# Adversarial Search and Game- Playing. C H A P T E R 5 C M P T : S u m m e r O l i v e r S c h u l t e

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1 Adversarial Search and Game- Playing C H A P T E R 5 C M P T : S u m m e r O l i v e r S c h u l t e

2 Environment Type Discussed In this Lecture 2 Fully Observable yes Turn-taking: Semi-dynamic Deterministic and non-deterministic yes Multi-agent yes Sequential no yes Discrete Discrete no Game Tree Search CMPT Blind Search yes Game Matrices Continuous Action Games

3 Adversarial Search Examine the problems that arise when we try to plan ahead in a world where other agents are planning against us. A good example is in board games. Adversarial games, while much studied in AI, are a small part of game theory in economics.

4 Typical AI assumptions Two agents whose actions alternate Utility values for each agent are the opposite of the other creates the adversarial situation Fully observable environments In game theory terms: Zero-sum games of perfect information. We ll relax these assumptions later.

5 Search versus Games Search no adversary Solution is (heuristic) method for finding goal Heuristic techniques can find optimal solution Evaluation function: estimate of cost from start to goal through given node Examples: path planning, scheduling activities Games adversary Solution is strategy (strategy specifies move for every possible opponent reply). Optimality depends on opponent. Why? Time limits force an approximate solution Evaluation function: evaluate goodness of game position Examples: chess, checkers, Othello, backgammon

6 Types of Games Perfect information Imperfect information (Initial Chance Moves) deterministic Chess, checkers, go, othello Bridge, Skat Chance moves Backgammon, monopoly Poker, scrabble, blackjack on-line backgam mon on-line chess tic-tactoe Theorem of Nobel Laureate Harsanyi: Every game with chance moves during the game has an equivalent representation with initial chance moves only. A deep result, but computationally it is more tractable to consider chance moves as the game goes along. This is basically the same as the issue of full observability + nondeterminism vs. partial observability + determinism.

7 Game Setup Two players: MAX and MIN MAX moves first and they take turns until the game is over Winner gets award, loser gets penalty. Games as search: Initial state: e.g. board configuration of chess Successor function: list of (move,state) pairs specifying legal moves. Terminal test: Is the game finished? Utility function: Gives numerical value of terminal states. E.g. win (+1), lose (-1) and draw (0) in tic-tac-toe or chess MAX uses search tree to determine next move.

8 Size of search trees b = branching factor d = number of moves by both players Search tree is O(b d ) Chess b ~ 35 D ~100 - search tree is ~ (!!) - completely impractical to search this Game-playing emphasizes being able to make optimal decisions in a finite amount of time Somewhat realistic as a model of a real-world agent Even if games themselves are artificial

9 Partial Game Tree for Tic-Tac-Toe

10 Game tree (2-player, deterministic, turns) How do we search this tree to find the optimal move?

11 Minimax strategy: Look ahead and reason backwards Find the optimal strategy for MAX assuming an infallible MIN opponent Need to compute this all the down the tree Game Tree Search Demo Assumption: Both players play optimally! Given a game tree, the optimal strategy can be determined by using the minimax value of each node. Zermelo 1912.

12 Two-Ply Game Tree

13 Two-Ply Game Tree

14 Two-Ply Game Tree

15 Two-Ply Game Tree Minimax maximizes the utility for the worst-case outcome for max The minimax decision

16 Pseudocode for Minimax Algorithm function MINIMAX-DECISION(state) returns an action inputs: state, current state in game v MAX-VALUE(state) return the action in SUCCESSORS(state) with value v function MAX-VALUE(state) returns a utility value if TERMINAL-TEST(state) then return UTILITY(state) v -! for a,s in SUCCESSORS(state) do v MAX(v,MIN-VALUE(s)) return v function MIN-VALUE(state) returns a utility value if TERMINAL-TEST(state) then return UTILITY(state) v! for a,s in SUCCESSORS(state) do v MIN(v,MAX-VALUE(s)) return v

