Embed Size (px)
Citation preview
StarCraft Winner PredictionPresented by: Yaser Norouzzadeh
Authors: Y. Norouzzadeh, S. Bakkes, P. Spronck
Tilburg Center for Cognition and CommunicationTilburg University
Tilburg, the Netherlands
7
Winner Prediction• Complexity:
• many action choices• managing units concurrently• different strategies• various match types (PvT, PvZ, TvZ, PvP, ZvZ, and TvT)
• Winner:• The first player who destroy all enemy units
• Application:• Evaluation function for AI bots
9
Dataset Information• Expert replays collected by Synnaeve and Bessiere 2012• Database provided by Robertson and Watson 2014 • Filter condition:
If (game-length < 10 min) or (game-length > 50 min) or (game-winner == Null) Remove replay
Match Type PvT PvZ TvZ PvP ZvZ TvT
Number of replays 2017 840 812 392 199 395
Number of replays(After filtering) 1490 579 612 263 115 298
10
Winning Rate
PvT PvZ TvZ48%
49%
50%
51%
52%
53%
54%
55%
56%
57%
Wining rate percent in non-symmetric match types
13
Feature sets• Time-Dependent Features (TDF)• Time-Independent Features (TIF)
Sample size per match typeMatch types PvT PvZ TvZ PvP ZvZ TvT
Feature samples 24k 9k 9k 3k 1k 4k
14
Extracting Time-Dependent Features
10 seconds Features during 10s
180 seconds TDF(mean,var,dif)
15
Time-Dependent Features• Proposed features:• Number of frequent commands (move, build, tech, hold, siege, and burrow)• Number of micro/macro commands• Number of control/strategy/tactic commands• Number of unique regions that include a building• Difference of building values of all regions
• Well-known features:• Unspent resources: available resources on average at any given time• Income: total resources that are collected over T• APM
17
Time-Independent Features (TIF)• Number of regions• Ratio of buildable tiles• Ratio of walkable tiles• Average of choke distances• Height level ratio (low, low doodads, high, high doodads, very high,
very high doodads)• Map dimension in tiles
19
Problem Formulation• Binary classification: win(1)/lose(0)• Classification methods• Gradient Boosting Regression Trees (GBRT)• Random Forest (RF)
• Approaches:• Individual model for each match type (6 models)• Mixed models
• For symmetric match types (PvP, ZvZ, and TvT)• For non-symmetric match types (PvT, PvZ, and TvZ)
• General model for all match types
21
A=APM and economy featuresB=time-dependent featuresC=time-independent features
Winner Prediction by Individual models
Model Features PvT PvZ TvZ PvP ZvZ TvT
baseline 0.55 0.51 0.56 0.50 0.50 0.50RF A,B,C 0.591 0.611 0.502 0.502 0.515 0.491
GBRT A,B,C 0.595 0.623 0.502 0.502 0.507 0.483
RF A,B 0.644 0.634 0.624 0.643 0.587 0.639
GBRT A,B 0.637 0.634 0.624 0.639 0.581 0.635
22
Winner Prediction by Mixed Models and General Model
Model Features Non-symmetric Symmetric General
RF A,B,C 0.575 0.497 0.591
GBRT A,B,C 0.577 0.499 0.593
RF A,B 0.639 0.637 0.639
GBRT A,B 0.635 0.634 0.635
23
Top-10 TDF In Individual Models
• Income and unspent resources always amongst top three (Except ZvZ)• In ZvZ, micro commands have strongest predictive value• Control commands (move, gather, build, …) and region values are the next strongest predictive value
26
Conclusion• Winner prediction is possible for all match types.• Mixed models also manage to predict the match winner as individual
models.• In all models, top-10 features are more or less the same.• Economic features (income and unspent) are strongest features across
match types
![Bigg Boss 7 Winner Prediction - WordPress.com · here I present the 2013 ... Bigg Boss 7 [or for that matter, any contest] winner prediction ... turn is based on correct birth details,]](https://img.pdfslide.us/doc/110x75/5ade9a707f8b9a9a768e6e8c/bigg-boss-7-winner-prediction-i-present-the-2013-bigg-boss-7-or-for-that.jpg)
