john s gero agents – agent simulations agent-based simulations
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John S Gero Agents – Agent Simulations
AGENT-BASED SIMULATIONS
John S Gero Agents – Agent Simulations
Simulations in Design
Visual Simulations e.g. renderings and models
Mathematical Simulations i.e. systems of equations
Physical Simulations e.g. wind tunnels
Computational Simulations e.g. finite element analysis
Agent-based Simulations e.g. crowd simulations
John S Gero Agents – Agent Simulations
Simulating Crowds
Craig Reynolds’ Flocking Algorithm A subclass of Reynolds’ Steering Behaviours
Extended flocking algorithms for games Additional behaviours for goal-oriented path-following
The Social Force Model Simulated crowd behaviour based on empirical
results
John S Gero Agents – Agent Simulations
Flocks, Herds and Schools
1. Separation. Steer to avoid flockmates.2. Cohesion. Steer to move toward the average
position.3. Alignment. Steer toward the average heading.4. Avoidance. Steer to avoid running into obstacles.
(a) Separation (b) Cohesion (c) Alignment (d) Avoidance
Steering behaviours used in Reynold’s model of flocking.
John S Gero Agents – Agent Simulations
The Social Force Model
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 (e.g. posters).
John S Gero Agents – Agent Simulations
SITUATED ANALYSIS
Pedestrians may be attracted to other pedestrians or objects.
Pedestrians try to maintain a comfortable distance from obstacles like walls.
Pedestrians try to maintain a comfortable distance from other pedestrians.
Pedestrians try to move as efficiently as possible to a destination.
Description of situated social force
1 2
3
4
pedestrian
obstacle
destination
attraction
repulsion
• Designing doors
John S Gero Agents – Agent Simulations
Narrow door
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are needed to see this picture.
John S Gero Agents – Agent Simulations
Wide door
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are needed to see this picture.
John S Gero Agents – Agent Simulations
Two doors
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are needed to see this picture.
John S Gero Agents – Agent Simulations
INTEREST IN EMERGENT BEHAVIOUR
John S Gero Agents – Agent Simulations
Agent-Centric Design Evaluations
Efficiency Inefficiency is measured with respect to the
deviation of an agent’s actual walking speed from it’s desired walking speed.
Comfort Discomfort is measured with respect to the number
of changes in direction that have to be made by an agent to navigate a space.
Non-homogenous crowd simulations e.g. simulating crowds of pedestrians with different
desired walking speeds.
John S Gero Agents – Agent Simulations
A Curious Agent
sense act
planlearn
detectnovelty
calculateinterest
long-termmemory
sample the world& generate astimulus pattern
classify stimuluspattern & updatethe prototypes inmemory
convert learningerror to measureof novelty
calculate ameasure of
interestingnessfrom novelty
update goals toreflect current
focus of interest
generate forcesto generatemovement
towards goals
The architecture of a curious agent.
John S Gero Agents – Agent Simulations
Detecting Novelty
1. How often similar patterns have been experienced.2. How similar these patterns have been.3. How recently these patterns have been experienced.
John S Gero Agents – Agent Simulations
Calculating Interestingness
reward
punish
0
1
-1
hedonic value
The Wundt CurveR
h
nnovelty
H
P
p
r
hedonic value = reward + punish
John S Gero Agents – Agent Simulations
The Curious Social Force Model
Extends the Helbing and Molnar’s Social Force Model Adds an additional rule to the Social Force Model:
Pedestrians are motivated to move towards potentially interesting areas
Models curious exploratory behaviour as a social force Uses the same simple model of locomotion as flocking and
the social force model to move agents
Incorporates learning and curiosity into the agent model Situates agents in past experience to detect novelty in new
experiences
John S Gero Agents – Agent Simulations
Situated Design Evaluations
Agent-centric evaluation of designs Evaluations of interestingness depends upon the hedonic
function which may vary from one agent to another.
Evaluations situated in experiences of agents Interestingness depends upon novelty detection which in
turn depends upon the long term memory of the agent.
Situatedness and changing evaluations The experience of a design changes how an agent will
evaluated it in the future.
MIT Class 4.208 Spring 2002
An Example Design Problem:Curating a Gallery
John S Gero Agents – Agent Simulations
Implementation
Sensing Simple vision through raycasting
Perceiving Perception of colours as hues
Learning & Novelty Detection Self-organising maps
Planning & Moving Generating and combining social forces
John S Gero Agents – Agent Simulations
Sensing
Simple vision implemented using raycasting.
John S Gero Agents – Agent Simulations
Perceiving
Simple perception of hues The artworks in the gallery
are modelled as blocks of colour, the agents are only interested in the hues of these artworks allowing the sampled environment to be represented as a vector of single values (angles on the colour wheel).
red
blue
green yellow
magenta
cyan 0°
60°120°
180°
240° 300°
A colour wheel.
John S Gero Agents – Agent Simulations
Learning & Novelty Detection
Learning 1D Self-organising map
Novelty detection Approximates detection of
novelty based on similarity of previous experiences, the past frequency of similar experience and time since the last similar experience.
red green blueyellow cyan magenta
red green blueyellow cyan magenta
p1p2
p1p2
(a) uniform sampling of colours
(b) non-uniform sampling of colours
John S Gero Agents – Agent Simulations
Planning & Moving
The Curious Social Force Model
Motivational forces are generated for all perceived objects in the direction of the object with a magnitude proportional to the object’s interestingness, the forces are then combined into a single curious social force by averaging their magnitudes and directions.
h=0.2
h=0.4
h=0.8
curious social force
John S Gero Agents – Agent Simulations
Emergent Design Problems
AQUA GOLD
OLIVE
ORANGE
YELLOW
BLUE
John S Gero Agents – Agent Simulations
Emergent Design Problems
Overcrowding to avoid uninteresting (radical) artworks The agents in the first room become overcrowded because the
artworks that are visible in the second room are too different from those in the first and generate a curious social force blocking entry to the second room.
Neglect of artworks because of improper sequencing The agents pass quickly through the last room because the
artworks in this room are too different from those in the previous room, encouraging a rapid exit.
John S Gero Agents – Agent Simulations
One Possible Solution toEmergent Design Problems
AQUAGOLD
OLIVE
ORANGE
YELLOW
BLUE
John S Gero Agents – Agent Simulations
One Possible Solution toEmergent Design Problems
Improving the progression of artworks Artworks in second and third room are swapped so that the
difference between artworks in successive rooms is minimised.
Improved flow of agents between rooms Agents are drawn into each new room as a result of the
greater interest the agents have in experiencing similar-yet-different artworks to those that they have already seen.
Increased interest, efficiency and comfort Better design improves situated and agent-centric
evaluations.
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