Multi-agent based electricity market simulator with VPP: Conceptual and implementation issues

This paper presents a new architecture for the MASCEM, a multi-agent electricity market simulator. This is implemented in a Prolog which is integrated in the JAVA program by using the LPA Win-Prolog Intelligence Server (IS) provides a DLL interface between Win-Prolog and other applications. This paper mainly focus on the MASCEM ability to provide the means to model and simulate Virtual Power Producers (VPP). VPPs are represented as a coalition of agents, with specific characteristics and goals. VPPs can reinforce the importance of these generation technologies making them valuable in electricity markets.

Multi-apprentice learning for meta-heuristics parameter tuning in a multi agent scheduling system

The scheduling problem is considered in complexity theory as a NP-hard combinatorial optimization problem. Meta-heuristics proved to be very useful in the resolution of this class of problems. However, these techniques require parameter tuning which is a very hard task to perform. A Case-based Reasoning module is proposed in order to solve the parameter tuning problem in a Multi-Agent Scheduling System. A computational study is performed in order to evaluate the proposed CBR module performance.

Case-based reasoning for meta-heuristics self-parameterization in a multi-agent scheduling system

A novel agent-based approach to Meta-Heuristics self-configuration is proposed in this work. Meta-heuristics are examples of algorithms where parameters need to be set up as efficient as possible in order to unsure its performance. This paper presents a learning module for self-parameterization of Meta-heuristics (MHs) in a Multi-Agent System (MAS) for resolution of scheduling problems. The learning is based on Case-based Reasoning (CBR) and two different integration approaches are proposed. A computational study is made for comparing the two CBR integration perspectives. In the end, some conclusions are reached and future work outlined.

Cooperation mechanism for team-work based multi-agent system in dynamic scheduling through meta-heuristics

This paper describes a Multi-agent Scheduling System that assumes the existence of several Machines Agents (which are decision-making entities) distributed inside the Manufacturing System that interact and cooperate with other agents in order to obtain optimal or near-optimal global performances. Agents have to manage their internal behaviors and their relationships with other agents via cooperative negotiation in accordance with business policies defined by the user manager. Some Multi Agent Systems (MAS) organizational aspects are considered. An original Cooperation Mechanism for a Team-work based Architecture is proposed to address dynamic scheduling using Meta-Heuristics.

ABS4GD: a multi-agent system that simulates group decision processes considering emotional and argumentative aspects

Emotion although being an important factor in our every day life it is many times forgotten in the development of systems to be used by persons. In this work we present an architecture for a ubiquitous group decision support system able to support persons in group decision processes. The system considers the emotional factors of the intervenient participants, as well as the argumentation between them. Particular attention will be taken to one of components of this system: the multi-agent simulator, modeling the human participants, considering emotional characteristics, and allowing the exchanges of hypothetic arguments among the participants.

Personality, emotion, and mood in agent-based group decision making

Group decision making plays an important role in organizations, especially in the present-day economy that demands high-quality, yet quick decisions. Group decision-support systems (GDSSs) are interactive computer-based environments that support concerted, coordinated team efforts toward the completion of joint tasks. The need for collaborative work in organizations has led to the development of a set of general collaborative computer-supported technologies and specific GDSSs that support distributed groups (in time and space) in various domains. However, each person is unique and has different reactions to various arguments. Many times a disagreement arises because of the way we began arguing, not because of the content itself. Nevertheless, emotion, mood, and personality factors have not yet been addressed in GDSSs, despite how strongly they influence results. Our group’s previous work considered the roles that emotion and mood play in decision making. In this article, we reformulate these factors and include personality as well. Thus, this work incorporates personality, emotion, and mood in the negotiation process of an argumentbased group decision-making process. Our main goal in this work is to improve the negotiation process through argumentation using the affective characteristics of the involved participants. Each participant agent represents a group decision member. This representation lets us simulate people with different personalities. The discussion process between group members (agents) is made through the exchange of persuasive arguments. Although our multiagent architecture model4 includes two types of agents—the facilitator and the participant— this article focuses on the emotional, personality, and argumentation components of the participant agent.

