Microbial Fuel Cells Using Agent-Based Simulation

Microbial Fuel Cells (MFC) could generate electrical energy combined with the wastewater treatment and they can be a promising technological opportunity. This chapter presents an agent-based model and simulation of MFC comparing it with analytical models, to show that this approach could model and simulate these problems with more abstraction and with excellent results.

Participatory Management of Protected Areas for Biodiversity Conservation and Social Inclusion

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.

Using Probability Distributions in Parameters of Variables at Agent-Based Simulations

Even treatable and preventable with medication, tuberculosis (TB) continues to infect and cause deaths globally, especially in the poorest countries and in most vulnerable parts of the rich countries. Given this situation, the study of the growth curve of Mycobacterium tuberculosis, which causes tuberculosis, can be a strong ally against TB. This study models the growth curve of Mycobacterium tuberculosis using simulation based agents, aiming to simulate the curve with the minimum possible error when compared to in vitro results. To implement this model, the agents represent the bacteria in their habitat and how they interact with each other and the environment. Some parameters of the agents are modelled with probability distributions.

Mase

This chapter presents research carried out under the MASE project, including the definition of a conceptual model to characterize the behavior of individuals that interact in the dynamics of land-use and cover change. A computational tool for analyzing environmental scenarios of land change was developed, called MASE - Multi-Agent System for Environmental Simulation. MASE enables agent-based simulation scenarios and integrates the influence of socio-economic and political dynamics through the interaction of agents with rules of land-use and planning policies and the environmental physical and spatial variables. MASE simulator was extended to implement the Belief-Desire-Intention (BDI) model, called MASE-BDI. MASE and MASE-BDI are discussed including the conceptual model complexity and statistical techniques of map comparison to land change models. Two real cases of the Brazilian Cerrado validate quantitative and qualitative aspects of MASE and MASE-BDI simulators. Finally, the authors present some auto-tuning aspects of adjusting simulation parameters of MASE-BDI.

Ignition of Algorithm Mind

The ignition of the algorithmic mind is a fascinating phenomenon that occurs in our brains. The algorithm mind is related to our reasoning. When we use it, we consume a lot of resources from our brains like energy. The ignition process is triggered by reflective mind and it works through neuronal assemblies. Specific neurons are ignited and then it begins a recruitment process for other neurons in order to assemble a complex structure. To understand these mechanisms, we have developed a simple multi-agent model, where we explored the role of energy and respective limits on neuronal assemblies. The available and consumed energy are the keystones to ignite the algorithm mind and to find out the limit that interrupts our reasoning's. The connections between incumbent and new neurons are at the same level as the connections established only between the new neurons in the case of algorithmic mind. Unlike, the autonomous mind established more connections, only between new neurons. Finally, the algorithmic mind consumes more energy than autonomous mind, which has a clearly declining trend.

Biomass Variation Phytoplanktons Using Agent-Based Simulation

The phytoplanktons are organisms that have limited locomotion about the current being drift in aquatic environment. Another characteristic of phytoplankton their growth and energy are result about photosynthetic process. It is important to emphasize that the phytoplankton is the main primary producer of aquatic environment, it means that, it is the base the aquatic food chain . The organic material produced by phytoplankton is responsible in provide the material and energy which sustains the growth of fish, crustaceans and mollusks, in marine ecosystems. Because of this, it is important to know the factors that interfere with their accumulation in environments mainly in fishing regions. In this way, this study tries to demonstrate the importance of retention time, often caused by hydrological issues, in the variation of phytoplankton biomass in the estuary of the Patos Lagoon (ELP), in Rio Grande/RS. To do that, we created one model that simulates this environment, using techniques of multi-agent-based simulation and its implementation was done with the NetLogo tool.

Use SUMO Simulator for the Determination of Light Times in Order to Reduce Pollution

Nowadays, urban mobility and air quality issues are prominent, due to the heavy traffic of vehicles and the emission of pollutants dissipated in the atmosphere. In the literature, a model of optimal control of traffic lights using Genetic Algorithms (GA) has been proposed. These algorithms have been introduced in the context of control traffic. In order to search for possible solutions to the problems of traffic lights in major urban centers. Thus, the study of the dispersion of pollutants and Genetic Algorithms with simulations performed in Urban Mobility Simulator SUMO (Simulation of Urban Mobility), seek satisfactory solutions to such problems. The AG uses the crossing of chromosomes, in this case the times of the traffic lights, featuring the finest green light times and the sum of each of the pollutants each simulation cycle. The simulations were performed and the results compared analyzes showed that the use of the genetic algorithm is very promising in this context.

Agent-Based Modelling in Multicellular Systems Biology

This chapter aims at discussing the content of multi-agent based simulation (MABS) applied to computational biology i.e., to modelling and simulating biological systems by means of computational models, methodologies, and frameworks. In particular, the adoption of agent-based modelling (ABM) in the field of multicellular systems biology is explored, focussing on the challenging scenarios of developmental biology. After motivating why agent-based abstractions are critical in representing multicellular systems behaviour, MABS is discussed as the source of the most natural and appropriate mechanism for analysing the self-organising behaviour of systems of cells. As a case study, an application of MABS to the development of Drosophila Melanogaster is finally presented, which exploits the ALCHEMIST platform for agent-based simulation.

A Scalable Multiagent Architecture for Monitoring Biodiversity Scenarios

Recently, there has been an explosive growth in the use of wireless devices, mainly due to the decrease in cost, size, and energy consumption. Researches into Internet of Things have focused on how to continuously monitor these devices in different scenarios, such as environmental and biodiversity tracking, considering both scalability and efficiency while searching and updating the devices information. For this, a combination of an efficient distributed structure and data aggregation method is used, allowing a device to manage a group of devices, minimizing the number of transmissions and saving energy. However, scalability is still a key challenge when the group is composed of a large number of devices. In this chapter, the authors propose a scalable architecture that distributes the data aggregation responsibility to the devices of the boundary of the group, and creates agents to manage groups and the interaction among them, such as merging and splitting. Experimental results showed the viability of adopting this architecture if compared with the most widely used approaches.

Multi-Agent Systems in Three-Dimensional Protein Structure Prediction

Tertiary protein structure prediction in silico is currently a challenging problem in Structural Bioinformatics and can be classified according to the computational complexity theory as an NP-hard problem. Determining the 3-D structure of a protein is both experimentally expensive, and time-consuming. The agent-based paradigm has been shown a useful technique for the applications that have repetitive and time-consuming activities, knowledge share and management, such as integration of different knowledge sources and modeling of complex systems, supporting a great variety of domains. This chapter provides an integrated view and insights about the protein structure prediction area concerned to the usage, application and implementation of multi-agent systems to predict the protein structures or to support and coordinate the existing predictors, as well as it is advantages, issues, needs, and demands. It is noteworthy that there is a great need for works related to multi-agent and agent-based paradigms applied to the problem due to their excellent suitability to the problem.