artificial intelligence in game design n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Artificial Intelligence in Game Design PowerPoint Presentation
Download Presentation
Artificial Intelligence in Game Design

Loading in 2 Seconds...

  share
play fullscreen
1 / 17
amory

Artificial Intelligence in Game Design - PowerPoint PPT Presentation

100 Views
Download Presentation
Artificial Intelligence in Game Design
An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. Artificial Intelligence in Game Design Probabilistic Finite State Machines and Markov State Machines

  2. Randomness Inside State • Randomness in actions taken by NPC • Randomness inside update method • Can depend on current state

  3. Randomness Inside State • Randomness in Initial Setup • Randomness in enter method • Example: choice of weapon in Fight state 60% 35% 5%

  4. Randomness in Transitions • Same current state + same stimuli = one of several possible next states • Possibly including current state • Performing different tasks at random Guard Door andShout for Help Player visible 60% Patrol in front of Door 40% Player visible Chase Player

  5. Random Behavior Timeouts • Continue strong emotional behavior for random number of steps Predator seen Predator seen Wander Flee Predator not seen 10% Predator not seen 90%

  6. Unpredictability of World • Small chance of “unexpected” occurrence • Adds “newness” to game even after multiple plays • Adds to “realism” of world Target in sights98% Reload Aim Fire Normal case Finished reloading Target in sights2% Gun Jam Gun cleared Unexpected case

  7. Randomness in Emotional States • Emotional transitions less predictable • Effect of “delayed reaction” Small hit by player75% Player HP < 1040% Player HP < 1060% Confident Angry Small hit by player25% Heavy hit by player70% My HP < 1050% Heavy hit by player30% My HP < 1050% Heavy hit by me30% Frightened Heavy hit by me70%

  8. Probabilities and Personality • NPCs with probabilities can give illusion of personalities • Differences must be large enough for player to notice in behavior Small hit by player10% Player HP < 1080% Player HP < 1020% Confident Angry Small hit by player90% Heavy hit by player70% My HP < 1080% Heavy hit by player30% Orc with anger management issues Heavy hit by me30% My HP < 1020% Frightened Heavy hit by me70%

  9. Dynamic Probabilities • Likelihood of transition depends on something else • More realistic (but not completely predictable) • Can give player clues about state of NPC % of bullets left Firing Reload Player not firing 1- % of bullets left Guard Door andShout for Help 1- Energy % Player visible Patrol in front of Door Energy % Chase Player

  10. Emergent Group Behavior • Each NPC in group can choose random behavior • Can appear to “cooperate” • Half of group fires immediately giving “cover” to rest • If player shoots firing players, rest will have time to reach cover Fire 50 % Player visible Patrol Cover reached 50 % Take Cover

  11. Emergent Group Behavior • Potential problem:Possibility all in group can choose same action • All either shoot or take cover • No longer looks intelligent • Can base probabilities on actions others take Fire 1 - % of other players firing Player visible Patrol Cover reached Take Cover % of other players firing

  12. “Markov” State Machines • Tool for decision making about states • Give states a “measure” describing how good state is • Move to state with best measure • Key: Measure changes as result of events • Possibly returns to original values if no events occur • Based (sort of) on Markov probabilistic process(but not really probabilities)

  13. “Markov” State Machines • Example: Guard choosing cover • Different cover has different “safety” measures • Firing from cover makes it less safe(player will start shooting at that cover) • Represent safety as vector of values trees 1.0 1.5 wall 0.5 brush

  14. “Markov” State Machines • Assign transition “matrix” to each action • Defines how each state affected by action • Multiplier < 1 = worse • Multiplier > 1 = better • Example: fire from trees • Trees less safe • Other positions marginally safer(player not concentrating on them) 0.1 1.2 1.2

  15. “Markov” State Machines • “Multiply” current vector by matrix to get new values • Note: real matrix multiplication requires 2D transition matrix 1.0 0.1 0.1 = 1.5 1.2 1.8 0.5 1.2 0.6 0.1 0 0 0 1.2 0 0 0 1.2

  16. “Markov” State Machines • Further events modify values • Example: Now fire from behind wall 0.12 0.1 1.2 = 0.9 1.8 0.2 0.72 0.6 1.2

  17. “Markov” State Machines • Note “total safety” (as sum of values) decreasing 3  2.5  1.74… • May be plausible (all cover becoming less safe) • Can normalize if necessary • Can gradually increasevalues over time • Usually result of time/turns without event • Example: player leaves area 0.12 1.1 0.132 = 0.9 1.1 0.99 0.72 1.1 0.792