17 Example of Algorithm Execution MAX to move

18 Minimax Algorithm Complete depth-first exploration of the game tree Assumptions: Max depth = d, b legal moves at each point E.g., Chess: d ~ 100, b ~35 Criterion Minimax Time O(b d ) Space O(bd)

19 Practical problem with minimax search Number of game states is exponential in the number of moves. Solution: Do not examine every node => pruning Remove branches that do not influence final decision Revisit example

20 Alpha-Beta Example Do DF-search until first leaf Range of possible values [-!,+!] [-!, +!]

21 Alpha-Beta Example (continued) [-!,+!] [-!,3]

22 Alpha-Beta Example (continued) [-!,+!] [-!,3]

23 Alpha-Beta Example (continued) [3,+!] [3,3]

24 Alpha-Beta Example (continued) [3,+!] This node is worse for MAX [3,3] [-!,2]

25 Alpha-Beta Example (continued) [3,14], [3,3] [-!,2] [-!,14]

26 Alpha-Beta Example (continued) [3,5], [3,3] ["!,2] [-!,5]

27 Alpha-Beta Example (continued) [3,3] [3,3] ["!,2] [2,2]

28 Alpha-Beta Example (continued) [3,3] [3,3] [-!,2] [2,2]

29 Alpha-beta Algorithm Depth first search only considers nodes along a single path at any time α = highest-value choice that we can guarantee for MAX so far in the current subtree. β = lowest-value choice that we can guarantee for MIN so far in the current subtree. update values of α and β during search and prunes remaining branches as soon as the value is known to be worse than the current α or β value for MAX or MIN. Alpha-beta Demo.

30 Effectiveness of Alpha-Beta Search Worst-Case branches are ordered so that no pruning takes place. In this case 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, performance is closer to best rather than worst-case In practice often get O(b (d/2) ) rather than O(b d ) this is the same as having a branching factor of sqrt(b), since (sqrt(b)) d = b (d/2) i.e., we have effectively gone from b to square root of b e.g., in chess go from b ~ 35 to b ~ 6 this permits much deeper search in the same amount of time Typically twice as deep.

31 Example MAX -which nodes can be pruned? MIN MAX

32 Final Comments about Alpha-Beta Pruning Pruning does not affect final results Entire subtrees can be pruned. Good move ordering improves effectiveness of pruning Repeated states are again possible. Store them in memory = transposition table

33 Practical Implementation How do we make these ideas practical in real game trees? Standard approach: cutoff test: (where do we stop descending the tree) depth limit better: iterative deepening cutoff only when no big changes are expected to occur next (quiescence search). evaluation function When the search is cut off, we evaluate the current state by estimating its utility using an evaluation function.

34 Static (Heuristic) Evaluation Functions An Evaluation Function: estimates how good the current board configuration is for a player. Typically, one figures how good it is for the player, and how good it is for the opponent, and subtracts the opponents score from the players Othello: Number of white pieces - Number of black pieces Chess: Value of all white pieces - Value of all black pieces Typical values from -infinity (loss) to +infinity (win) or [-1, +1]. If the board evaluation is X for a player, it s -X for the opponent. Many clever ideas about how to use the evaluation function. e.g. null move heuristic: let opponent move twice. Example: Evaluating chess boards, Checkers Tic-tac-toe

35

36 Iterative (Progressive) Deepening In real games, there is usually a time limit T on making a move How do we take this into account? using alpha-beta we cannot use partial results with any confidence unless the full breadth of the tree has been searched So, we could be conservative and set a conservative depth-limit which guarantees that we will find a move in time < T disadvantage is that we may finish early, could do more search In practice, iterative deepening search (IDS) is used IDS runs depth-first search with an increasing depth-limit when the clock runs out we use the solution found at the previous depth limit

37 The State of Play Checkers: Chinook ended 40-year-reign of human world champion Marion Tinsley in Chess: Deep Blue defeated human world champion Garry Kasparov in a six-game match in Othello: human champions refuse to compete against computers: they are too good. Go: human champions refuse to compete against computers: they are too bad b > 300 (!) See (e.g.) for more information

