Presentation on theme: "Oct 30, Fall 2006IAT 4101 AI in games Simple steering, Flocking Production rules FSMs, Probabilistic FSMs Path Planning, Search Unit Movement Formations."— Presentation transcript:
Oct 30, Fall 2006IAT 4101 AI in games Simple steering, Flocking Production rules FSMs, Probabilistic FSMs Path Planning, Search Unit Movement Formations
Oct 30, Fall 2006IAT 4102 AI in video games 5-10% of CPU for Realtime 25-50% of CPU for Turn-based –Chase/Escape behaviors –Group behaviors –Finite State machines –Adaptation/Learning
Oct 30, Fall 2006IAT 4103 Questions How good should the AI be? Why are people more fun than NPC’s? Will networked games reduce AI? New directions for AI in games?
Oct 30, Fall 2006IAT 4104 Learning/Adaptation Increment aggressiveness if player is doing well –The Computer-Based SATs do this! Levels are a pre-programmed version of adaptation Tuning Stability How might adaptation make play Better or Worse?
Oct 30, Fall 2006IAT 4105 Adaptation vs. Play quality Do you want the monsters in Quake to get better as you get better? Force the user to live with the consequences of his/her actions Can surprise the designer (Creatures) Pit AI creatures against each other to find bugs or tune actions Robotwar
Oct 30, Fall 2006IAT 4106 What is good AI? Perceived by user as challenging –Cruel, but fair! User is surprised by the game –but later understands why Feeling that reality will provide answers –able to make progress solving problem What games have used AI effectively?
Oct 30, Fall 2006IAT 4107 Chase/Evade Algorithm for the predator?
Oct 30, Fall 2006IAT 4108 Enhancements to Chase Speed Control –Velocity, Acceleration max/min –Limited turning Radius Randomness –Moves –Patterns
Oct 30, Fall 2006IAT 4109 Enhancements to Chase Anticipation Build a model of user behavior
Oct 30, Fall 2006IAT Steering Behaviors Pursue Evade Wander Obstacle Avoidance Wall/Path following Queuing Combine behaviors with weights What could go wrong?
Oct 30, Fall 2006IAT Group Behaviors Lots of background characters to create a feeling of motion Make area appear interesting, rich, populated
Oct 30, Fall 2006IAT Flocking -- (HalfLife, Unreal) What might go wrong? Simple version: Compute trajectory to head towards centroid
Oct 30, Fall 2006IAT Group Behaviors Reaction to neighbors -- Spring Forces Craig Reynolds SIGGRAPH 1987
Oct 30, Fall 2006IAT What could go wrong? Repulsive springs around obstacles Does not handle changes in strategy Exactly aligned Forces balance out in dead end
Oct 30, Fall 2006IAT “Perceptual” Models
Oct 30, Fall 2006IAT Production Rules If( enemy in sight ) fire If( big enemy in sight ) run away If( --- ) ---- Selecting among multiple valid rules –Priority weighting for rules or sensor events –Random selection No state (in pure form)
Oct 30, Fall 2006IAT Finite State Machines States: Action to take –Chase –Random Walk –Patrol –Eat Transitions: –Time –Events –Completion of action Chopping Take Wood to closest depot Enough wood Drop wood: Go back to woods At depot At woods
Oct 30, Fall 2006IAT State Machine Problems Predictable –Sometimes a good thing –If not, use fuzzy or probabilistic state machines Simplistic –Use hierarchies of FSM’s (HalfLife)
Oct 30, Fall 2006IAT Probabilistic State Machines Personalities –Change probability that character will perform a given action under certain conditions
Oct 30, Fall 2006IAT Probabilistic Example Fire At Enemy Run out of Range Enemy Within Hand- to-Hand Range 50% Far Enough to Take Shot Run Away Enemy Within Hand- to-Hand Range 50%
Oct 30, Fall 2006IAT Probabilistic State Machines Other aspects: –Sight –Memory –Curiosity –Fear –Anger –Sadness –Sociability Modify probabilities on the fly?
