John S Gero Agents – Agent Simulations AGENT-BASED SIMULATIONS

John S Gero Agents – Agent Simulations

AGENT-BASED 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


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


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.


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).


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


Narrow door

QuickTime™ and a decompressor

are needed to see this picture.


Wide door




Two doors




INTEREST IN EMERGENT BEHAVIOUR


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.


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.


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.


Calculating Interestingness

reward

punish

0

1

-1

hedonic value

The Wundt CurveR

h

nnovelty

H

P

p

r

hedonic value = reward + punish


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


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


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


Sensing

Simple vision implemented using raycasting.


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.


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


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


Emergent Design Problems

AQUA GOLD

OLIVE

ORANGE

YELLOW

BLUE


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.


One Possible Solution toEmergent Design Problems

AQUAGOLD

OLIVE

ORANGE

YELLOW

BLUE


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.

Documents

John S Gero Agents – Agent Simulations AGENT-BASED SIMULATIONS