Abstract
Affective computing has increased its significance both in terms of academic and industry attention and investment. Alongside, immersive digital environments have settled as a reliable domain, with progressively inexpensive hardware solutions. Having this in mind, the authors envisioned the automatic real-time user emotion extraction through biometric readings in an immersive digital environment. In the running example, the environment consisted in an aeronautical simulation, and biometric readings were based mainly on galvanic skin response, respiration rate and amplitude, and phalanx temperature. The assessed emotional states were also used to modify some simulation context variables, such as flight path, weather conditions and maneuver smoothness level. The results were consistent with the emotional states as stated by the users, achieving a success rate of 77%, considering single emotions and 86% considering a quadrant-based analysis. © Springer Science & Business Media BV 2009.

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Abstract

Abstract
This work presents a methodology for modelling and simulating aquatic ecological systems and an associated methodology and software for creating Decision Support Systems (DSS) for this kind of environment. Both are based on object oriented programming (OOP) and, in the case of the DSS, on Autonomous Intelligent Agents. The modelling software (EcoDynamo) includes several object dynamic link libraries to simulate different physical and biogeochemical ecosystem processes. These libraries were designed to be possible their linkage with different model shells, for the sake of portability and usability. A high level communication language (ECOLANG) was developed to allow the communication between EcoDynamo and the agents. The DSS uses one methodology of discrete multi-criteria, where given a users preference structure (elicited with the Analytic Hierarchy Process methodology - AHP), a priority ranking of the preprocessed scenarios is achieved. To validate the approach, EcoDynamo was used with the DSS to simulate different management scenarios in a coastal lagoon at the South of Portugal - Ria Formosa. The experiments performed showed that these tools may be widely used by people involved in the management of coastal areas to integrate environmental, economic and social issues in the decision process, without an in-depth knowledge of modelling methodologies.

Abstract
This paper presents the development of a bivalve farmer agent interacting with a realistic ecological simulation system. The purpose of the farmer agent is to determine the best combinations of bivalve seeding areas in a large region, maximizing the production without exceeding the total allowed seeding area. A system based on simulated annealing, tabu search, genetic algorithms and reinforcement learning, was developed to minimize the number of iterations required to unravel a semi-optimum solution by using customizable tactics. The farmer agent is part of a multi-agent system where several agents, representing human interaction with the coastal ecosystems, communicate with a realistic simulator developed especially for aquatic ecological simulations. The experiments performed revealed promising results in the field of optimization techniques and multi-agent systems applied to ecological simulations. The results obtained open many other possible uses of the simulation architecture with applications in industrial and ecological management problems, towards sustainable development.

Abstract
Simulating ecological models is always a difficult task, not only because of its complexity but also due to the slowness associated with each simulation run as more variables and processes are incorporated into the complex ecosystem model. The computational overhead becomes a very important limitation for model calibration and scenario analysis, due to the large number of model runs generally required. This paper presents a framework for ecological simulations that intends to increase system performance through the ability to do parallel simulations, allowing the joint analysis of different scenarios. This framework evolved from the usage of one simulator and several agents, that configure the simulator to run specific scenarios, related to possible ecosystem management options, one at a time, to the use of several simulators, each one simulating a different scenario concurrently, speeding up the process and reducing the time for decision between the alternative scenarios proposed by the agents. This approach was tested with a farmer agent that seeks optimal combinations of bivalve seeding areas in a large mariculture region, maximizing the production without exceeding the total allowed seeding area. Results obtained showed that the time needed to acquire a "near" optimal solution decreases proportionally with the number of simulators in the network, improving the performance of the agent's optimization process, without compromising its rationality. This work is a step forward towards an agent based decision support system to optimize complex environmental problems.