Oct 30, Fall 2006IAT Planning Part of intelligence is the ability to plan Move to a goal –A Goal State Represent the world as a set of States –Each configuration is a separate state Change state by applying Operators –An Operator changes configuration from one state to another state
Oct 30, Fall 2006IAT Path Planning States: –Location of Agent/NPC in space –Discretized space Tiles in a tile-based game Floor locations in 3D Voxels Operator –Move from one discrete location to next
Oct 30, Fall 2006IAT Path Planning Algorithms Must Search the state space to move NPC to goal state Computational Issues: –Completeness Will it find an answer if one exists? –Time complexity –Space complexity –Optimality Will it find the best solution
Oct 30, Fall 2006IAT Search Strategies Blind search –No domain knowledge. –Only goal state is known Heuristic search –Domain knowledge represented by heuristic rules –Heuristics drive low-level decisions
Oct 30, Fall 2006IAT Breadth-First Search Expand Root node –Expand all Root node’s children Expand all Root node’s grandchildren Problem: Memory size Root Child1 Child2 Root Child1 Child2 GChild1 GChild2 GChild3 GChild4
Oct 30, Fall 2006IAT Uniform Cost Search Modify Breadth-First by expanding cheapest nodes first Minimize g(n) cost of path so far Root Child1 Child2 GChild1 9 GChild2 5 GChild3 3 GChild4 8
Oct 30, Fall 2006IAT Depth First Search Always expand the node that is deepest in the tree Root Child1 GChild1 GChild2 Root Child1 Root Child1 GChild1
Oct 30, Fall 2006IAT Depth First Variants Depth first with cutoff C –Don’t expand a node if the path to root > C Iterative Deepening –Start the cutoff C=1 –Increment the cutoff after completing all depth first probes for C
Oct 30, Fall 2006IAT Bidirectional Search Start 2 Trees –Start one at start point –Start one at goal point
Oct 30, Fall 2006IAT Avoid Repeating States Mark states you have seen before In path planning: –Mark minimum distance to this node
Oct 30, Fall 2006IAT Heuristic Search Apply approximate knowledge –Distance measurements to goal –Cost estimates to goal Use the estimate to steer exploration
Oct 30, Fall 2006IAT Greedy Search Expand the node that yields the minimum cost –Expand the node that is closest to target –Depth first –Minimize the function h(n) the heuristic cost function Not Complete! Local Minima
Oct 30, Fall 2006IAT A* Search Minimize sum of costs g(n) + h(n) –Cost so far + heuristic to goal Guaranteed to work –If h(n) does not overestimate cost Examples –Euclidean distance
Oct 30, Fall 2006IAT A* Search Fails when there is no solution –Avoid searching the whole space –Do bi-directional search –Iterative Deepening
Oct 30, Fall 2006IAT Coordinated Movement Somewhat more difficult than moving just one NPC –Disappearing goal –New obstacles in path –Collisions with other NPCs –Groups of units –Units in formation
Oct 30, Fall 2006IAT Coordinated Elements Collision detection –Detection of immediate collisions –Near future Perform the usual collision detection optimizations –Spatial hierarchies –Simplified tests –Unit approximations
Oct 30, Fall 2006IAT Collision Detection Levels of collision –Hard radius (small) Must not have 2 units overlap hard radius –Soft radius (large) Soft overlap not preferred, but acceptable
Oct 30, Fall 2006IAT Collision Detection With movement, need to avoid problems with bad temporal samples –Sample frequently –Detect collisions with extruded units –Use a movement line –Detect distance from Line segment
Oct 30, Fall 2006IAT Unit Line Unit line follows path Can implement minimum turn radius Gives mechanism for position prediction Connected line segments –Time stamps per segment –Orientation per segment –Acceleration per segment
Oct 30, Fall 2006IAT Prediction line Given prediction, use next prediction as move Prediction must have dealt with collisions already
Oct 30, Fall 2006IAT Collision Avoidance Planning Don’t search a new path at each collision Adopt a Priority Structure –Higher priority items move –Lower priority items wait or get out of the way Case-based reasoning to perform local path reordering Pairwise comparison
Oct 30, Fall 2006IAT Collision Resolution Summary Favor: –High priority NPCs over Low Priority –Moving over non-moving Lower Priority NPCs –Back out of the way –Stop to allow others to pass General –Resolve all high-priority collisions first
Oct 30, Fall 2006IAT Avoidance Case 1: Both units standing –Lower priority unit does nothing itself –Higher unit Finds which unit will move Tells that unit to resolve hard collision by shortest move
Oct 30, Fall 2006IAT Avoidance Case 2: I’m not moving, other unit is moving –Non-moving unit stays immobile
Oct 30, Fall 2006IAT Avoidance Case 3: I’m moving, other is not: –If lower priority immobile unit can get out of the way: Lower unit gets out of way Higher unit moves past lower to get to collision free point –Else If we can avoid other unit Avoid it!
