No abstract

In the analysis of trophic state of the water body is fundamental to know chlorophyll-a concentration. Thus, this work has as main aim to determinate and to assess the behavior of chlorophyll-a in the Itaparica reservoir, São Francisco river. This way, we used Landsat-TM imagery, in which it was used bands from 1 to 5 and 7. The algorithm used was written in LEGAL/SPRING 5.2. From the chlorophyll-a result was held slicing the water body in six concentration classes. As observed by histogram, the minimum value of Chl-a was < 1 µg/L and the highest was 249.5 µg/L. The classes that had the biggest area were Classe 01 (0-5 µg/L) with 27.4%, followed by Classe 02 (5-10 µg/L) with 24.6% of the total area of the study area. Through graphical analysis of points located along the reservoir it was possible to verify that chlorophyll concentration augmented from fluvial to lacustrine region and from the contact of streams with reservoir. In the next studies there is a need to validate the values with field data in order to verify the mapping accuracy in this reservoir, taking into account the day and also the transit time of the sensor.

In this paper, we discuss the experience in the design, use and evaluation of a serious game about participatory management of national parks for biodiversity conservation and social inclusion. Our objective is to help various stakeholders (e.g., environmentalist NGOs, communities, tourism operators, public agencies, and so on) to collectively understand conflict dynamics for natural resources management and to exercise negotiation management strategies for protected areas, one of the key issues linked to biodiversity conservation in national parks. Our serious game prototype combines, techniques such as: distributed role-playing games, support for negotiation between players, and insertion of various types of artificial agents (decision making agents, virtual players, assistant agents). After a general introduction to the project, we will present project's current prototype architecture and results from game sessions, as well as some prospects for the future, namely: the design of assistant artificial agents and of virtual players and the integration of a viability-based simulation engine.

The objective of this paper is to reflect on our experience in a serious game research project, named SimParc, about multi-agent support for participatory management of protected areas for biodiversity conservation and social inclusion. Our project has a clear filiation with the MAS-RPG methodology developed by the ComMod action-research community, where multi-agent simulation (MAS) computes the dynamics of the resources and role-playing game (RPG) represents the actions and dialogue between stakeholders about the resources. We have explored some specific directions, such as: dialogue support for negotiation; argumentation-based decision making and its explanation; technical assistance to the players based on viability modeling. In our project, multi-agent based simulation focuses on the negotiation process itself, performed by human players and some artificial participants/agents, rather than on the simulation of the resources dynamics. Meanwhile, we have also reintroduced the modeling of the socioecosystem dynamics, but as a local technical assistance/analysis tool for the players.

This paper addresses an ongoing experience in the design of an artificial agent taking decisions and combining them with the decisions taken by human agents. The context is a serious game research project, aimed at computer-based support for participatory management of protected areas (and more specifically national parks) in order to promote biodiversity conservation and social inclusion. Its objective is to help various stakeholders (e.g., environmentalist, tourism operator) to collectively understand conflict dynamics and explore negotiation strategies for the management of parks. In this paper, after introducing the design of our serious game, named SimParc, we will describe the architecture of the decision making agent playing the role of the park manager. In the game, the park manager makes final decisions based on its own analysis and also on the votes of the stakeholders. It includes two modules: 1) individual decision -based on a model of argumentation, which also provides a basis to justify and explain the decision; 2) participatory decision -to take into account the preferences/votes from the stakeholders.