程序代写 COMP 424 – Artificial Intelligence Game Playing – cscodehelp代写 – INTERVIEW&CODEHELP

COMP 424 – Artificial Intelligence Game Playing
Instructor: Jackie CK Cheung and Readings: R&N Ch 5

Quick recap

Standard assumptions (except for the lecture on uncertainty):
• Discrete (vs continuous) state space
• Deterministic (vs stochastic) environment
• Observable (vs unobservable) environment
• Static (vs changing) environment
• There is only a single AI agent
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Quick recap
Standard assumptions (except for the lecture on uncertainty):
• Discrete (vs continuous) state space
• Deterministic (vs stochastic) environment
• Observable (vs unobservable) environment
• Static (vs changing) environment
• There is only a single AI agent
• In the Tic-tac-toe exercise, we pushed uncertainty from the other
agent’s moves into “the environment” (AND nodes)
• Doesn’t account for the fact that the other player has its own goal
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Today’s lecture
• Adversarial search → games with 2 players
• Planning ahead in a world where other agents are planning
against us
• Minimax search
• Evaluation functions
• Alpha-beta pruning
• State-of-the-art game playing programs
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Game playing
• One of the oldest, best studied domains in AI! Why?
• They’re fun! People enjoy games and are good at playing them
• Many games are hard:
• State spaces can be huge and complicated
• Chess has branching factor of 35 and games go to 50 moves per player → 35100 nodes in the search tree!
• Games may be stochastic and partially observable
• Real-time constraints (e.g., fixed amount of time between moves)
• Games require the ability to make some decision even when calculating the optimal decision is infeasible
• Clear, clean description of the environment and actions
• Easy performance indicator: winning is everything
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Types of games
• Perfect information vs. Imperfect information
• Perfect: players observe the exact state of the game
• Imperfect: information is hidden from players
• Fully observable vs. partially observable
• Deterministic vs. Stochastic
• Deterministic: state changes are fully determined by player moves • Stochastic: state changes are partially determined by chance
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What type of game?
Perfect information? Yes
Checkers Othello Chess Go
Mastermind Solitaire*
Backgammon
Scrabble Monopoly
Most card games
Deterministic
Stochastic
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Game playing as search
• Consider 2-player, turn-taking, perfect information, deterministic games
• Can we formulate them as a search problem?
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Game playing as search
• Consider 2-player, turn-taking, perfect information, deterministic games
• Can we formulate them as a search problem? Yes!
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Game playing as search
• Consider 2-player, turn-taking, perfect information, deterministic games
• Can we formulate them as a search problem? Yes! State, s: the state of the board + which player moves
Operators, o:
Transition fcn:
Legal moves in a given state Defines the result of a move States in which the game is over
Terminal states: (won/lost/drawn)
Utility fcn: Defines a numeric value for the game for player p, when the game ends in terminal
Simple case: +1 for win, -1 for loss, 0 for draw. More complex cases: points won, money, …
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Game playing as search
• Consider 2-player, turn-taking, perfect information, deterministic games
• Can we formulate them as a search problem? Yes!
• We want to find a strategy (a way of picking moves) that
maximizes utility
• i.e., maximize the probability of winning or the expected points,
minimize the cost, …
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Game search challenge
• It’s not quite the same as simple searching
• There’s an opponent! That opponent is adversarial!
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Game search challenge
• It’s not quite the same as simple searching
• There’s an opponent! That opponent is adversarial!
• The opponent has its own goals which don’t match our goals
• Opponent tries to make things good for itself and bad for us
• We must simulate the opponent’s decisions
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Quick aside
• In games, there’s an adversarial opponent
• It has its own goals which don’t match our goals
• A special case: the zero-sum game
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Quick aside
In games, there’s an adversarial opponent
• It has its own goals which don’t match our goals
• A special case: the zero-sum game
Zero-sum games
• Each player’s gain or loss of utility is exactly balanced by the
losses or gains of the utility of the other player
• If I win, you lose: U(p1) = 1 → U(p2) = -1 U(p1) + U(p2) = 0
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Game search challenge
• It’s not quite the same as simple searching
• There’s an opponent! That opponent is adversarial!
• It has its own goal which does not match our goal
• We must simulate the opponent’s decisions
• Key idea: Define
• a max player (who wants to maximize the utility)
• a min player (who wants to minimize it)
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Example: Game Tree for Tic-Tac-Toe
Term for the move of one player. A full move is two ply.
Search Tree:
A tree superimposed on the full game tree that examines enough nodes for the player to determine what move to make.
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Minimax search
An algorithm for finding the optimal strategy: the best move to play at each state (node)
How it works:
Expand the complete search tree until terminal states have been reached
Compute utilities of the terminal states
Back up from the leaves towards the current game state:
• At each min node: back up the worst value among its children
• At each max node: back up the best value among its children
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Minimax search
• Expand the search tree to the terminal states
• Compute utilities at terminal states
• Back up from the leaves towards the current game state
• At each min node: back up the worst value among the children
• At each max node: back up the best value among the children
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Minimax search
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Minimax search
The minimax value at each node tells us how to play an optimal game!
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Minimax Algorithm
operator MinimaxDecision() for each legal operator o:
apply the operator o and obtain the new game state s.
Value[o] = MinimaxValue(s)
return the operator o with the highest value Value[o].
double MinimaxValue(s)
if isTerminal(s), return Utility(s). for each state s’ in Successors(s)
let Value(s’) = MinimaxValue(s’).
if Max’s turn to move in s, return maxs’Value(s’). if Min’s turn to move in s, return mins’Value(s’).
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• Apply minimax search to the following game search tree
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Apply minimax search to the following game search tree 7
27 Max4 27
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Properties of Minimax search
• Complete? • Optimal?
• Time complexity?
• Space complexity?
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Properties of Minimax search
• Complete? If the game tree is finite
• Optimal? Against an optimal opponent, yes
• It maximizes the worst-case outcome for Max
