Artificial Inteligence for Games an Overview SBGAMES 2012
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This document provides an overview of artificial intelligence concepts for games. It discusses navigation techniques like waypoints, navmeshes and pathfinding using A* and steering behaviors. It also covers perceptions, finite state machines, behavior trees, planning using GOAP and learning approaches like neural networks and genetic algorithms. Specific examples are given for how AI was implemented in the game Left 4 Dead to deliver robust behaviors, provide competent human proxies and generate dramatic pacing through an adaptive director algorithm.
Artificial Inteligence for Games an Overview SBGAMES 2012
- 1. Inteligência artificial Overview Bruno Duarte Corrêa – poli/usp Thiago Dias Pastor – poli/usp
- 3. Purpose of ai Maximize fun Emulates the reality Not necessarily simulation Scoped by the gameplay Challenging ...
- 4. Desired Characteristics Be smart Does not looks like dumb, but purposely flawed Not intended weakness Real time performance Configurable Transparent
- 5. Basic needs of ai Navigation Perceptions Understand / Capture a snapshot of the world Acting Planning Learning / adapt
- 6. Navigation Path finding Waypoints Navmesh Astar / Dijkstra Steering behaviors Field based navigation
- 7. Waypoint • World Discretization • Manual Waypoints • Automatic Waypoints • Static Environment • Connect by Raytrace • Different types
- 8. NavMesh • Mesh processing algorithm • Pre-processing stage • Optimized waypoints placement. • Sloped based • Easier to handle • State of art
- 9. NavMesh - Recast • OpenSource Api for NavMesh Generation (c++) • Used in many commercial games • Fully integrated with Detour (Navigation System) • Recast is state of the art navigation mesh construction toolset for games. • It is automatic, which means that you can throw any level geometry at it and you will get robust mesh out • It is fast which means swift turnaround times for level designers • Extensible and easy to integrate Source http://code.google.com/p/recastnavigation/ Information http://www.critterai.org/book/export/html/2
- 10. PathFinding - Astar • Find paths in the • waypoint graph • Robot Paths • Smooth filters • Bezier • Highly Parallelizable • Can be implemented on GPU
- 11. PathFinding - Astar F=G+H where G = the movement cost to move from the starting point A to a given square on the grid, following the path generated to get there. H = the estimated movement cost to move from that given square on the grid to the final destination, point B.
- 12. PathFinding Summary of the A* Method 1.Add the starting square (or node) to the open list. 2.Repeat the following: 1. Look for the lowest F cost square on the open list. We refer to this as the current square. 2. Switch it to the closed list. 3. For each of the 8 squares adjacent to this current square … 4. If it is not walkable or if it is on the closed list, ignore it. Otherwise do the following. If it isn’t on the open list, add it to the open list. Make the current square the parent of this square. Record the F, G, and H costs of the square. 5. If it is on the open list already, check to see if this path to that square is better, using G cost as the measure. If so, change the parent of the square to the current square, and recalculate the G and F scores of the square. If you are keeping your open list sorted by F score. 3.Stop when you: 1. Add the target square to the closed list, in which case the path has been found (see note below), or 2. Fail to find the target square, and the open list is empty. In this case, there is no path. 4.Save the path. Working backwards from the target square, go from each square to its parent square until you reach the starting square. That is your path.
- 13. PathFinding
- 14. Steering Behaviors • Flow Driven • Atraction and Repulsion • No need for environment discretization • Dynamic calculation • More human like (natural) walking style • Characters can get stuck !!! • Swarm Behavior !!!
- 15. Basic Behaviors Seek and Flee Pursuit and Evasion
- 16. Basic Behaviors Collision avoidance Path Pursuit Follow the Leader Advanced Collisions Avoidance
- 17. Steering Behaviors • Combine all results like a Resultant Force • Use physic Integration to get: • Force • Aceleration • Velocity • Space • Can be integrated with common physics APIs
- 18. Basic needs of ai Navigation Perceptions Understand / Capture a snapshot of the world Acting Planning Learning / adapt
- 19. Perceptions “See the world as like a human does” Emulate human senses Response time Sensors Sight Field of view/Raycast Sounds Smells ?!?! Receive Messages
- 20. Basic needs of ai Navigation Perceptions Understand / Capture a snapshot of the world Acting Planning Learning / adapt
- 21. Finite state machine algorithm • Simple theoretical construct – Set of states (S) – Input vocabulary (I) – Transitional function T(s,i) • A way of denoting how an object can change its state over time. State: Set of properties of the environment at a time
- 22. Finite state machine • States – E: enemy in sight – S: hear a sound – D: dead • Events – E: see an enemy – S: hear a sound – D: die • Action performed – On each transition – On each update in some states (e.g. attack)
- 23. Non deterministic Finite state machine
- 24. FSM - Limitations Hard to Expand Hard to Maintain Not Flexible enough As larger it gets as f... you are.
