PERDIDA DE SU ESPOSO

For GIS in general, the profile of users is rapidly changing from ‘computer specialists’ to the ‘application-oriented users’ with little or no familiarity with the idiosyncrasies of computer systems (Spaccapietra, 2001). Hence, there is a great demand for friendlier and simpler human-computer interfaces that support application-oriented interactions. Given the fact that the user groups of PSS – planners and a variety of stakeholders – are all application-oriented, this demand also holds for any PSS that seeks successful deployment. Consequently, PSS interfaces ought to be flexible in order to account for the typical interactions with casual users that can be rather abrupt and unstructured. As described before, multi-agent technology provides the means – interface agents – to change the system from a dumb receptor of task descriptions to an entity that cooperate with the user to achieve their goal (Jennings and Wooldridge, 1998). For instance, Tang

interactively assist users with forming geographic queries. Such interface agents can be structurally provided by means of an organization (section 3.1.2), enabling them to cope with the possibly unstructured user requests.

Klosterman (2001) claimed that the difficulty of realizing the ideal PSS is not so much the development of the required analytic and display modules as is the integration of all these modules, and especially the development of standards for data storage, access and interchange. Some research studies can be found that illustrate the potential of multi- agent technology to serve such software design and operation issues. For instance, Sengupta and Bennett (2003) presented agents that emulate the behaviour of GIS analysts to retrieve spatial data and analytical models from the Internet, and to transform such data in order to meet the input requirements of models through the use of GIS software. Tang

et al. (2001) addressed the integration of heterogeneous data sources by means of a

broker-agent architecture (section 3.2.3). Rodriques et al. (1997) developed a multi-agent concept for a system that is able to dynamically evolve as the requirements and data associated with the system change. The incorporated agents are capable of reasoning over representations of space, while intended to give support with locating and retrieving spatial information in large networks, to facilitate GIS user interfaces, to implement improved spatial tasks, and to create interfaces between GIS and specific software packages (see also Rodrigues and Raper, 1999). These studies give good reasons to believe that multi-agent technology can be the guiding principle for implementing and following standards for data management and thus for increasing PSS interoperability.

With concern to the core functions of PSS – providing the means for both generating and evaluating alternative plans (section 2.2.2) – it is obvious that planning support tools need to have a geographical context in order to deal with aspects of location, adjacency and accessibility. Successful attempts have been made with concern to developing tools that integrate multi-agents and GIS. However, the attention has been mostly restricted to the development of agent-based micro-simulation tools that visualize and analyse time- evolving processes in an urban environment by using agents to model the behaviour of individuals, e.g. regarding shopping (Koch, 2000), recreation (Bishop & Gimblett, 2000) or patterns of daily activities.4 The agents are submerged into a virtual urban environment and interact with each other and their environment. The use of multi-agents for micro-

4

See Parker et al. (2003) for an extensive review of agent-based micro-simulation tools for land use and land cover change.

simulation purposes has already resulted in valuable and promising tools for predicting and evaluating the effects of different policy scenarios and plan alternatives as part of the

Choice phase in urban plan development (section 1.1.2).

Concurrently, from literature it seems to be far less evident to use multi-agent technology for decision support in the preceding Design phase of urban plan development in which the alternatives are generated. Such a design-oriented application would opt for an agent representation of the various specialists involved in that phase, implying a fundamentally different approach to the implementation and operation of agents. Whereas in micro-simulations agents are situated inside the environment and act within that environment, in applications where they represent specialists they will actually be situated

outside the environment and only act upon it (cf., a group of experts gathering around a

map representation of the environment). Despite the lack of attempts to utilize multi- agent technology for this designing part of urban plan development, it would be entirely complementary to micro-simulation tools, providing the means to quickly generate sets of alternative plans, while offering insight into the effects of decision-makers’ trade-offs during the process. It brings about opportunities to shed light on the problem-solving side of planning as well, in addition to the study of urban phenomena.

Some examples do exist that support the idea that multi-agent technology is equally suitable for supporting group decision-making processes like alternative plan generation. In Ligtenberg et al. (2001) and Ligtenberg et al. (2004) an application, leaning on cellular automata techniques, is discussed in which agents are applied to simulate multi-actor spatial planning. In this application agents have the same overall objective – locating space for urbanization – but as they represent different interest groups – e.g., planning authorities, farmers organizations and environmentalists – they have different preferences. Based on a set of predefined scenarios the agents negotiate and vote in a stepwise fashion to develop plans. Zamenopoulos and Alexiou (2003) presented a tool concept in which plans are generated by a group of distributed knowledge sources (humans or agents), mainly focussing on how to arrange it as an interactive process of continuous learning and adaptation. Ferrand (1996) applied multi-agent concepts to multi-actor spatial planning in three different applications and within the social and political context of the process as it appears in France. The first application uses the agent concept to represent physical objects (e.g., electricity masts) that solve location problems together; the second enables negotiation between distributed (human) actors by attaching an assisting agent to each of them that communicate with each other; the third simulates a spatial negotiation process

between actors that are implemented as agents.

Given the vast amount of tools that is likely to be necessary to have PSS facilitate the whole process of urban plan development, functionality is required to assist users in selecting appropriate tools and to manage the collections of tools. Yeh and Qiao (2004a, 2004b) proposed a knowledge-based PSS following a component-based software development approach. Their system provides assistance to users and model developers to build new models or to select predefined models from an existing model library for their problems. Multi-agent technology is used to handle model knowledge, to facilitate knowledge-based model selection and incremental model development, and to incorporate models and knowledge rules in a problem-solving process.