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
Top Related