• There might exist better strategies for suboptimal opponents
• Time complexity? O(bm)
• Space complexity? O(bm) if we use DFS
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Properties of Minimax search
• Complete? If the game tree is finite
• Optimal? Against an optimal opponent, yes
• It maximizes the worst-case outcome for Max
• There could be superior strategies for suboptimal opponents
• Time complexity? O(bm)
• Space complexity? O(bm) if we use DFS
• So is Minimax suitable for solving chess?
• In chess: b≈35, m≈100, so an exact solution is impossible!
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Coping with resource limitations
• Suppose we have 100 seconds to make a move, and we can search 104 nodes per second
• Then we can only search 106 nodes
(Or even fewer, if we spend time deciding which nodes to search)
• Possible approach:
• Use a cutoff test (e.g., based on a depth limit)
• Use an evaluation function for the nodes where we cut the search (since they’re not terminal states, we don’t know the true utility)
• Need to think about real-time search
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Evaluation functions
• An evaluation function v(s) estimates the expected utility
of a state (e.g., the likelihood of winning from that state)
• Performance depends strongly on the quality of v(s)
• If it is too inaccurate it will lead the agent astray
• Desiderata:
• It should order the terminal states according to their true utility
• It should be relatively quick to compute
• For nonterminal states, it should be strongly correlated with the actual chances of winning
• An evaluation function can be designed by an expert or learned from experience
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Evaluation functions
• Most evaluation functions work by calculating features of the state (e.g., # of pawns, queens, etc.)
• Features define categories or equivalence classes of states
• Typically, we compute features separately and then combine
them for an aggregate value
• E.g., if the features of the board are independent, use a weighted linear function:
v(s) = w1 f1(s) + w2 f2(s) + … + wn fn(s)
• Independence is a strong, likely incorrect assumption. But it could still be useful!
• An extra bishop is worth more in the endgame; its weight should depend on the “move number” feature
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Example: Chess
Black to move White to move White slightly better Black winning
• Linear evaluation function: v(s) = w1 f1(s) + w2 f2(s) f1(s) = (# white queens) – (# black queens)
f2(s) = (# white pawns) – (# black pawns)
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Example: Chess
Black to move White to move White slightly better Black winning
• Linear evaluation function: v(s) = w1 f1(s) + w2 f2(s)
f1(s) = (# white queens) – (# black queens) f2(s) = (# white pawns) – (# black pawns)
w1 = 9 w2 = 3
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How precise should the evaluation fcn be?
• For deterministic games, all that matters is that the fcn preserve the ordering of the nodes
• Thus, the move chosen is invariant under monotonic transformations of the evaluation function
• In deterministic games, payoff acts as an ordinal utility function
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Minimax with an evaluation function
• Use the evaluation function to evaluate non-terminal nodes
• This helps make a decision without searching until the end of the game
• Minimax cutoff algorithm:
Same as standard Minimax, except stop at some maximum depth m, use the evaluation function on those nodes, back up from there
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Minimax cutoff in chess
• How many moves ahead can we search in chess?
If our hardware could search 106 nodes in the available time, then
minimax cutoff with b=35 could search 4 moves ahead
• Is that good?
4 moves ahead ≈ novice player
8 moves ahead ≈ human master, typical PC 12 moves ahead ≈ Deep Blue, Kasparov
• Key idea:
Instead of exhaustive search, let’s search a few lines of play, but deeply We need pruning!
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α-β pruning
• Simple idea: if a path looks worse than what we already have, then discard it (prune)
• If the best move at a node cannot change (regardless of what we would find by searching), there’s no need to search further!
• α-β is a standard technique for deterministic, perfect information games
• How does it work?
• It uses two parameters, α and β, to track bounds on the utility or
evaluation values
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α-β pruning
• Proceed like Minimax, with DFS and then back ups
• Additionally, keep track of:
• the best (highest) leaf value found for Max (in α)
• the best (lowest) leaf value found for Min (in β)
• At max nodes, update α only
• At min nodes, update β only
• Prune in the event of inconsistency (α ≥ β)
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Initialize α and β
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In this search, we pruned away two leaf nodes compared to Minimax.
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• The textbook’s version of this example (Edition 3, p. 168, Figure 5.5) shows the correct steps, but with incorrect and confusing alpha and beta values, which don’t match their algorithm!
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α-β pruning algorithm
double MaxValue(s,α,β)
if cutoff(s), return Evaluation(s).
for each state s’ in Successors(s)
let α = max { α, MinValue(s’,α,β) }. if α ≥ β, return β.
double MinValue(s,α,β)
if cutoff(s), return Evaluation(s). for each state s’ in Successors(s)
let β = min { β, MaxValue(s’,α,β) }.
if α ≥ β, return α. return β.
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Important lessons of α-β pruning
• Pruning can greatly increase efficiency!
• Pruning does not affect the final result.
• The best moves are same as returned by Minimax (assuming the opponent is optimal and the evaluation function is correct.)
• Order matters for search efficiency!
• α-β pruning demonstrates the value of reasoning about
which computations are important!
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Efficiency of α-β pruning
• With bad move ordering, time complexity is O(bm) • The same as Minimax since nothing is pruned
• With perfect ordering, time complexity is O(bm/2)
• This means double the search depth for the same resources!
• In chess: the difference between a novice and expert agent
• On average, O(b3m/4), if we expect to find the max/min after b/2 expansions
• Randomizing the move ordering can achieve the average
• An Evaluation function can be used to order the nodes
• A useful ordering fcn for chess: try captures first, then threats, then forward moves, then backward moves
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Drawbacks of α-β
• If the branching factor is really big, search depth is still too limited. E.g., Go, where branching factor is about 300
• Optimal play is guaranteed against an optimal opponent if search proceeds to the end of the game. But the opponent may not be optimal!
• If heuristics are used, this assumption turns into the opponent playing optimally according to the same heuristic function as the player.
• This is a very big assumption! What if the opponent plays very differently?
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Forward pruning
Idea (for domains with large branching factor): Only explore the n best moves for current state (according to the evaluation function)
• Unlike α-β pruning, this can lead to suboptimal solutions
• But it can be very efficient with a good evaluation function
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State-of-the-art game playing programs

