Introduction to AI Engine & Common Used AI Techniques Created by: Abdelrahman Al-Ogail Under Supervision of: Dr. Ibrahim Fathy

 What’s Game AI?  Why AI Engine?  Structure of AI Engine  Elements That Need AI in RTS Games  Areas That Need Improvement in RTS AI  Common Used Techniques in AI Engine  So why working on that project (what’s new)?

 Let the computer think  Goal of Game AI:  Entertainment NOT perfection  How that guy finds the right answer?  Deeper Blue Example

AI Engine AI Engine That’s our guy

 Workers (peons, gatherers)  Individual units (soldiers, tanks…)  Town building:  how to build my town to max. benefits  Pathfinding  What’s the best (not shortest) way to get from A to B

 Low level strategies  Which pathfinding algorithm I should use?  Medium level strategies  How to achieve high level strategies?  High level strategies  What are my goals?

 Terrain Analysis (keep track of your enemy)  Opponent Modeling (know your enemy)  Resource Management (take the control)  Diplomacy Systems (always have allies)

 Determine when AI element is stuck  Opponent modeling  More strategies less tactics  Construct consistent army (solders, tanks, planes)  Think about support lines  How to retreat  Setup and detect ambushes

 Learning  Some areas of learning: ▪ AI opponent get in the same trap repeatedly ▪ Know safe map locations and get away from kill zones ▪ Know how human player attacks and which units he favors ▪ Does the player rushes ? ▪ Does the player rely on units that require certain resources? ▪ Does he frequently build a number of critical structures in a poorly defensive place? ▪ Are his attacks balanced? ( rock, paper, scissors example)

 Categories of Used Techniques:  Decision Making  Other Techniques ▪ Data-Driven Techniques ▪ Perception Techniques ▪ Communication Techniques

When to use: to represent states

When to use: to represent several states at the same time

 Used to find best solutions to a given problems  Genetic Process Rely on the idea of reproduction  Example of using:  finding best optimal # of peons working in each areas (area = building, money, wood, stone…)

 Rely on simulating human brain  Used in:  Classification  Opponent modeling

 ALife is about searching to find “governing principles” to the life  Newton theorem  Alife Techniques:  Cellular Automata  Steering Behaviors  Add the creativity to the AI opponent

 Simple rules that produces emergent behaviors  Boids research by  Craig Reynolds  Used:  To simulate real life  In producing emergent behavior  Provide autonomies agents

 Planning is, deciding upon a course of action before acting  Usage in games:  Pathfinding algorithms  Set plans to high level strategies in RTS Games  anticipating ambushes  Some of Planning Techniques:  A*, Mean & Analysis, Patch Recalculation, Minimax

 As prolog  Used techniques:  Forward changing  Backward changing

 Complex IF-ELSE Statements represented as tree  Usage if games:  Player modeling  High level strategies

 Scripting Systems  Using an external resource (not coded) that controls the AI opponent  Advantage  Add extendibility to the game

 Supplies communication between game objects

 LBI: Location Based Information Systems  It’s a perception technique  Keeps track of the world attributes  Common techniques:  Influence Maps  Terrain Analysis  Smart Terrain

 Usage in games:  Helps with obstacle avoidance  Detecting player, resources places  Danger specification (keep track of kill zones)  Discover critical points in the world (as bridges)

 Future of wars is going to be more robotic  Sharing & validating plans  MIT asks for researches in this area ( )  Alex J.C. said: “there’s no real learning and adaptation in commercial games”  Researches in this area is so active!  One of the papers date