Adaptive AI Engine for RTS Games

Discussing the theory and practice

AI Engine Structure for RTS Games

Posted by Ogail on June 29, 2009

AI Engine Structure for RTS Games

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  • Aim, discuss following:
    • The elements that require some form of AI in RTS games
    • AI techniques used in finished products
    • Real-world examples
    • Notable exceptions
    • Areas of improvement
  • Common AI Elements:
    • Individual Players:
      • The more individual intelligence a unit has, the less you have to chech your army
      • Replicating behavior (attack/retrieve pattern)
    • Economic Individual Units:
      • Called peons (builders, gatherers)
      • Techniques used:
        • Order Queue: make order queue to each unit
        • Auto-retreating: retrieving when there is an attack and after danger go they get back to work
          • Improvement: go to more secure place! Saying Help!
    • Commanders and Medium-level Strategic Elements:
      • Controlling group of elements in a bigger war sense
      • This is medium level AI not low (such as shoot) and not high (such as defense base)
    • High Level Strategic AI
      • Think of things in general as a real army
      • Here it’s multifaceted
    • Town Building:
      • Criteria of building the buildings are hard to cover all
  • Indigenous Life:
    • As sheep, wolf …
  • Pathfinding (PF):
    • Why pathfinding is one of the most important aspects in AI
    • Flocking techniques and follow-the leader-type systems will improve the speed of per-unit PF
  • Tactical and Strategic Support Systems:
    • Techniques that makes the decisions of AI systems appear smart:
      • Terrain Analysis:
        • Dividing terrains into meaningful chunks and breaking down each aspect of chuck, then AI can glean huge amount of useful data in strategic decisions:
          • Used in pathfinding
          • Keep track of enemy base locations, resources, finding holes,
        • Most of this is done by influence map (fancy name for grid-based map)
        • Intelligent collection of resources (centered resource)
    • Opponent Modeling “Know your enemy”:
      • AI controller can’t ASSUME what his opponent will do
      • AI should build a model of his opponent
      • Some of data to consider: which unit player use, time interval between attacks…)
    • Resource Management:
      • Using need-base system AI opponent could be implemented on map type a attributes
    • Reconnaissance:
      • Explaining the concept of “fog of war”
      • Bottleneck of keeping the data up-to-date
        • Common solutions: tower, guard-post placement or complex wall building
      • Diplomacy Systems:
        • Synchronizing work between AI opponent and its allies
  • Useful AI Techniques in RTS Games:
    • Messaging:
      • Messaging systems can be used for broadcasting of events and game flags to large number of registered units quickly and easily
    • Finite-State-Machines (FSM)
      • Individual work unit, AI most likely implemented as stack-based FSMs so that they can be temporally interrupted then restored easily
      • Systems within the strategy level, a city building might be a FSM with an offline constructed build order that has been proven to work
    • Fuzzy-State-Machines (FuSM):
      • Help AI to take decision in blend of opponent behavior not cheating!
      • There are several ways to victory FuSMs help in determining which one to use
    • Hierarchal AI:
      • Attacked group example
        • Answer is determined by the amount of strategic Vs. tactics
        • Hierarchical systems provide a means for RTS games to form high-level goals but also appear smart at unit level, without choking (الإختناق) the primary AI system for resources
    • Planning:
      • Planning is important to accomplish higher-level tasks
      • Even running away from threats need some level of thinking beyond just pathfinding
    • Scripting:
      • Used to describe certain unit behavior under certain conditions
    • Data Driven AI (135):
      • Many RTS games are putting large portions of AI decision making into non-code form
      • Advantages: level designers and gamers can change them later easily
      • Example:


  • Developed RTS Games Examples:
    • Herzog Zwei was developed using simple AI as FSM in states like GetMoney. Defines, Attack


  • Warcraft, Starcraft and Command & Conquer emphasis on pathfinding not influence maps or better planning algorithms because the memory and processing limitations
  • Later games used super-units and heroes (instead of just using mindless units)
  • Areas That Need Improvement:
    • Learning
      • RTS AI get in the same trap repeatedly
      • The AI should take into account successful travel information about map locations (using influence map techniques), so it didn’t go to kill zones where there are areas of migration


  • Determine when AI Element is Stuck:
    • What to do in stuck situations (from a peon to a group of tanks)
    • The AI should know it has been beaten! (The last peon problem)
  • AI Helper (to the human player)
  • Opponent Personality:
    • Herzog Zwei had two opponent AI personalities (offense based defensive based
    • Examples: AI madness
  • More Strategy, Less Tactics:
    • What we mean by this title?
    • Make it real: clicking the mouse and hitting hot keys
    • Some common strategic systems that the RST AI should accomplish:
      • Grouping units by type and then using groups to back up other groups
        • Respond to specific threats with the correct counter type unit
        • When attacking make each suitable unit to attack object
      • Setting up attack lines to take advantage of multiple fronts and leave support lines open for additional forces to come on
      • Using terrain feature to setup optimal wall structure
      • How to retreat
      • Setup ambush situations:
        • Get the enemy army to an ambush place

Get enemy army out from base and attack after that!

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