38

39 Deep Blue 1957: Herbert Simon within 10 years a computer will beat the world chess champion 1997: Deep Blue beats Kasparov Parallel machine with 30 processors for software and 480 VLSI processors for hardware search Searched 126 million nodes per second on average Generated up to 30 billion positions per move Reached depth 14 routinely Uses iterative-deepening alpha-beta search with transpositioning Can explore beyond depth-limit for interesting moves

40 Summary Game playing can be effectively modeled as a search problem Game trees represent alternate computer/opponent moves Evaluation functions estimate the quality of a given board configuration for the Max player. Minimax is a procedure which chooses moves by assuming that the opponent will always choose the move which is best for them Alpha-Beta is a procedure which can prune large parts of the search tree and allow search to go deeper For many well-known games, computer algorithms based on heuristic search match or out-perform human world experts.

41 AI Games vs. Economics Game Theory Seminal Work on Game Theory: Theory of Games and Economic Behavior, 1944, by von Neumann and Morgenstern. Agents can be in cooperation as well as in conflict. Agents may move simultaneously/independently.

42 Example: The Prisoner s Dilemma Other Famous Matrix Games: Chicken Battle of The Sexes Coordination

43 Solving Zero-Sum Games Perfect Information: Use Minimax Tree Search. Imperfect Information: Extend Minimax Idea with probabilistic actions. von Neumann s Minimax Theorem: there exists an essentially unique optimal probability distribution for randomizing an agent s behaviour.

44 Matching Pennies Why should the players randomize? What are the best probabilities to use in their actions? Heads Tails Heads 1,-1-1,1 Tails -1,1 1,-1

45 Nonzero Sum Game Trees The idea of look ahead, reason backward works for any game tree with perfect information. I.e., also in cooperative games. In AI, this is called retrograde analysis. In game theory, it is called backward induction or subgame perfect equilibrium. Can be extended to many games with imperfect information (sequential equilibrium).

46 Backward Induction Example: Hume s Farmer Problem H 1 Not H H1 2 noth 2 1 H2 Not H

47 Summary: Solving Games Zero-sum Non zero-sum Perfect Information Minimax, alpha-beta Backward induction, retrograde analysis Imperfect Information Probabilistic minimax Nash equilibrium Nash equilibrium is beyond the scope of this course.

48 Single Agent vs. 2-Players Every single agent problem can be considered as a special case of a 2-player game. How? 1. Make one of the players the Environment, with a constant utility function (e.g., always 0). 1. The Environment acts but does not care. 2. An adversarial Environment, with utility function the negative of agent s utility. 1. In minimization, Environment s utility is player s costs. 2. Worst-Case Analysis. 3. E.g., program correctness: no matter what input user gives, program gives correct answer. So agent design is a subfield of game theory.

49 Single Agent Design = Game Theory Von Neumann-Morgenstern Games Decision Theory = 2-player game, 1st player the agent, 2 nd player environment/nature (with constant or adversarial utility function) From General To Special Case Markov Decision Processes Planning Problems

50 Example: And-Or Trees If an agent s actions have nondeterministic effects, we can model worst-case analysis as a zero-sum game where the environment chooses the effects of an agent s actions. Minimax Search And-Or Search. Example: The Erratic Vacuum Cleaner. When applied to dirty square, vacuum cleans it and sometimes adjacent square too. When applied to clean square, sometimes vacuum makes it dirty. Reflex agent: same action for same location, dirt status.

51 And-Or Tree for the Erratic Vacuum 7 GOAL 5 LOOP Suck 1 Suck 1 6 LOOP Suck 8 5 GOAL Right Left 5 1 Left LOOP LOOP Right 2 GOAL Suck 8 4 The agent moves at labelled OR nodes. The environment moves at unlabelled AND nodes. The agent wins if it reaches a goal state. The environment wins if the agent goes into a loop.

52 Summary Game Theory is a very general, highly developed framework for multi-agent interactions. Deep results about equivalences of various environment types. See Chapter 17 for more details.

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