Problems with the Imperative 1 Method

Transcription

1 Announcements Video Game AI: Lecture 13 Behavior Trees & Blackboards Nathan Sturtevant COMP 3705 HW #4 online Who is using the makefiles? Project online after class 4 lectures left after today Schedule on course web page Behavior Trees Mentioned by Jeff Orkin Can also be seen as a Hierarchical FSM Or a special type of scripting language But Not particularly standardized Many different implementation details Overview of the general ideas Problems with the Imperative 1 Method Generator 3 Generator Generator 2 Explicit transitions! n 2 complexity

2 Motivation: defending 3 generators How would you design the decision of which generator to defend? Behavior Tree [Spore] Based on GDC talk by Lauren McHugh [now on DarkSpore] and notes from Chris Hecker Arrays of deciders Processed in order A decider chooses whether the underlying behaviors should be activated Very similar to decision tree, but more linear Behaviors at the bottom of the tree Group Deciders

3 How things work In general: Start at the root Ask a decider if it wants to run Behavior Deciders If it is a group decider, recurse on children Otherwise allow decider to choose relevant behavior

4 Now what? Behavior runs until next tick of AI Then decide if you want to keep running things: Evaluate if any higher priority behaviors should run Now what? Some details to get right Behavior runs until next tick of AI Then decide if you want to keep running things: Evaluate if any higher priority behaviors should run If not, continue current behavior If current behavior wants to terminate Continue evaluating behaviors in the rest of the tree A group decider should be able to guarantee that a child decider will want to run Might or might not want to re-check conditions when returning to current behavior Caching expensive pre-condition checks eg finding a path If higher-priority behavior wants to run Terminate current behavior and run high-level

5 Design a BT Simple BT for a FPS? Performance results Do not need explicit transitions like in FSM Implicit transitions between all behaviors Decisions organized differently than a DT What can t a BT do? Behavior Trees [book] Sequence and Selectors Composite Tasks Sequence? Run all children in order Fail if any child fails Selector Attack Taunt Stare Run children until one succeeds Conditions Tests / deciders Actions Enemy Visible? Turn away Run away Figure 5.22 and 5.23

6 Simple AI Decorator Modifier on behavior Run a certain number of times Don t run for 10s after previous event Repeat indefinitely until failure Figure 5.27 Parallel Tasks Simple AI Run tasks in parallel; eg: Animate sound Could also control a group of units Can also continually check conditions er in position? Different paradigm than Spore-style BT s Figure 5.32 & 5.33 Use computers

7 Simple AI Communication between agents Often need to know what other agents are doing Difficult to coordinate this information by passing messages or by triggering events Instead, use a shared architecture Until fail Use computers er in position? Figure 5.32 & 5.33 Blackboard Generic term for a shared data structure Agents can read/write from/to data structure Usually store: id [eg string] type [what data is stored] data [actual data] Example usage: Keep track of how many agents are guarding a generator Blackboard usage Blackboards can be used with all of our decision making techniques Can be used in decider Could be an action taken in a BT Can add implicit cooperation

8 Blackboard usage If running in parallel, usually will need a semaphore to guard multiple write/read coherency Book discusses semaphore usage for BTs in some detail Can be easily implemented with a hash table