1. An Introduction to Knowledge Representation Damián Isla, Naimad Games Peter Gorniak, Rockstar

2. Knowledge Representation We spend a lot of time on what our AIs do but very little time on what they know One of the great neglected problems of [game] AI This talk: Introduce techniques Agitate

3. Behavioral Knowledge? Behavioral knowledge When to run away, when to shoot, when to flank left, etc. Does an ant know where the anthill is? State Knowledge What is true about the world

4. The Point of KR Perception of a thing != the thing itself

5. The Point of KR Object 1 Object 2 Object 3 Behavior Pathfinding Animation Agent

6. The Point of KR Object 1 Object 2 Object 3 Behavior Pathfinding Animation KR Decisions about action perception and interpretation Agent

7. Why is KR Interesting? Fun exploit mistakes / limited perception new modes of interaction Lifelike reason about AI as thinking perceiving creatures emotional reactions Were doing it already anyway Search for better representations == Search for more expressive power build behavior out of better primitives

8. Timescales This instant dogs are animals birds have wings pushing the button calls the elevator Guy X is behind the crate I have three bullets left That car is coming towards me Bobby is 5 years old Jane is spending the semester in France.

9. 3 Key Concepts Confidence How sure am I in the knowledge I have? Salience How important is the sensory data Im getting? Prediction What do believe will happen given what Ive seen and what I know?

11. Behavior update void s_agent::behavior_update() { if (!confused()) { s_pos2d pos; omap.get_target_position(&pos); move_to(pos); }

12. Behavior update void s_agent::behavior_update() { if (!confused()) { s_pos2d pos; omap.get_target_position(&pos); move_to(pos); } +

13. Expectation-related Emotions Confusion Surprise

14. Expectation-related Emotions Confusion Surprise

16. Target Lists Object 1 Object 2 Object 3 Behavior Pathfinding Animation KR Agent

17. Target Lists Object 1 Object 2 Object 3 Behavior Pathfinding Animation Target 2 Target 1 Target 3 Agent

18. Target Lists Target Perceived data location(x,y,z) actionshoot hitpoints44 Derived data Threat0.8 Target weight0.9 Intentionshurt_me Allows AI to make mistakes Shared computation + expressive power 0.99 0.8 0.95 0.98 0.6 0.9 0.98

19. Example search_for_target switch_to_knife shoot_at_target switch_to_knife search_for_target !

20. Phil Carlisle Memorial Memory Slide

21. PCMMS Working memory Short-term Episodic ??? Volatile behavior state Target Perceived data location(x,y,z) Target Perceived data location Remember that

22. Challenge #1: Representational Versatility Solution: Polymorphism

23. Polymorphism Percept DAG (Synthetic Characters, MIT Media Lab, circa 2002)

24. Challenge #2: Performance

25. Object 1 Object 2 Object 3 Agent KR Agent Shared KR

26. Challenge #2: Performance Object 1 Object 2 Object 3 Agent KR Agent KR Hybrid KR X:weapon: pistol X:location: crates enemies

27. Challenge #2: Performance Enemy 1 Enemy 2 Grass Agent Salience Threshold OA O x A

28. Benefits of Target Lists Reasonable mistakes / limited perception Shared computation Expressive power

29. Limitations of Target Lists Relational information Where does the notion of behind live? Wholes and parts Does a cars wheel deserve its own representation? A guys arm? What about a mob of guys?

30. Hood Wheel behind Wheel Car has-a Head Arm next-to has-a next-to Enemy Gun Enemy holding Representational Wankery

31. Wild Speculation Lazy Representation??? 1. Perception is active 2. Behavioral / emotional / motivational state changes the way you see the world 3. And WHAT you see in the world.