Agent-based Systems

1

• Agent-based Systems
• in geosimulation
• Geog 220, Winter 2005
• Arika Ligmann-Zielinska
• February 14, 2005

2
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

3
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

4
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 experience
• Agent - 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?
5
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

6
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!

7
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?

8
Summary of MAS attributes
9
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

10
Types of Agents

• Geosimulation mobile, adaptive ?
• Weak vs. strong agency
• Geosimulation deals with weak agents

11
Urban Agents
10th of seconds

• Characteristic time t

months
years
seconds
months
12
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 Functions
• Set of opportunities Ciavailable 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

13
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) gt 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

14
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

15
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/

16
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/m
akse/urban.html
17
General Models of Agents Collectives

• Intermittency
• Bifurcation of a cell
• Each time a fraction a of population leaves a
  cell C
• a 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

18
General Models of Agents Collectives

• Spatiodemographic processes
• Particles are born and die
• Parameters of reproduction ß and mortality ?
• ?T
• T - threshold
• Partially clustered
• Diffusion of Innovation
• probability of acceptance 1 ?
• ?T (T threshold) defined as intensity of
  innovation dissemination ß

19
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
• Schellings segregation
• Source Nicholas Gessler UCLA
• http//www.sscnet.ucla.edu/geog/gessler/borland/