=Paper=
{{Paper
|id=Vol-1157/sp3
|storemode=property
|title=Towards Using i* for Modeling Mega-Urban Processes
|pdfUrl=https://ceur-ws.org/Vol-1157/paper21.pdf
|volume=Vol-1157
|dblpUrl=https://dblp.org/rec/conf/istar/LiebenbergMBL14
}}
==Towards Using i* for Modeling Mega-Urban Processes==
https://ceur-ws.org/Vol-1157/paper21.pdf
Towards Using i* for Modelling
Mega-Urban Processes
Martin Liebenberg1 , Victor Mataré2 , Klaus Baier2 , and Gerhard Lakemeyer1
1
Knowledge-Based Systems Group (KBSG), RWTH Aachen University, Ahornstraße
55, D-52056 Aachen, Germany,
{liebenberg, gerhard}@kbsg.rwth-aachen.de
2
Chair of Engineering Geology and Hydrogeology (LIH), RWTH Aachen University,
Lochnerstraße 4-20, D-52064 Aachen, Germany
{matare, baier}@lih.rwth-aachen.de
Abstract. In this paper, we consider the use of i* for modelling social
networks in the context of mega-urbanization and water resources. In
particular, we discuss, mostly by way of example, issues that are special
to this domain such as the need to explicitly model nature or a new
kind of agent evolution. Another important issue is the usability of i*
by scientists from different disciplines to model mega-urban scenarios
without any background in programming or mathematical models.
1 Introduction
In the literature megacities are characterized as cities with at least five to ten
million residents. Because of their size, megacities are confronted with severe
problems regarding water quality. Especially in cities situated in less developed
countries where the government tends to be inefficient in providing proper in-
frastructures for the fast growing urbanization. Those cities have large areas
with informal settlements, for example slums, which are not properly connected
to a water grid and much less to sewerage resulting in bad conditions for waste
disposal and pollution of various qualities. Other sources of water pollution are
factories and even agriculture within the urbanized areas [7].
Because of the size and the fast growth of megacities, it is hard to survey
the very complex processes which influence the water quality in such big ur-
banized regions. To better understand such processes the SiKAMUS3 project
was originated to simulate the behaviour of inhabitants of megacities. During
such a simulation the residents are represented by agents who do their work in
a megacity while consuming and polluting water.
The project is based on the multi-agent simulation framework SNet [5], which
is intended for the modelling and simulation of inter-organizational networks.
SNet primarily uses (extended) i* SR diagrams [12] to describe the relationships
between the various stakeholders in such networks. These diagrams are then
automatically translated into programs of the action language ConGolog [4],
3
Simulating Knowledge-Based Agents in Mega-Urban Systems [2]
which are the basis of the simulations. Here we follow the same methodology
and hence focus on SR diagrams as our main i* modelling tool.
Since the domain experts in our project are hydrogeologists, who often have
little background in formal methods (as understood in computer science), an-
other objective is to provide a user-friendly interface to make it possible for
those researchers to model and simulate different hydrological megacity scenar-
ios without the need for aid from computer scientists or mathematicians.
In the next section we briefly outline, mainly by way of a small example,
first thoughts on how to use and extend SR diagrams to capture our application
domain. We then highlight a number of other i*-related issues that are currently
under investigation or still need to be addressed in the future.
2 Modelling Megacities
In our application domain the main use of i* SR diagrams is to model the rela-
tionships between residents of a megacity and their influence on water resources.
In the future, this modelling should be done by geologists and urban planners
to improve the water quality in megacities. We think that i* is a proper tool for
these users because it avoids programming simulations.
The typical use of i* lies in the early phases of requirements engineering,
where the actors can be both humans and technical artifacts such as software
systems. In the context of megacities we are mainly dealing with human ac-
tors whose behaviour influences the environment they inhabit. The relationships
between humans can in principle be modelled by i*, but as we will see below
there are novel features which may call for an extension of the formalism. Since
the environment (nature) plays a central role in our domain, there is also the
question how to model nature and its interaction with humans.
In order to use the SNet simulation framework for megacity scenarios we had
to adapt the i* dialect used in SNet. In Figure 1 we see an i* diagram for a
small fragment of our application scenario. It depicts the relationship between
water users and local water suppliers in a megacity like Hyderabad: A water
user tries to get water with a good quality from a reliable resource. She gets the
water from a local water supplier who gets the water from a natural resource
and maintains a water grid to distribute the water. If the water quality tends to
be on a low level, a number of water users may join forces to exert pressure on
the water supplier to raise the water quality.
