Adaptive AI Engine for RTS Games

Discussing the theory and practice

Game AI

Posted by Ogail on August 20, 2009

Game AI

  • It is a common mistake to think that the more complex the AI in a game, the better the characters will look to the player. Creating good AI is all about matching the right behaviors to the right algorithms
  • Knowing when to be complex and when to stay simple is the most difficult element of the game AI programmer’s art. The best AI programmers are those who can use a very simple technique to give the illusion of complexity
  • You need to make sure that a characters’ AI matches their purpose in the game and the attention they’ll get from the player
  • Perceptions should only be what exactly AI character need not more
  • Take care of changing behaviors (2 solders conversion example – 63)
  • For in-game AI, behaviorism is the way to go. We are not interested in the nature of reality or mind; we want characters that look right. In most cases, this means starting from human behaviors and trying to work out the easiest way to implement them in software
  • Developing Game AI Could Use:
    • Hacks: ad-hoc solutions and neat effects
    • Heuristics: rules of thumb that only work in most, but not all, case
    • Algorithms (the “proper” stuff)
  • Developers rarely build a great new algorithm and then ask themselves, “So what can I do with this?” Instead, you start with a design for a character and apply the most relevant tool to get the result
  • Some if the AI doesn’t require an algorithm or a technique it only requires a simple bit of code that performs an animation at the right point
  • Human beings use heuristics all the time. We don’t try to work out all the consequences of our actions. Instead, we rely on general principles that we’ve found to work in the past (or that we have been brainwashed with, equally)
  • Problems with heuristics all the time: range units when shooting peons (65)
  • Common Heuristics:
    • Most constrained
      • For example, a group of characters come across an ambush. One of the ambushers is wearing phased force-field armor. Only the new, and rare, laser rifle can penetrate it. One character has this rifle. When they select who to attack, the most constrained heuristic comes into play: it is rare to be able to attack this enemy, so that is the action that should be taken
    • Do the most difficult first
      • For example, an army has two squads with empty slots. The computer schedules the creation of five Orc warriors and a huge Stone Troll. It wants to end up with balanced squads. How should it assign the units to squads? The Stone Troll is the hardest to assign, so it should be done first. If the Orcs were assigned first, they would be balanced between the two squads, leaving room for half a Troll in each squad, but nowhere for the Troll to go
    • Try the most promising first:
  • Hacks and heuristics will get you a long way, but relying on them solely means you’ll have to constantly reinvent the wheel. General bits of AI, such as movement, decision making, and tactical thinking, all benefit from tried and tested methods that can be endlessly reused (use algorithms here)
  • Just remember that for every situation where a complex algorithm is the best way to go, there are likely to be at least five where a simpler hack or heuristic will get the job done
  • One of the major reasons that new AI techniques don’t achieve widespread use is their processing time or memory requirements
  • Processor Issues in Game AI:
    • Complex AI that does work in games needs to be split into bite-size components that can be distributed over multiple frames. The chapter on resource management shows how to accomplish this. Applying these techniques to any AI algorithm can bring it into the realm of usability
    • SIMD:
      • Most modern CPUs have dedicated SIMD processing. SIMD (single instruction multiple data) is a parallel programming technique where a single program is applied to several items of data at the same time
      • Steering algorithms benefit from this feature
    • Multi-Core Processing and Hyper-Threading:
      • Modern processors have several execution paths active at the same time. Code is passed into the processor, dividing into several pipelines which execute in parallel. The results from each pipeline are then recombined into the final result of the original code. When the result of one pipeline depends on the result of another, this can involve backtracking and repeating a set of instructions. There is a set of algorithms on the processor that works out how and where to split the code and predicts the likely outcome of certain dependent operations; this is called branch prediction. This design of processor is called super-scalar
      • Normal threading is the process of allowing different bits of code to process at the same time. Since in a serial computer this is not possible, it is simulated by rapidly switching backward and forward between different parts of the code.
      • A multi-core processor effectively has multiple separate processing systems (each may be super-scalar in addition). Different threads can be assigned to different processor cores
    • Virtual Functions/ Indirection
      • Virtual functions add flexibility to the code but it’s very costly
  • Memory Concerns:
    • Cache:
      • If processors had to rely on the main RAM, they’d be constantly stalled waiting for data
      • All modern processors use at least one level of cache: a copy of the RAM held in the processor that can be very quickly manipulated. Cache is typically fetched in pages; a whole section of main memory is streamed to the processor. It can then be manipulated at will. When the processor has done its work, the cached memory is sent back to the main memory.
      • In my experience (author), dramatic speed ups can be achieved by making sure that all the data needed for one algorithm is kept in the same place
  • PC Constraints:
    • If the AI gets less time to work, how should it respond? It can try to perform less work. This is effectively the same as having more stupid AI and can affect the difficulty level of the game. It is probably not acceptable to your quality assurance (QA) team or publisher to have your game be dramatically easier on lower specification machines
  • Develop your AI code to be an AI Engine that could be reused
  • Try to develop tools that can generate you AI Code ( as in steering behaviors)
  • AI-Implant’s Maya module, for example, exposes complex Boolean conditions, and state machines, through graphical controls

 

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