1. Evaluating and re-evaluating agent modeling: simulation and design January 11 th, 2007 Arch 484: Design Computing Seminar ? Daniel Belcher

2. Simulations and Models…Visual Models Ballard Library & Neighborhood Service Center. Bohlin Cywinski Jackson, Architects

4. UW project: http://www.urbansim.org /

5. Physical simulation ECOTECT.com

6. Old-school Mechanisms “For seeing life is but a motion of limbs, the beginning whereof is in some principal part within, why may we not say that all automata (engines that move themselves by springs and wheels as doth a watch) have an artificial life?" -Thomas Hobbes, Leviathan, 1660. “L’homme est une machine.” Man is a machine. -Julien Offray de La Mettrie, L’homme machine, 1748. “Verum et factum convertuntur.” The true and the made are convertible. -Giambattista Vico, De nostri temporis studiorum ratione, 1709.

8. MASSIVE Software “Fellowship of the Ring” battle scenes by Weta digital. Agents on film…

9. “It’s all soooooo pedestrian…” Much of agent modeling is focused on navigation, locomotion, and movement through space. Why? Humans are extremely complex…and even walking around is difficult to model. However… The dynamics of pedestrian crowds are surprisingly predictable …

10. Pedestrian activity can be modeled as a self- organizing system (Helbing et al, 2001). Time-lapse photography: standing crowd outside a movie theater showing crossing pedestrians forming a river-like flow. Agent models of pedestrian flows.

11. EVAS Pedestrian Modeling Software http://www.vr.ucl.ac.uk/research/evas/evas.html

12. “What behavior should the agent simulate?” “Does the agent exhibit this behavior?” “Do humans behave in the same way?” “How do groups of humans behave?” “Do models exhibit these group behaviors?” “Can models capture something beyond simply behavior?” “Can they capture emotion? Mood? Cognitive process?” “Just how predictable are people?” “Should we model agents at all?” “What assumptions does agent modeling make?”

13. Model-based control Information-based control Environment Agent control information Planned path Emergent path Two types of control…

14. Sense-Model-Plan-Act environment Adapted from (Russell & Norvig, “Artificial Intelligence,”1995 ) agent Model Sense Act Plan

15. The “Ecological” approach…S(P)A ??? AKA: Gibson’s direct perception: (Gibson, “The Ecological Approach to Visual Perception,” 1979) AKA: Active Perception in robotics (Brooks, “Intelligence without representation”, 1991) Subsumption architecture AKA: Situated, reactive agents

16. Behaviors as rules… a) Separation. Steer to avoid local flock- mates. b) Cohesion. Steer to move toward the average position of local flock-mates. c) Alignment. Steer toward the average heading of local flock-mates. d) Avoidance. Steer to avoid running into local obstacles or non-flock-mates. 1) Pedestrians are motivated to move as efficiently as possible to a destination. 2) Pedestrians wish to maintain a comfortable distance from other pedestrians. 3) Pedestrians wish to maintain a comfortable distance from obstacles. 4) Pedestrians may be attracted to other pedestrians or objects. (Reynolds, “Flocks, herds, and schools: A distributed behavioral model,” 1987)

17. Evaluating agent modeling…

18. Why does all this matter? Learn more about the agent-environment dynamic Validate new designs against known behavior from old designs Better understand and improve upon existing buildings Help train building operators to better manage their buildings Generate building visualizations showing life-like usage patterns Illustrate the consequences of changes to building structure Answer: Agent-based simulation allows designers to evaluate the behavior of individuals and groups inhabiting a space.

19. MouseHaus (Therakomen, 2000; 2001). + Pros: Seeks to model reflexive, reactive and motivated behaviors. Computationally efficient. - Cons: Agent steering dynamics are simplistic. Linear behavior…no learning.

20. Agent-based Virtual Users (Yan and Kalay, “Simulating Human Behavior in Built Environments,” 2005)

21. International standard of human modeling: Humanoid Animation Specification (H-Anim, 1.1) Artificial Life Behavior Modeling: primary movement control was flocking (as in Reynolds, 1987). B = f(G,R,E)

22. 3D visual simulation of plaza, with and without fountain. (Yan and Kalay, 2005). + Pros: + Interactive simulation. + Uses standard media (DXF) + “BIM”. + Conducted study of observed behavior. - Cons: - Artificial life model is extremely simplistic. - Agents explore, but do not learn. - Affordances are explicitly encoded in the environment, and not as emergent behavior.

23. Curious Agents (Saunders and Gero, “Curious Agents and Situated Design Evaluation,” 2004) Exploratory agents Ray-casting perception Curiosity model (Saunders and Gero, 2001) Learning model Exploring an “art gallery”

24. Agent Evaluations, before and after… Agents learn a random array of “art work” Uneven dispersal Crowding around entrance and exit Stuck in local-minima (NW room empty) Agent’s Post-Occupancy Evaluation… Even dispersal of interest Less crowding All rooms visited by each agent

25. Ecoconfiguration & Generative Design (Turner, Mottram & Penn, “An Ecological Approach to Generative Design,” IJDC, 2004) Simple Agent: Affordances: “walkable” and “seeable” Walk three steps, look around, repeat Generative Component: Environments are randomly seeded. Genetic Algorithm employed to optimize configurations. Spatial syntax used as fitness function… Axial arrangements selected for. (Penn & Turner, 2002)

26. (from Turner, Mottram and Penn, 2004.) Foyer?

