DUSTIN DANNENHAUER [email protected] PRESENTATION FOR CSE 435 11/05/2012 CBR in the Oil Industry.
AI and Computer Games (informational session) Lecture by: Dustin Dannenhauer Professor Héctor...
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Transcript of AI and Computer Games (informational session) Lecture by: Dustin Dannenhauer Professor Héctor...
AI and Computer Games(informational session)
Lecture by: Dustin DannenhauerProfessor Héctor Muñoz-AvilaComputer Science and Eng.
AI and Game Design• We want to understand what kinds of in-game tasks AI can
perform
• This will help us understand what kinds of possibilities and limitations we can expect of AI when designing games
• Some games have failed because of unrealistic expectations - amount of effort required to create good AI or even its feasibility– E.g., the poor AI of the automated companion in the Daikatana game
we discussed before
• In this lecture we will focus on “what” instead of “how”– Recommended courses to learn about “how”:
• CSE 348 AI Game Programming• CSE 327 AI Theory and Practice
What is AI?
Systems that think like humans
Systems that think rationally
Systems that act like humans
Systems that act rationally
Categories for definitions of AI
Pathfinding
(1)
•Red team versus blue team
• Player controlling red team sets blue flag as destination
•Pathfinding can be used to plan the movement (1) of red units automatically
• Pathfinding is a well understood problem
Pathfinding•Frequently games use a collection of nodes (“waypoints”) and edges connecting those nodes
•Algorithms are devised that find a path through the waypoints for two given nodes
•Challenges arise when there are too many waypoints
Memory Available
• The amount of memory available has increased• Speed to process this memory has also increased
Taken Advantage of Memory Available • Pre-compute all possible paths (figure from AI Game
Programming Wisdom)
• So finding any (start,destination) path can be done quickly
• This is why pathfinding works so well in many commercial games
• If map changes (e.g., “terraforming”), an AI technique called “heuristic search” can be used. But this is used localized in a region to run efficiently
Pathfinding in Games
• Many examples: Most RTS use standard “point and click” command telling units where to go
• NPCs in RPGs can follow the player– Although not “perfect”: See video (9:40-10:10). Amazon
(avatar) is followed by the lance mercenary (NPC). Sometimes the mercenary falls behind
• NPCs in FPS games can navigate by themselves in complex environments (e.g., human vs AI death match in Unreal Tournament)– See video FFA, frag count, 16 players (only one is human)
Planning
• Planning: automated generation of a sequence of actions to achieve a goal
• Example of an action:
• Example of a plan controlling an NPC
Attack
positioned
Weapon ready
Enemy attacked
preconditions effects
Take-cover Load-weapon attack
Planning in Games
• NPCs in FEAR are controlled using planning
• (video on image) 40:00-1:40 NPCs takes cover, flank
• One could have created an FSM for this same behavior
• But for play variety purposes we will need to create many FSMs
• Planning can generate these behaviours automatically– This is what FEAR does
What-If Analysis
(1)
• What-if analysis to counter the opponent
• Red team is controlled by AI
•The AI can analyze and predict what blue will do and counter-plan accordingly
•In this example: red sends force (2) to delay blue
•This works only on small spaces (maps)
(2)
Minimax
• Idea: Build a tree describing all possible gaming moves between two opponents• Analyze tree looking ahead N moves into the “horizon” and select best move• Well understood: Nash equilibrium• Challenges:
game tree can be too large•Ways around:
Parallelism & heuristics: Deep Blue
Abstraction
What-if Analysis in Games
• Used in some Poker playing games– Because tree is so large, state abstraction is needed
(e.g., grouping hands that have similar expected strength)
• Demonstrated in RTS games for micromanagement
Mini-battle between small groups of opposing units
Many such encounters take place in a match
Proficiency in these mini-battles is crucial
Machine Learning
(1)
• Suppose red is a automated player
•Machine learning enables an automated player to learn
•Suppose blue had strong defenses along path (1)
•As a result, the approach (1) fails
•The automated player learns form this and tries path (3)
(3)
Machine Learning• Multiples ways to learn in games:
– Learn by observing other players– Learn by processing gameplay logs– Learn while playing
• Why machine learning in games? Among others:– Self-correction
• Automatically fixing exploits– Creativity
• Responding intelligently to new situations– Scalability
• Better entertainment for strong players• Better entertainment for weak players
Recall: Domination Games
• A number of fixed domination locations.
• When a team member steps into one of these locations, the status of the location changes to be under the control of his/her team.
• The team gets a point for every five seconds that each domination location remains under the control of that team.
• The game is won by the first team that gets a pre-specified amount of points.
We used Unreal Tournament© a team-based FPS
Before/After LearningBefore (video) After (video)
Bad: NPCs concentrate on one flag Better: NPCs focus on two flags
Machine Learning in Games
• Player trains a creature• The creature learns from player’s
feedback:– If player wants to reward
creature’s actions scratches creature's belly
– If player wants to punish creature’s actions slap create
• Over time creature behaves consistently with feedback received (video linked from figure)
Final remarks• AI has been successfully applied in a number of games
– We have mentioned some: Fear, Black and White
• Aside from heuristic pathfinding techniques, use of AI has been limited in commercial games
• Some reasons for this:– Game designers are worried about the AI doing unexpected (e.g.,
“silly”) things– Lack of understanding about its capabilities– Historic limitations in computing power (this is becoming less of
an issue)• But there are a lot of exciting possibilities: video