Agent-based Systems

in geosimulation

Geog 220, Winter 2005

Arika Ligmann-Zielinska

February 14, 2005

Sources

1) Weiss G. ed. (1999) Multiagent Systems: a modern approach to distributed artificial intelligence, Cambridge, MA, MIT Press

• Prologue pp. 1 – 9• Chapter 1 Intelligent Agents by Michael Wooldridge pp. 27 – 42• Chapter 2 Multiagent Systems and Societies of Agents by Michael N.

Huhns and Larry M. Stephens pp. 79 – 84

2) Batty M., Jiang B. (1999) Multi-agent Simulation: new approaches to exploring space-time dynamics within GIS, CASA paper 10

• pp. 1 – 7

3) Benenson I., Torrens P. (2004) Geosimulation Automata-based Modeling of Urban Phenomena, John Wiley & Sons, LTD

• Chapter 6 Modeling Urban Dynamics with Multiagent Systems pp.154 – 184

Outline

• Agency

• Distributed Artificial Intelligence & Multi Agent Systems

• Agents environments

• Agents in geosimulation

• General typology of agents & urban agents

• Location choice behavior

• General Models of Urban Agents

• Examples

Agents Demystified

agere (Latin) – to do

Agent - a computational entity such as a software program or robot that can be viewed as perceiving and acting upon its environment and that is autonomous in that its behavior at least

partially depends on its own experienceAgent - system that decides for itself what it needs to do in order to satisfy its objectives

Characteristics• Autonomous• Goal-oriented• Interacting – agents “sense” or are “aware” of other agents

Key behavioral processes• Problem solving• Planning• Decision-making• Learning

When and how to interact with whom?

Agents Demystified

Intelligent agents - agents operating robustly in rapidly changing, unpredictable, or open environments

“Sense the future”

• Flexible autonomous action in order to meet design objectives (flexibility – reactivity)

• Pro-activeness (goal directed behavior, taking the initiative)

• Social ability (interact with other agents/humans)

 

 Effective integrating goal-oriented and reactive behavior

Multiagent Systems (MAS)

MAS – a community of agents, situated in an environment.MAS – systems in which several interacting, intelligent agents pursue some set

of goals or perform some set of tasks.

– Inherent distribution (spatial, temporal, semantic, functional)– Inherent complexity

• MAS studied by Distributed Artificial Intelligence – DAI• DAI and AI

– AI – intelligent BUT stand-alone systems• Intelligence acts in isolation• Cognitive processes of individuals• Psychology and behaviorism

– DAI – intelligent connected systems• Intelligence acts through interaction• Social processes in groups of individuals• Sociology and economics

Hence DAI is a generalization of AI, and not its specialization!

Agents’ environment

• Accessible vs. inaccessible

• Deterministic vs. non-deterministic

• Episodic vs. non-episodic

• Static vs. dynamic

• Discrete vs. continuous What typology can be assigned to urban/spatial models?

If an environment is sufficiently complex, the fact that it is actually deterministic is not much help – Why?

Summary of MAS attributes

Why Agents in Spatial Models?

• Urban systems are a product of human decisions

• CA cousins lack

– Mobility

– Purposefulness

– Social ability

– Adaptability

– Transition Rules heterogeneity

Refer to Figure 5.4 p. 169 in BenTor

Types of Agents

• Geosimulation: mobile, adaptive &…?• Weak vs. strong agency

Geosimulation deals with weak agents

Urban Agents

Characteristic time ”t”

years

10th of seconds

seconds

months

months

Urban Agent Choice Behavior

• Location and migration behavior

• Changes in state and location

• Mobile agents carry their characteristics with them

• Ability to make decision concerning the entire urban space (action-at-a-distance)

• Location choice modeled with rational decision-making and bounded rationality

• Utility FunctionsSet of opportunities {Ci}available for agent A, where each Ci has some level

of Utility U(A, Ci) and/or Disutility D(A, Ci) = 1 – U(A, Ci)

(assumed that U belongs to [0,1])

Variability in the perception of utility – choice probabilities P(A, Ci), where

P(A, Ci) = f(U(A, Ci)) e.g. logit model

Bounded Rationality Heuristics

• Random choice: pick one of the opportunities Ci randomly

• Satisfier choice: pick one of the opportunities Ci randomly and compare it to a pre-defined threshold ThA of an Agent A

if U(A, Ci) > ThA

pick Ci

• Ordered choice: order Ci for A in descending order, creating an ordered set of opportunities, pick the first opportunity from this set

Residential Decision Making

Experimental results based on:• Revealed preferences of subjects• Stated preferences of subjects

Taxonomy of residential decision-factors (adapted from Speare, 1974):

• Individual• Household• Housing• Neighborhood• Above-neighborhood

Stress(dissatisfaction/dissonance)-resistance Residential Behavior (steps):

• Decision to leave the current location• Decision to reside in a new location

General Models of Agents’ Collectives

Diffusion-Limited Aggregation (DLA)• Urban context – simulating new building locations: DLA

of Developers Efforts• Monocentricity (CBD core)• Sprawl diffusion

• Urban land use density represented by power law:Density(d) ~ d D-2

d – distance from the city center

D – fractal dimension

Nicholas Gessler UCLA http://www.sscnet.ucla.edu/geog/gessler/borland/DLA

General Models of Agents’ Collectives

Percolation

• Percolation of the Developers’ Efforts

• Developers build close to existing constructions

• Clustered

• Multicenteric

• Density of urban uses decreases according to exponential law

Density(d) = d0e-Ld

d – distance from the city center

L – constant

Real

Simulation

Image source: http://lisgi1.engr.ccny.cuny.edu/~makse/urban.html

General Models of Agents’ Collectives

Intermittency

Bifurcation of a cell• Each time a fraction α of population leaves a cell C• α distributes among von Neumann neighborhood of C –

close migration• C becomes an attractor or repelling center – distant

migration• Exponential decrease in density of urbanized land from

the city center

General Models of Agents’ Collectives

Spatiodemographic processes

• Particles are born and die

• Parameters of reproduction β and mortality γ– γT

T - threshold

– Partially clustered

Diffusion of Innovation

• probability of acceptance 1 – γ

• γT (T – threshold) defined as intensity of innovation dissemination β

ABM in Urban Context - Examples

• XJ Technologies demos

http://www.xjtek.com/models/agent_based_models/

• CommunityViz Policy Simulator Analysis

by Arika Ligmann-Zielinska

http://www.uweb.ucsb.edu/~arika/agents/chelan/anim/basic.html

• Schelling’s segregation

Source: Nicholas Gessler UCLA

http://www.sscnet.ucla.edu/geog/gessler/borland/Segregation