27. Re-evaluating agent modeling…

28. Model-based control Information-based control Environment Agent control information Planned path Emergent path Two types of control…revisited

29. Behavioral Dynamics Behavioral variables (Schöner, Dose & Engles, 1995) goals expressed as (sets of) points in space spanned by behavioral variables behavior corresponds to trajectories through that space Environment Agent control information Behavioral dynamics Behavioral Dynamics (Fajen and Warren, 2001) trajectories expressed as solutions to system of differential equations attractors (intended states) and repellors (avoided states) behavior emerges as a consequence of how information is used to adjust action system

30. Behavior corresponds to trajectories through that space? Attractor State (minima) Repellor state (maxima)

31. “Trajectories expressed as solutions to system of differential equations ?” Mass-spring-damper Sinusoid oscillation

32. Dynamics of Steering  heading Fixed exocentric frame of reference Behavioral variables Heading (  ) and its rate of change (  ) Behavioral dynamics Identify factors Develop an equation of motion Predict routes (Fajen & Warren, 2001) goal gg Direction of goal  g  attractor of  goal angle = (  -  g ) obstacle oo Direction of obstacle  o  repellor of  obstacle angle = (  -  o ) Dynamics of Steering & Obstacle Avoidance.

33. The Steering Model (Fajen & Warren, 2001)  =  b  k g (  g )  k o (  o )... Damping Term: -b  limits angular acceleration. Goal Component: k g = k 1 (e -c 1 d g +c 2 ) reflects the finding that acceleration toward the goal decreases exponentially with distance Obstacle Component: k o = k 2 (e -c 3 d o )(e -c 4 |  o | ) reflects the finding that influence of an obstacle on angular acceleration decreases with both obstacle angle and distance

34. The VENLab Kaiser Proview 80 HMD stereo (60˚ x 40˚) InterSense 900 Tracker: sonic beacons (12 x 12 m) microphones inertia cube Manipulate goals & obstacles during walking Record paths: x and z position data

35. 8 random arrays, forward and backward 15 trials per condition, Number of subject =10 –8 * 15 = 120 trials per subject…10 subjects = 1200 reps Our goal: Observe and predict paths - 4 - 2024 0 2 4 6 8 X (m) Z (m) Random Obstacle Fields (Warren & Belcher, 2002)

36. individual differences  different set of parameters Random Obstacles …Findings 65% of all human paths are within 1 obstacle of model Array 1: Array 2: Human Model 0 2 4 6 8 S2 0 2 4 6 8S2 S8 S8 012 012 -2012-201 Z (m) X (m)

37. Why does all this matter again? Agent-based simulation allows designers to evaluate the behavior of individuals and groups inhabiting a space. Important to iteratively re- evaluate agent modeling on the basis of emergent models from cognitive science and robotics. Deepens our understanding of the dynamic complexity of human activity and our coupling with the Built Environment.

38. Thank You

39. Observers begin walking in a given direction After a few steps, a goal appears Vary initial goal angle and distance Instructions: “Walk to goal.” Goal Experiment (Fajen & Warren, 2001) heading goal  g = {5°, 10°, 15°, 20°, 25°} d g = {2, 4, 8 m}

40. z (m) 0123 0 2 4 6 8 x (m) 2 m 4 m 8 m 20˚ condition Goal Experiment - Findings… Goals function as attractors of  : –Acceleration toward goal increases with goal angle –Acceleration toward goal decreases with goal distance 012 0 1 2 3 4 5˚25˚ 4 m condition z (m) x (m)

41. Begin walking toward goal After a few steps, an obstacle appears Vary initial obstacle angle and distance Instructions: “Walk to goal while avoiding obstacle.” goal  o = {1°, 2°, 4°, 8°} d o = {3, 4, 5 m} obstacle 9 m Single Obstacle (Fajen & Warren, 2001)

42. z (m) x (m) -0.4-0.200.20.40.6 0.8 0 1 2 3 4 5 6 7 8 9 5 m 4 m 3 m 4° condition Single Obstacle Experiment – Findings… Obstacles function as repellors of  : Acceleration away from obstacle decreases with obstacle angle Acceleration away from obstacle decreases with obstacle distance

43. 0 2 4 6 8 -2012 -2012-2012 small angle medium angle large angle Model predictions goal obstacle #1 obstacle #2 right left middle angle = {0°,2°,4°,6°,8°,10°} Two Obstacles (Fajen & Warren, 2001)

44. 2° -2012 6° -2012 10° 0° -201 2 4° 8° 65% 19% 16% 37% 0% 63% 50% 0%50% 69% 2% 29% 48% 46% 6% 34% 64% 1% Humans switch from right  left  center route as obstacle angle increases Two Obstacles – Findings…