly on a random world (see Game Options below). Players cyclically alternate play Objective: Obtain highest score, conquer all opponents, or build first spaceship Scoring: “Basic” goal is to obtain 1000 points. Game options affect the score. –Citizens: 2 pts per happy citizen, 1 per content citizen –Advances: 20 pts per World Wonder, 5 per “futuristic” advance –Peace: 3 pts per turn of world peace (no wars or combat) –Pollution: -10pts per square currently polluted Top-level tasks (to achieve a high score): –Develop an economy –Increase population –Pursue research advances –Opponent interactions: Diplomacy and defense/combat Game initialization: Your only unit, a “settler”, is placed randomly on a random world (see Game Options below). Players cyclically alternate play Objective: Obtain highest score, conquer all opponents, or build first spaceship Scoring: “Basic” goal is to obtain 1000 points. Game options affect the score. –Citizens: 2 pts per happy citizen, 1 per content citizen –Advances: 20 pts per World Wonder, 5 per “futuristic” advance –Peace: 3 pts per turn of world peace (no wars or combat) –Pollution: -10pts per square currently polluted Top-level tasks (to achieve a high score): –Develop an economy –Increase population –Pursue research advances –Opponent interactions: Diplomacy and defense/combat Game OptionY1Y2Y3 World sizeSmallNormalLarge Difficulty levelWarlord (2/6)Prince (3/6)King (4/6) #Opponent civilizations557 Level of barbarian activityLowMediumHigh

33 FreeCiv Concepts Concepts in an Initial Knowledge Base Resources: Collection and use oFood, production, trade (money) Terrain: oResources gained per turn oMovement requirements Units: oType (Military, trade, diplomatic, settlers, explorers) oHealth oCombat: Offense & defense oMovement constraints (e.g., Land, sea, air) Government Types (e.g., anarchy, despotism, monarchy, democracy) Research network: Identifies constraints on what can be studied at any time Buildings (e.g., cost, capabilities) Cities oPopulation Growth oHappiness oPollution Civilizations (e.g., military strength, aggressiveness, finances, cities, units) Diplomatic states & negotiations Resources: Collection and use oFood, production, trade (money) Terrain: oResources gained per turn oMovement requirements Units: oType (Military, trade, diplomatic, settlers, explorers) oHealth oCombat: Offense & defense oMovement constraints (e.g., Land, sea, air) Government Types (e.g., anarchy, despotism, monarchy, democracy) Research network: Identifies constraints on what can be studied at any time Buildings (e.g., cost, capabilities) Cities oPopulation Growth oHappiness oPollution Civilizations (e.g., military strength, aggressiveness, finances, cities, units) Diplomatic states & negotiations

34 FreeCiv Decisions Civilization decisions Choice of government type (e.g., democracy) Distribution of income devoted to research, entertainment, and wealth goals Strategic decisions affecting other decisions (e.g., coordinated unit movement for trade) Choice of government type (e.g., democracy) Distribution of income devoted to research, entertainment, and wealth goals Strategic decisions affecting other decisions (e.g., coordinated unit movement for trade) City decisions Unit decisions Diplomacy decisions Production choice (i.e., what to create, including city buildings and units) Citizen roles (e.g., laborers, entertainers, or specialists), and laborer placement –Note: Locations vary in their terrain, which generate different amounts of food, income, and production capability Production choice (i.e., what to create, including city buildings and units) Citizen roles (e.g., laborers, entertainers, or specialists), and laborer placement –Note: Locations vary in their terrain, which generate different amounts of food, income, and production capability Task (e.g., where to build a city, whether/where to engage in combat, espionage) Movement Task (e.g., where to build a city, whether/where to engage in combat, espionage) Movement Whether to sign a proffered peace treaty with another civilization Whether to offer a gift Whether to sign a proffered peace treaty with another civilization Whether to offer a gift

35 FreeCiv CP Decision Space Variables Civilization-wide variables oN: Number of civilizations encountered oD: Number of diplomatic states (that you can have with an opponent) oG: Number of government types available to you oR: Number of research advances that can be pursued oI: Number of partitions of income into entertainment, money, & research U: #Units oL: Number of locations a unit can move to in a turn C: #Cities oZ: Number of citizens per city oS: Citizen status (i.e., laborer, entertainer, doctor) oB: Number of choices for city production Civilization-wide variables oN: Number of civilizations encountered oD: Number of diplomatic states (that you can have with an opponent) oG: Number of government types available to you oR: Number of research advances that can be pursued oI: Number of partitions of income into entertainment, money, & research U: #Units oL: Number of locations a unit can move to in a turn C: #Cities oZ: Number of citizens per city oS: Citizen status (i.e., laborer, entertainer, doctor) oB: Number of choices for city production Decision complexity per turn (for a typical game state) O(D N GRI*L U *(S Z B) C ) ; this ignores both other variables and domain knowledge oThis becomes large with the number of units and cities oExample: N=3; D=5; G=3; R=4; I=10; U=25; L=4; C=8; Z=10; S=3; B=10 oSize of decision space (i.e., possible next states): 2.5*10 65 (in one turn!) oComparison: Decision space of chess per turn is well below 140 (e.g., 20 at first move) O(D N GRI*L U *(S Z B) C ) ; this ignores both other variables and domain knowledge oThis becomes large with the number of units and cities oExample: N=3; D=5; G=3; R=4; I=10; U=25; L=4; C=8; Z=10; S=3; B=10 oSize of decision space (i.e., possible next states): 2.5*10 65 (in one turn!) oComparison: Decision space of chess per turn is well below 140 (e.g., 20 at first move)


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