Oct 30, Fall 2006IAT Avoidance Case 3: I’m moving, other is not: –Else: Can higher unit push lower along Push! –Else: Recompute paths!
Oct 30, Fall 2006IAT Avoidance Case 4: Both units moving –Lower unit does nothing –If hard collision inevitable and we are high unit Tell lower unit to pause –Else: If we are high unit Slow lower unit down and compute collision-free path
Oct 30, Fall 2006IAT Storage Store predictions in a circular buffer If necessary, interpolate between movement steps
Oct 30, Fall 2006IAT Planning Prediction implies –A plan for future moves Once a collision has been resolved –Record the decision that was made –Base future movement plans on this Blocking unit Get-To Point Predicted Position
Oct 30, Fall 2006IAT Units, Groups, Formations Unit –An individual moving NPC Group –A collection of units Formation –A group with position assignments per group member
Oct 30, Fall 2006IAT Groups Groups stay together –All units move at same speed –All units follow the same general path –Units arrive at the same time Obstruction Goal
Oct 30, Fall 2006IAT Groups Need a hierarchical movement system Group structure –Manages its own priorities –Resolves its own collisions –Elects a commander that traces paths, etc Commander can be an explicit game feature
Oct 30, Fall 2006IAT Formations Groups with unit layouts –Layouts designed in advance Additional States –Forming –Formed –Broken Only formed formations can move
Oct 30, Fall 2006IAT Formations Schedule arrival into position –Start at the middle and work outwards –Move one unit at a time into position –Pick the next unit with Least collisions Least distance –Formed units have highest priority Forming units medium priority Unformed units lowest
Oct 30, Fall 2006IAT Formations Not so good… Better…
Oct 30, Fall 2006IAT Formations: Wheeling Only necessary for non-symmetric formations Break formation here Stop motion temporarily Set re-formation point here
Oct 30, Fall 2006IAT Formations: Obstacles Scale formation layout to fit through gaps Subdivide formation around small obstacles
Oct 30, Fall 2006IAT Formations Adopt a hierarchy of paths to simplify path-planning problems High-level path considers only large obstacles –Perhaps at lower resolution –Solves problem of gross formation movement –Paths around major terrain features
Oct 30, Fall 2006IAT Formations Low-level path –Detailed planning within each segment of high-level path –Details of obstacle avoidance Implement path hierarchy with path stack High-Level Path Low-Level Path1 High-Level Path Low-Level Path2 High-Level Path Low-Level Path2 Avoidance Path
Oct 30, Fall 2006IAT Compound Collisions Solve collisions pairwise Start with highest priority pair –Then, resolve the next “highest priority pair” now colliding
Oct 30, Fall 2006IAT General Optimize for 2D if possible Use high-level and low-level pathing Units will overlap! Understand the update loop –It affects unit movement Maintain a brief collision history