Modeling group decision meeting participants with an Agent-based approach

This paper aims to present a multi-agent model for a simulation, whose goal is to help one specific participant of multi-criteria group decision making process.This model has five main intervenient types: the human participant, who is using the simulation and argumentation support system; the participant agents, one associated to the human participant and the others simulating the others human members of the decision meeting group; the directory agent; the proposal agents, representing the different alternatives for a decision (the alternatives are evaluated based on criteria); and the voting agent responsiblefor all voting machanisms.At this stage it is proposed a two phse algorithm. In the first phase each participantagent makes his own evaluation of the proposals under discussion, and the voting agent proposes a simulation of a voting process.In the second phase, after the dissemination of the voting results,each one ofthe partcipan agents will argue to convince the others to choose one of the possible alternatives. The arguments used to convince a specific participant are dependent on agent knowledge about that participant. This two-phase algorithm is applied iteratively.

ISEM: a multi-agent system that simulates competitive electronic marketplaces

With the increasing importance of large commerce across the Internet it is becoming increasingly evident that in a few years the Iternet will host a large number of interacting software agents. a vast number of them will be economically motivated, and will negociate a variety of goods and services. It is therefore important to consider the economic incentives and behaviours of economic software agents, and to use all available means to anticipate their collective interactions. This papers addresses this concern by presenting a multi-agent market simulator designed for analysing agent market strategies based on a complete understanding of buyer and seller behaviours, preference models and pricing algorithms, consideting risk preferences. The system includes agents that are capable of increasing their performance with their own experience, by adapting to the market conditions. The results of the negotiations between agents are analysed by data minig algorithms in order to extract rules that give agents feedback to imprive their strategies.

Agent based simulation for group formation

Group decision making plays an important role in today’s organisations. The impact of decision making is so high and complex, that rarely the decision making process is made just by one individual. The simulation of group decision making through a Multi-Agent System is a very interesting research topic. The purpose of this paper it to specify the actors involved in the simulation of a group decision, to present a model to the process of group formation and to describe the approach made to implement that model. In the group formation model it is considered the existence of incomplete and negative information, which was identified as crucial to make the simulation closer to the reality.

Minimização de odores em contentores de resíduos sólidos

Mestrado em Engenharia Química

Iron oxide/gold core/shell nanomagnetic probes and CdS biolabels for amplified electrochemical immunosensing of Salmonella typhimurium

There is an imminent need for rapid methods to detect and determine pathogenic bacteria in food products as alternatives to the laborious and time-consuming culture procedures. In this work, an electrochemical immunoassay using iron/gold core/shell nanoparticles (Fe@Au) conjugated with anti-Salmonella antibodies was developed. The chemical synthesis and functionalization of magnetic and gold-coated magnetic nanoparticles is reported. Fe@Au nanoparticles were functionalized with different self-assembled monolayers and characterized using ultraviolet-visible spectrometry, transmission electron microscopy, and voltammetric techniques. The determination of Salmonella typhimurium, on screen-printed carbon electrodes, was performed by square-wave anodic stripping voltammetry through the use of CdS nanocrystals. The calibration curve was established between 1×101 and 1×106 cells/mL and the limit of detection was 13 cells/mL. The developed method showed that it is possible to determine the bacteria in milk at low concentrations and is suitable for the rapid (less than 1 h) and sensitive detection of S. typhimurium in real samples. Therefore, the developed methodology could contribute to the improvement of the quality control of food samples.