In Figure 1 we used links between agents like in SNet. One of these links
used by SNet is the softgoal contribution link. An example for that is the link
from the task “improve infrastructure” to the softgoal “supply reliability”, which
means that an improvement of the infrastructure contributes to the reliability
of the water supply. Another SNet link between agents is the decomposition link
between the task “accept quality” and “supply water” which, in SNet, means
delegating a task to another agent. However, in our domain of megacities such
links are better understood as demands. For instance, if a water user accepts
the quality of the water, she rather demands water from the local water supplier
Fig. 1. A small example model of water usage in a megacity.
instead of delegating the task “supply water”. Finally, we use SNet links to and
from the triangle in the diagram in Figure 1 which is called precondition-effect
element in SNet. That is, if the task “complain about quality”, the precondition,
is fulfilled, a value representing the discontent with the water supply is raised.
If then a certain threshold for discontent is exceeded, the effect is that the task
“gather affected people” can be performed.
This means that any water user who complains about the water quality raises
the discontent which eventually leads to gathering affected people. If we model it
like that, the repeated complaining of one water user is enough to gather affected
people. But if we want that gathering people needs more than one water user,
there is no obvious way to model this. For that reason we decided to enhance
i* to be able to express that we need more than one agent for issues like raising
discontent. In particular, we propose something similar to cardinalities in entity-
relationship diagrams. The “50..N” next to the arrow towards the precondition-
effect element indicates that more than fifty different agents must be discontented
until the effect can occur. Note that this is another kind of threshold than in
the precondition-effect elements themselves. There the threshold concerns the
quantity of the subject of the element; here discontent. The cardinalities indicate
that the effect occurs only if this threshold is exceeded by raising the discontent
by, in this case, at least 50 different agents.
Furthermore, to model hydrological scenarios in a megacity we need some-
thing to represent nature. That is necessary because we have natural resources
like groundwater and natural phenomenons like rain and floods which influence
the water level and quality.
In the middle of the diagram in Figure 1 we see a green octagon which is
a proposal for a new element. An interesting question is: what kind of resource
is the water resource? The problem here is that water is not provided by an
agent; it is a natural resource. Furthermore, it is influenced by natural forces
like rain. On the other hand, it is influenced by the behaviour of the citizens of a
megacity. For instance, the water can be contaminated by waste from informal
households and production processes. On the other side, the government can
manage to build a water grid and sewerage to raise the quality of the water
resources. Because the resource is not provided by some agent and is influenced
by many others, we think that we cannot use the original i* resource element
for natural resources. Therefore, we propose a new element whose first version
is the green octagon in Figure 1. The link from this element to the task “supply
water” means that this task is only possible to perform if the water resource
provides water.
In addition, in Figure 1 we also use resources which are provided from agents
rather than from nature. For instance, the link to the resource element “funds”
in the diagram means that the water users raise the funds by paying their water
bills. Furthermore, the links from “funds” to the tasks “maintain water grid”
and “supply water” mean that these tasks can only be performed if the funds
are sufficient. The nature resources as well as these resources are not used in
SNet.
Finally, a problem occurring in modelling megacities is that diagrams soon
become very large, often with many links between elements, which users find
confusing. For that reason we need a proper approach to make such big dia-
grams manageable. A first attempt to overcome this problem can be seen in the
income resource in Figure 1. We propose to modularize the diagram by con-
necting separate diagrams with linking elements in the diagram linking to other
diagrams. The small arrow at the right upper corner at the resource element
indicates a link to the diagram with the provider of this resource.
In conclusion, i* appears to be a promising tool for modelling actors and their
interdependencies in megacities, but some additional features may be needed for
this purpose.
As in SNet the i* diagrams serve as input for a translation into ConGolog pro-
grams, whose execution generate simulations for particular instantiations of the
given network. In our case additional information is provided by geodata which
describes the surroundings of agents in a megacity. In particular, we make use of
existing data about groundwater quality, water management and urban growth
in megacities like Hyderabad (India), Guangzhou (China) and Yogyakarta4 (In-
donesia) [10, 11, 8]. From the gathered data a semantic geodatabase was built
using OWL [3]. It contains information about land-use units, where each of
them represents a piece of land in a megacity. In short, it is stored for what this
4
Although Yogyakarta is not a megacity it is considered because of its fast growth
which is comparable with that of megacities.
particular land is used, what kind of housing and workplaces are placed on it and
what is located in the neighbourhood. Additionally, it is stored what geological
properties the land has.
While the information contained in the OWL database is crucial to obtain
faithful simulations, it is unclear at this point how much of the OWL ontology
needs to be reflected in the i* model.
3 Ongoing and Future Work
We are in the middle of investigating how we can model megacities. Therefore,
the proposed adaptations of i* are likely not the final versions. To complete our
work we still have to consider the following.
For instance, if we think about natural phenomenons, we can also think about
representing nature as an actor. But in contrast to the predominant view in i*,
this actor acts according to natural laws and without intentions.
This notion of nature then can provide natural resources. The work is still
in progress, so we have no complete concept for this ready. Furthermore, we still
need a semantics for natural elements. Since, as mentioned above, the ontology
stored in an OWL database determines the surrounding of an agent, we think the
semantics of natural elements has to follow the structure in the OWL ontology.