- 25. Behavior tree Replaces fsm Encapsulates Actions / Logic / State Direct acyclic graph that is traversed Non-leaf = Condition Leafs = Actions Divide to conquer approach
- 26. Crysis Soldier • Real Time Extensible • SIMPLE • Scriptable
- 28. Example
- 29. Example
- 30. Example
- 31. Example
- 32. Example
- 33. Example
- 34. Example
- 35. Example
- 37. Basic needs of ai Navigation Perceptions Understand / Capture a snapshot of the world Acting Planning Learning / adapt
- 38. Planning Planning is a formalized process of searching for sequence of actions to satisfy a goal. Goal oriented planning (GOAP) HTN planning STRIPS
- 39. GOAP • Composed by: • Collection of states • Pre-condictions • Pos-condictions • Starting State • Goal State • Answer the What question • Self adaptable
- 40. GOAP-Example
- 41. GOAP-A*
- 42. Basic needs of ai Navigation Perceptions Understand / Capture a snapshot of the world Acting Planning Learning / adapt
- 43. Learning Supervised Learning • Learning an input-output relationship from examples • Tasks: regression, classification, ranking • Applications: skill estimation, behavioural cloning Reinforcement Learning • Learning policies from state-action-reward sequences • Tasks: control, value estimation, policy learning • Applications: learning to drive, learning to walk, learning to fight Unsupervised Learning • Learning the underlying structure from examples • Tasks: clustering, manifold learning, density estimation • Applications: modelling motion capture data, user behaviour
- 44. Neural Network • Information processing paradigm • Is inspired by the way biological nervous systems • Composed of a large number of highly interconnected processing elements • ANNs, like people, learn by example.
- 46. Neural Network
- 47. Neural Network Learning Actual algorithm for a 3-layer network (only one hidden layer): Initialize the weights in the network (often randomly) Do For each example e in the training set O = neural-net-output(network, e) ; forward pass T = teacher output for e Calculate error (T - O) at the output units Compute delta_wh for all weights from hidden layer to output layer ; backward pass Compute delta_wi for all weights from input layer to hidden layer ; backward pass continued Update the weights in the network Until all examples classified correctly or stopping criterion satisfied Return the network
- 48. Genetic Algorithm • Genetic Algorithms are a way of solving problems by mimicking the same processes mother nature uses. • Use the same combination of selection, recombination and mutation to evolve a solution to a problem • Usefull to optimize uncertain domains
- 50. Genetic Algorithm-Problems • Takes too many time to converge • Hard to define the genes • Hard to define the objective function • Hard to delivery in games
- 51. Case Study – L4D Left 4 Dead is a replayable, cooperative, survival-horror game where four Survivors cooperate to escape environments swarming with murderously enraged “Infected” (ie: zombies)
- 52. Case Study – L4D • Deliver Robust Behavior Performances • Provide Competent Human Player Proxies • Promote Replayability • Generate Dramatic Game Pacing
- 53. Deliver Robust Behavior Performances • Reactive Path Following Move towards “look ahead” point farther down path • Use local obstacle avoidance • Good • (Re)pathing is cheap • Local avoidance handles small physics props, other bots, corners, etc • Superposes well with mob flocking behavior • Resultant motion is fluid • Bad • Can avoid off path too much, requiring repath
- 54. Deliver Robust Behavior Performances • Locomotion Owns moving the actor to a new position in the environment (collision resolution, etc) • Body •Owns animation state • Vision •Owns line-of-sight, field of view, and “am I able to see <X>” queries. •Maintains recognized set of entities. • Intention •Behavior Tree based
- 55. Provide Competent Human Player Proxies • Believability/Fairness • Imperfect knowledge (senses simulation) • Reaction times • Trust • Survivor Bots Helps Human • Undesirable events CANNOT occurs (Cheating) • Bots that are far from the battle are magicaly teleported • Survivor Bots cannot deal friendly fire • …. • Human Behavior
- 56. Promote Replayability • Complex Procedural Population • Game session is viewed as a skill challenge instead of a • memorization exercise • Structured Unpredictability
- 57. Structured unpredictability NavMesh Active Area Set Potential Visible Area Flow Distance
- 58. Generate Dramatic Game Pacing Adaptive Dramatic Pacing algorithm • Creates peaks and valleys of intensity similar to the proven pacing success of Counter-Strike • Algorithm: • Estimate the “emotional intensity” of each Survivor • Track the max intensity of all 4 Survivors • If intensity is too high, remove major threats for awhile • Otherwise, create an interesting population of threats
- 59. AI DIRECTOR Use Survivor Intensity to modulate the Infected population • Build Up • Create full threat population until Survivor Intensity crosses peak threshold • Sustain Peak • Continue full threat population for 3-5 seconds after Survivor Intensity has peaked. Ensures minimum “build up” duration. • Peak Fade • Switch to minimal threat population (“Relax period”) and monitor Survivor Intensity until it decays out of peak range. This state is needed so current combat engagement can play out without using up entire Relax period. Peak Fade won’t allow the Relax period to start until a natural break in the action occurs. • Relax • Maintain minimal threat population for 30-45 seconds, or until Survivors have traveled far enough toward the next safe room, then resume Build Up.
- 60. Implementation considerations Multithreading Functional Modular Data driven Cache friendly Game interface Level of Detail
- 61. Trends Emotion !!! Realistic Crowd simulation Ai in contact with the player itself Improve ai testing Character control and animation helper Online ais Interactive cut scenes
- 62. Artificial intelligence ‘Far too often, AI has been a last-minute rush job, implemented in the final two or three months of development by overcaffeinated programmers with dark circles under their eyes and thousands of other high-priority tasks to complete” - Paul Tozour, Ion Storm