188Re-PEI-MP as potential agent for metabolic radiotherapy

Introduction: 188Re is a promising radionuclide for metabolic therapy because of the emission of high energy beta-particles. The development of watersoluble bone-seeking polymers such as PEI-MP (polyethyleneimine, functionalised with methylphosphonate-groups) that might be labeled with 188Re are recent approaches, with a strong potential for bone cancer treatment. The aim of this study was to evaluate the efficacy of 188Re-PEI-MP, as therapeutic agent for osteosarcoma, through in vitro and in vivo models.

Heterogeneous kinetics of the reduction of chromium (VI) by elemental iron

Zero valent iron (ZVI) has been extensively used as a reactive medium for the reduction of Cr(VI) to Cr(III) in reactive permeable barriers. The kinetic rate depends strongly on the superficial oxidation of the iron particles used and the preliminary washing of ZVI increases the rate. The reaction has been primarily modelled using a pseudo-first-order kinetics which is inappropriate for a heterogeneous reaction. We assumed a shrinking particle type model where the kinetic rate is proportional to the available iron surface area, to the initial volume of solution and to the chromium concentration raised to a power ˛ which is the order of the chemical reaction occurring at surface. We assumed α= 2/3 based on the likeness to the shrinking particle models with spherical symmetry. Kinetics studies were performed in order to evaluate the suitability of this approach. The influence of the following parameters was experimentally studied: initial available surface area, chromium concentration, temperature and pH. The assumed order for the reaction was confirmed. In addition, the rate constant was calculated from data obtained in different operating conditions. Digital pictures of iron balls were periodically taken and the image treatment allowed for establishing the time evolution of their size distribution.

Six thinking hats for electricity markets simulation: adapting meeting tools to agent decision

In almost all industrialized countries, the energy sector has suffered a severe restructuring that originated a greater complexity in market players’ interactions. The complexity that these changes brought made way for the creation of decision support tools that facilitate the study and understanding of these markets. MASCEM – “Multiagent Simulator for Competitive Electricity Markets” arose in this context providing a framework for evaluating new rules, new behaviour, and new participants in deregulated electricity markets. MASCEM uses game theory, machine learning techniques, scenario analysis and optimisation techniques to model market agents and to provide them with decision-support. ALBidS is a multiagent system created to provide decision support to market negotiating players. Fully integrated with MASCEM it considers several different methodologies based on very distinct approaches. The Six Thinking Hats is a powerful technique used to look at decisions from different perspectives. This tool’s goal is to force the thinker to move outside his habitual thinking style. It was developed to be used mainly at meetings in order to “run better meetings, make faster decisions”. This dissertation presents a study about the applicability of the Six Thinking Hats technique in Decision Support Systems, particularly with the multiagent paradigm like the MASCEM simulator. As such this work’s proposal is of a new agent, a meta-learner based on STH technique that organizes several different ALBidS’ strategies and combines the distinct answers into a single one that, expectedly, out-performs any of them.

Green production of zero-valent iron nanoparticles using tree leaf extracts

The interest in zero-valent iron nanoparticles has been increasing significantly since the development of a green production method in which extracts from natural products or wastes are used. However, this field of application is yet poorly studied and lacks knowledge that allows the full understanding of the production and application processes. The aim of the present work was to evaluate the viability of the utilization of several tree leaves to produce extracts which are capable of reducing iron(III) in aqueous solution to form nZVIs. The quality of the extracts was evaluated concerning their antioxidant capacity. The results show that: i) dried leaves produce extracts with higher antioxidant capacities than non-dried leaves, ii) the most favorable extraction conditions (temperature, contact time, and volume:mass ratio) were identified for each leaf, iii) with the aim of developing a green, but also low-cost,method waterwas chosen as solvent, iv) the extracts can be classified in three categories according to their antioxidant capacity (expressed as Fe(II) concentration): >40 mmol L−1; 20–40 mmol L−1; and 2–10 mmol L−1; with oak, pomegranate and green tea leaves producing the richest extracts, and v) TEManalysis proves that nZVIs (d=10–20 nm) can be produced using the tree leaf extracts.