For example, if we instantiate the water resource element in Figure 1, we dispose
where an agent is situated in the megacity and depending on this we set the
availability of this resource.
Furthermore, the third role in the diagram in Figure 1 besides “local water
supplier” and “water user” is “water quality activism.” This should be played
by some agent who exerts influence on the water supplier and gathers people
to raise the pressure. But if we think about people who complain about the
government, it must be more than one agent which plays this role. In addition,
these have to be the same agents which play the role “water user.” For this,
agents have to change their roles; it should be possible that a water user becomes
an activist. Roesli et al. [9] proposed two ways of agent evolution. The first way is
to change a role if a certain threshold is exceeded. For instance, a role is changed
after getting some qualifications. This happens automatically after reaching the
needed qualifications. The second approach needs planning to make a role change
which means that it happens not automatically but with deliberation of an agent.
We think that the second approach fits most to the domain of discourse because,
for example, a resident of a slum needs some deliberation until she turns into a
protester against the government. One of our research objectives is to investigate
how we can model deliberation according to [9] and how we can enhance that.
For example, we want to model a goal-oriented role change. That is, we think
about a meta-goal which always is the main goal through all role changes.
To modularize huge diagrams we proposed linking elements which connect
separated diagrams. Alencar et al. [1] presented two ways how to improve mod-
ularity of i* diagrams. First, they proposed so called aspects as new actor-like
elements in i*. Second, they proposed a model transformation approach to get
models with better clarity. Another approach for our project might be different
views of the same diagram, where some of its elements are hidden. An example
of such views is the concept of scenarios in the User Requirements Notation
(URN) [6]. It remains to be seen how approaches such as [1,6] can be adapted
to arrive at user-friendly tools to manage large i* diagrams.
In this paper we highlighted a number of issues that arise from using i* to
model actors in mega-urban cities with a focus on water resources. In many ways
we have only scratched the surface of the problems in this interesting domain.
In the future we hope to report on new findings, also regarding our long-term
goal of simulating agents in mega-urban scenarios.
References
1. F. Alencar, M. Lucena, C. Silva, E. Santos, and J. Castro. Improving the Modu-
larity of i* Models. In Fourth International i* Workshop, pages 3–8, 2010.
2. K. Baier, V. Mataré, M. Liebenberg, and G. Lakemeyer. Towards Integrated Inten-
tional Agent Simulation and Semantic Geodata Management in Complex Urban
Systems Modeling. To appear in Computers, Environment and Urban Systems.
3. S. Bechhofer, F. van Harmelen, J. Hendler, I. Horrocks, D. L. McGuinness, P. F.
Patel-Schneider, and L. A. Stein. OWL Web Ontology Language Reference, 2004.
4. G. De Giacomo, Y. Lespérance, and H. J. Levesque. ConGolog, a concurrent pro-
gramming language based on the situation calculus. Artificial Intelligence, 121(1-
2):109–169, 2000.
5. G. Gans, G. Lakemeyer, M. Jarke, and T. Vits. SNet: A modeling and simu-
lation environment for agent networks based on i* and ConGolog. In Advanced
Information Systems Engineering, pages 328–343. Springer, 2006.
6. International Telecommunication Union. Recommendation Z.151 (10/2012), User
Requirements Notation (URN) - Language definition, 2012.
7. F. Kraas, S. Aggarwal, M. Coy, and G. Mertins, editors. Megacities: Our Global
Urban Future. Springer, 2014.
8. D. Putra and K. Baier. Impact of urbanization on groundwater recharge – the ex-
ample of the Indonesian Million City Yogyakarta. In UN Habitat – United Nations
Settlement Programs: Fourth session of the World Urban Forum, Nanjing, China,
Documentations of Germanys Contribution to a Sustainable Urban Future, 2008.
9. A. Roesli, D. Schmitz, G. Lakemeyer, and M. Jarke. Modelling Actor Evolu-
tion in Agent-Based Simulations. In G. A. Vouros, A. Artikis, K. Stathis, and
J. V. Pitt, editors, Organized Adaption in Multi-Agent Systems, First Interna-
tional Workshop, OAMAS 2008, Estoril, Portugal, volume 5368, pages 126–144.
Springer, 2008.
10. H. Wakode, K. Baier, R. Jha, and R. Azzam. Analysis of urban growth using
Landsat TM/ETM data and GIS – a case study of Hyderabad, India. Arabian
Journal of Geosciences, 7(1):109–121, 2014.
11. R. Wehrhahn, A. L. Bercht, C. L. Krause, R. Azzam, F. Kluge, R. Strohschön,
K. Wiethoff, and K. Baier. Urban Restructuring and Social and Water-Related Vul-
nerability in Megacities – the Example of the Urban Village of Xincún, Guangzhou
(China). Die Erde, 139(3):227–249, 2008.
12. E. S. Yu. Modelling Strategic Relationships for Process Reengineering. PhD thesis,
Department of Computer Science, University of Toronto, 1995.