Chinook (Schaeffer et al., U. of Alberta)
• Best checkers player (since 1990’s)
• Plain α-β search, performed on standard PCs
• Evaluation function based on expert features of the board
• Opening move database
• HUGE endgame database!
Chinook has perfect information for all checkers positions involving 8
or fewer pieces on the board (a total of 443,748,401,247 positions)
• Only a few moves in middle of the game are actually searched
• They’ve now done an exhaustive search for checkers, and through optimal play can force at least a draw
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Deep Blue (IBM)
• Specialized chess processor, special-purpose memory architecture
• Very sophisticated evaluation function (expert features, tuned weights)
• Database of standard openings/closings
• Uses a version of α-β pruning (with undisclosed improvements)
• Can search up to 40-deep in some branches
• Can search over 200 billion positions per second!
• Overall, an impressive engineering feat
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Computer Chess
• Now, several computer programs running on regular hardware are on par with human champions (e.g., Fritz, Stockfish)
• ELO ratings of top chess bots are estimated at over 3500
• C.f., rating of 2500 needed to become a Grandmaster
• Top human players at just over 2800
• Human chess players use chess bots as a way to analyze and improve their game
• Interesting preview of how humans and AI can cooperate?

AlphaGo (DeepMind, 2016)
• Uses Monte Carlo tree search (more on this next class!) to simulate future game states
• Uses deep reinforcement learning (i.e., multi-layer neural networks + reinforcement learning) to learn how to value moves (policy network) and board states (value network):
• These are machine learning techniques
• Requires access to a database of previous games by expert players
• Performance:
• March 2016: 4W-1L record against
• 2017: 60W-0L record after further training against human pros
• AlphaZero (2017) removed the need for human game data, learned to play “from scratch” against itself!
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MuZero (DeepMind, 2020)

Some possible strategies for AI in games
• Design a compact state representation
• Search using iterative deepening for real-time play
• Use alpha-beta pruning with an evaluation function
• Order moves using the evaluation function
• Tune the evaluation function using domain knowledge, trial-and-error, learning
• Searching deeper is often more important than having a good evaluation function
• Consider using different strategies for opening, middle and endgame (including look-ups)
• Consider that the opponent might not be optimal
• Decide where to spend the computation effort!
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• Understand the different types of games
• Understand Minimax and alpha-beta pruning, what they compute, how to implement, and common methods to make them work better (e.g., node ordering)
• Understand the role of the evaluation function and desirable characteristics
• Get familiar with the state-of-the-art for solving some common games
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Extra Reading (R&N)
• Stochastic games
• Add chance nodes to the search try,
• Minimax → Expectiminimax
• α-β applies (with modifications for chance nodes)
• More sophisticated move ordering schemes
• killer moves
• transposition tables
• More sophisticated cutoffs
• quiescence search
• singular extensions
• Multiplayer games
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Human or computer – who is better?
• 1994: Chinook

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程序代写 COMP 424 – Artificial Intelligence Game Playing – cscodehelp代写

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