GamePlaying Read Chapter 6 Adversarial Search Game Types
- Slides: 16
Game-Playing Read Chapter 6 Adversarial Search
Game Types • Two-person games vs multi-person – chess vs monopoly • Perfect Information vs Imperfect – checkers vs card games • Deterministic vs Non-deterministic – go vs backgammon
Two-Person Games: Perfect Information • BF = branching factor (average) • Chess: BF ~36 – expert level • Checkers: BF ~ 8, world champion • Othello: BF ~10, better world champion • Go: BF ~200 – $2 million prize
Mini. Max Algorithm (perfect information, 2 person game) • • Assume: evaluation of terminal position Win = +1, Loss = -1, Draw = 0. Descendants of max node is min node, etc. Algorithm: recursive – Value Max Node = max(descendants of node) – Value Min Node = min(descendants of node) – Value of terminal node: by evaluation function • Applies to any tree with values assigned to leaves. • Needed if full tree too large.
Mini. Max Example
Optimal Play • Make move that yields highest minimax score. • Computation: search: depth-first • Time = b^d • Memory= b*d
Applied to Chess • • • Average game is 40+ moves Tree to large to reach terminal positions Static board evaluation of worthiness Uses Partial Tree Mini. Max yields optimal value for restricted tree, with values assigned by evaluation. • No theorems connecting valuation on partial tree to estimates for complete tree.
Alpha-Beta Algorithm • • • Yields exactly same value as minimax Knuth analyzed: time or nodes = O(b^d/2) Doubles depth of search with same time. Constant depends on ordering of nodes Iterative deepening alpha/beta achieves better ordering. (reorder after depth)
Alpha-beta Algorithm • Each node is assigned a range of values: [alpha, beta]. The real value will lie between. • The root is assigned [-inf, +inf]. • For any max node N with values [A, B] – if a son has value >=C, then N has new range [C, B]. – If interval is empty, all nodes below cut. • For any min node N with values [A, B] – if son has value <=D, then N updated to [A, D]. • Formal code in text. • http: //www. cs. mcgill. ca/~cs 251/Old. Courses/1997/ topic 11/
Alpha-Beta Example
Alpha-Beta Example
(1, 2, 2) Nim
Multi-player Games • Extension of minimax – assign a vector of values to each position – vector has value relative to each player – Each player maximizes choice – Equals minimax for 2 person game • No variations like alpha-beta
Games with Uncertainty • Card games like hearts or bridge • Backgammon (roles of dice) • Expectimax – Does it work? – Theoretically nice, but where’s the meat – for what games was it successful?
Certainty from Uncertainty • Simulation – Replace unknown world by specific world – simulate (or use alpha-beta) – Each simulation yields a play – Vote • Works for hearts and bridge play – bridge high level card play can’t make information gathering plans
What about War • Games are games – restricted uncertainty • What are the operators in war? – unknown effects – unknown number • What is the state? – unknown
- Adversarial search problems uses
- Adversarial stakeholders
- Adversial system
- Adversary system definition
- Hexiangnan
- Friendly adversarial training
- Quantum generative adversarial learning
- On adaptive attacks to adversarial example defenses
- Adversarial interview
- Adversarial system law definition
- Adversarial examples
- Singing
- Spectral normalization gan
- Conditional generator
- Adversarial multi-task learning for text classification
- Voice conversion
- Adversarial training