User:Dsekercioglu/Thoughts on targeting
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Revision as of 20:44, 8 September 2017 by Dsekercioglu (talk | contribs) (Created page with "==Introduction== When your robot fights against a weaker robot about %55 - %65 of the score comes from damage. ==Prediction Technics== There are mainly 2 ways for predicting ...")
Contents
Introduction
When your robot fights against a weaker robot about %55 - %65 of the score comes from damage.
Prediction Technics
There are mainly 2 ways for predicting where the enemy will be.
- 1. Angular Systems.
- 2. Positional Systems.
- Angular Systems
- Generally GuessFactors are used.
- Works better against perpendicular movement.
- Positional Systems
- Linear Targeting, Circular Targeting, Pattern Matching are used.
- The most efficient system so far is Play It Forward.
Choosing the Correct Angle/Position
- Oscillators change their direction periodically.
- When close to walls some bots change direction.
- Many bots tries to get away from the enemy when close.
- Stop And Go bots move less when bullet float time is low.
- Decelerating bots generally changes direction.
- Classification algorithms are used here to get the best prediction.
- The most used 2 algorithms are KNN(DC) and VCS.
Choosing Attributes
- Lateral Velocity
- Lateral Velocity has a direct affect on enemy movement.
- Simple movements generally keeps the velocity while moving.
- Advancing Velocity
- Advancing velocity helps you to understand if enemy is coming closer or moving away.
- Useful against: Bots doing distancing, bots moving non-perpendicularly.
- Bullet Float Time
- Helps to hit non perpendicular movement.
- For example Walls, SpinBot.
- Forward/Backward Wall MEA
- Helps to hit nearly any type of movement.
- If a bot changes direction after hitting or before hitting the wall.
- If a bot does wall smoothing
- If a bot gets stuck in the corners
Forward/Backward Wall MEA will increase your hit rate.
- Time Since Attributes
Useful against Oscillator,Stop And Go, Direction Based Random Movement.
Targeting Learning Movements
- When a bot with a learning movement gets hit it won't do the same movement for a very long time.
- For the gun to be more adaptive it needs Data Decay so
- The gun forgets the old moves quickly
- Adapts faster to the new movement.
- This can be done in two ways
- A simple formula for VCS(Look at Data Decay)
- Having a fired bullets attribute