A NEW URBAN

Meili Vanegas-Hernandez

Célia da Costa Pereira

Diego Moreno

Giovanni Fusco

Andrea G. B. Tettamanzi

Michel Riveill

José Tiberio Hernández

Universidad de los Andes, Colombia

Université Côte d'Azur, CNRS, ESPACE, France

Universidad de los Andes, Colombia

Université Côte d'Azur, CNRS, ESPACE, France

Université Côte d'Azur, CNRS, I3S, France

Université Côte d'Azur, CNRS, I3S, France

Université Côte d'Azur, CNRS, I3S, France

SEGREGATION-GROWTH

COUPLED MODEL USING A

BELIEF-DESIRE-INTENTION

POSSIBILISTIC FRAMEWORK

MOTIVATION

URBAN DECISION MAKING

GEOSIMULATION TOOLS

UNDERSTAND THE EVOLUTION OF URBAN FORMS

INDENTIFY SEGREGATION PATTERNS

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1.

MOTIVATION

USE CASE OF THE BELIEF-DESIRE-INTENTION POSSIBILISTIC FRAMEWORK

2 CASE STUDIES

THEORETICAL

BOGOTA, COLOMBIA

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SCHELLING'S SEGREGATION MODEL

RELATED WORK

AGENT-BASED MODELS (ABM's)

MULTIPLE ELEMENTS INTERACTING BETWEEN THEM GENERATING GLOBAL PATTERNS

EXPLAINS HOW INDIVIDUAL INCENTIVES AND INDIVIDUAL PERCEPTIONS LEAD TO COLLECTIVE SEGREGATION

CONSIDERS ONLY SOCIAL MOTIVATIONS

NO COGNITIVE ASPECTS

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2.

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OUR APPROACH

URBAN SEGREGATION-GROWTH MODEL USING POSSIBILISTIC FRAMEWORK

PROXIMITY TO

TRANSPORT NETWORK

CITY FACILITIES

INTRODUCING

PROMOTERS

INVESTORS

USING

BDI POSSIBILISTIC FRAMEWORK

+

2.

3.

4.

6.

3. http://www.washingtonpost.com/wp-srv/special/entertainment/metro-love/img/map.png
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5.

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OUR APPROACH

WHY A POSSIBILISTIC FRAMEWORK?

AN AGENT MAY HAVE A PARTIAL VISION BASED ON QUALITATIVE ORDER OF UNCERTAINTY ABOUT ITS ENVIRONMENT.

THE AGENT’S OPINIONS CAN CHANGE IN THE LIGHT OF NEW (PARTIALLY TRUTH) INFORMATION.

AN AGENT’S GOALS MAY CHANGE CONSEQUENTLY.

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USING PROBABILITIES, A HIERARCHY CAN NOT BE ESTABLISHED WHEN HAVING MULTIPLE GOALS.

OUR APPROACH

POSSIBILISTIC FRAMEWORK

DEMOGRAPHICS

FACILITIES

TRANSPORT

URBAN FORM

ECONOMY

SIMULATION

AGENT TYPE

6.

7.

3.

8.

9.

7.

10.

4.

5.

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8. https://image.flaticon.com/icons/png/512/33/33777.png
9. http://www.sekolahtiarakasih.sch.id/images/75811-team-leader.png
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BDI FRAMEWORK

\phi, \tau_\phi
ϕ,τϕ\phi, \tau_\phi
*
∗*
\pi
π\pi

SIMULATION LAYER

\phi
ϕ\phi
\tau_\phi
τϕ\tau_\phi

FORMULA

DEGREE OF UNCERTAINTY

\pi
π\pi

POSSIBILITY DISTRIB.

BELIEFS

NECCESITY MEASURE

N
NN
N
NN
R_J
RJR_J

DELIBERATION

u
uu
\mathcal{J}
J\mathcal{J}

ELECTION

\Delta
Δ\Delta

AGENT TYPE

R_J
RJR_J
u
uu

RULE BASE

POSSIBILITY DISTRIB.

\Pi
Π\Pi

JUSTIFIED DESIRES

\mathcal{J}
J\mathcal{J}
\Delta
Δ\Delta

POSSIBILITY DISTRIB.

4.

5.

11.

7.

7.

4.

5.

11.

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BDI FRAMEWORK

\phi, \tau_\phi
ϕ,τϕ\phi, \tau_\phi
*
∗*
\pi
π\pi

BELIEFS

N
NN
R_J
RJR_J

DELIBERATION

u
uu
\mathcal{J}
J\mathcal{J}

ELECTION

\Delta
Δ\Delta
\Pi
Π\Pi

GOALS

PLANNER

GOALS

DESIRES

BELIEFS

actions

7.

11.

4.

5.

7.

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BDI FRAMEWORK

BUYS

BUILDS

BUYS

RENTS

AGENT MODEL DEFINITION

11.

5.

4.

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BDI FRAMEWORK

DEFINITION OF THE FINITE SET OF ATOMIC PROPOSITIONS (\(\mathcal{A}\))

\neg \phi \in \mathcal{L}
¬ϕ∈L\neg \phi \in \mathcal{L}
\phi \wedge \psi \in \mathcal{L}
ϕ∧ψ∈L\phi \wedge \psi \in \mathcal{L}
\phi \vee \psi \in \mathcal{L}
ϕ∨ψ∈L\phi \vee \psi \in \mathcal{L}
\phi \supset \psi \equiv \neg\phi \vee \psi
ϕ⊃ψ≡¬ϕ∨ψ\phi \supset \psi \equiv \neg\phi \vee \psi

Having \(\mathcal{L}\) as the propositional language:

For all \(\phi, \psi \in \mathcal{L}\):

\(ab\)

\(abl\)

\(ac\)

\(i\)

be a landlord

\(o\)

afford buying

afford buying land

afford constructing

invest

\(l\)

be an owner

10 / 17

BDI FRAMEWORK

DEFINITION OF THE DESIRE-GENERATION RULES (\(R\))

R is defined as an expresion of the form: \[\beta_R, \psi_R \Rightarrow_{D}^+ \phi\] where \(\beta_R, \psi_R, \phi \in \mathcal{L}\). Also, \(\alpha \Rightarrow_{D}^+ \phi\) with \(\alpha \in (0, 1]\).

The investing degree for the current household is \(\alpha\):
\[\alpha \Rightarrow_{D}^+ i\]

If the household believes it affords buying and the household desires to change then the household desires to buy:
\[ab,ch \Rightarrow_{D}^+ b\]

FOR EXAMPLE:

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CASE STUDY

THEORETICAL

SCHELLING

TRANSPORT NETWORK

SCHELLING AND TRANSPORT NETWORK

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CASE STUDY

BOGOTA, COLOMBIA

13110

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HOUSEHOLDS

INITIAL INVESTORS

PROMOTERS

CITY FACILITIES

HOSPITALS

PARKS

CULTURAL INSTITUTIONS

SITES OF WORKSHIP

SCHOOLS

ROADS OF THE TRANSPORT NETWORK

1814

100

HIGHWAYS

AVENUES

ROADS

CASE STUDY

BOGOTA, COLOMBIA

RANDOM UTILITY FUNCTION

1 ARTERY OF TRANSPORT NETWORK

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CASE STUDY

BOGOTA, COLOMBIA

1 ARTERY, SOME CITY FACILITIES

ALL ARTERIES, ALL FACILITIES

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FUTURE WORK

MODULARIZATION AND PARAMETRIZATION

MODEL CALIBRATION

POSSIBILISTIC FRAMEWORK

SIMULATION

6.

7.

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13.

MAIN

BDI

DEMOGRAPHICS

FACILITIES

TRANSPORT

URBAN FORM

POLICY

ECONOMY

CONFIGURATION

FUTURE WORK

MODULARIZATION AND PARAMETRIZATION

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3.

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REFERENCES

  1. T. Schelling. 1971. Dynamic models of segregation. The Journal of Mathematical Sociology 1, 2 (1971), 143–186.

  2. N. Gilbert. 2006. When does social simulation need cognitive models? In Cogni- tion and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation,  R. Sun (Ed.). Cambridge University Press, Cambridge, 428–432.

  3. C. da Costa Pereira and A. Tettamanzi. 2010. Belief-Goal Relationships in Possi- bilistic Goal Generation. In ECAI 2010. 641–646.

  4. C. da Costa Pereira and A. Tettamanzi. 2010. An Integrated Possibilistic Frame- work for Goal Generation in Cognitive Agents. In AAMAS’10. 1239–1246.

  5. C. da Costa Pereira and A. Tettamanzi. 2014. Syntactic Possibilistic Goal Generation. In ECAI’14. 711–716.

QUESTIONS?

A New Urban Segregation-Growth Coupled Model using a Belief-Desire-Intention Possibilistic Framework

By Meili Vanegas-Hernandez

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