Multi-agent system for personalization of location-based services

The current ubiquitous network access and increase in network bandwidth are driving the sales of mobile location-aware user devices and, consequently, the development of context-aware applications, namely location-based services. The goal of this project is to provide consumers of location-based services with a richer end-user experience by means of service composition, personalization, device adaptation and continuity of service. Our approach relies on a multi-agent system composed of proxy agents that act as mediators and providers of personalization meta-services, device adaptation and continuity of service for consumers of pre-existing location-based services. These proxy agents, which have Web services interfaces to ensure a high level of interoperability, perform service composition and take in consideration the preferences of the users, the limitations of the user devices, making the usage of different types of devices seamless for the end-user. To validate and evaluate the performance of this approach, use cases were defined, tests were conducted and results gathered which demonstrated that the initial goals were successfully fulfilled.

Argumentation as Distributed Belief Revision: Conflict Resolution in Decentralised Multi-agent Systems

Decentralised co-operative multi-agent systems are computational systems where conflicts are frequent due to the nature of the represented knowledge. Negotiation methodologies, in this case argumentation based negotiation methodologies, were developed and applied to solve unforeseeable and, therefore, unavoidable conflicts. The supporting computational model is a distributed belief revision system where argumentation plays the decisive role of revision. The distributed belief revision system detects, isolates and solves, whenever possible, the identified conflicts. The detection and isolation of the conflicts is automatically performed by the distributed consistency mechanism and the resolution of the conflict, or belief revision, is achieved via argumentation. We propose and describe two argumentation protocols intended to solve different types of identified information conflicts: context dependent and context independent conflicts. While the protocol for context dependent conflicts generates new consensual alternatives, the latter chooses to adopt the soundest, strongest argument presented. The paper shows the suitability of using argumentation as a distributed decentralised belief revision protocol to solve unavoidable conflicts.

Environmental Decision Support: a Multi-Agent Approach

The environmental management domain is vast and encompasses many identifiable activities: impact assessment, planning, project evaluation, etc. In particular, this paper focusses on the modelling of the project evaluation activity. The environmental decision support system under development aims to provide assistance to project developers in the selection of adequate locations, guaranteeing the compliance with the applicable regulations and the existing development plans as well as satisfying the specified project requirements. The inherent multidisciplinarity features of this activity lead to the adoption of the Multi-Agent paradigm, and, in particular, to the modelling of the involved agencies as a community of cooperative autonomous agents, where each agency contributes with its share of problem solving to the final system’s recommendation. To achieve this behaviour the many conclusions of the individual agencies have to be justifiably accommodated: not only they may differ, but can be interdependent, complementary, irreconcilable, or simply, independent. We propose different solutions (involving both local and global consistency) to support the adequate merge of the distinct perspectives that inevitably arise during this type of decision making.

Belief Revision in Multi-Agent Systems

The ability to respond sensibly to changing and conflicting beliefs is an integral part of intelligent agency. To this end, we outline the design and implementation of a Distributed Assumption-based Truth Maintenance System (DATMS) appropriate for controlling cooperative problem solving in a dynamic real world multi-agent community. Our DATMS works on the principle of local coherence which means that different agents can have different perspectives on the same fact provided that these stances are appropriately justified. The belief revision algorithm is presented, the meta-level code needed to ensure that all system-wide queries can be uniquely answered is described, and the DATMS’ implementation in a general purpose multi-agent shell is discussed.

Consistency and Context Management in a Multi Agent Belief Revision Testbed

Multi-agent architectures are well suited for complex inherently distributed problem solving domains. From the many challenging aspects that arise within this framework, a crucial one emerges: how to incorporate dynamic and conflicting agent beliefs? While the belief revision activity in a single agent scenario is concentrated on incorporating new information while preserving consistency, in a multi-agent system it also has to deal with possible conflicts between the agents perspectives. To provide an adequate framework, each agent, built as a combination of an assumption based belief revision system and a cooperation layer, was enriched with additional features: a distributed search control mechanism allowing dynamic context management, and a set of different distributed consistency methodologies. As a result, a Distributed Belief Revision Testbed (DiBeRT) was developed. This paper is a preliminary report presenting some of DiBeRT contributions: a concise representation of external beliefs; a simple and innovative methodology to achieve distributed context management; and a reduced inter-agent data exchange format.

Beliefs and Conflicts in a Real World Multi-Agent System

In a real world multiagent system, where the agents are faced with partial, incomplete and intrinsically dynamic knowledge, conflicts are inevitable. Frequently, different agents have goals or beliefs that cannot hold simultaneously. Conflict resolution methodologies have to be adopted to overcome such undesirable occurrences. In this paper we investigate the application of distributed belief revision techniques as the support for conflict resolution in the analysis of the validity of the candidate beams to be produced in the CERN particle accelerators. This CERN multiagent system contains a higher hierarchy agent, the Specialist agent, which makes use of meta-knowledge (on how the con- flicting beliefs have been produced by the other agents) in order to detect which beliefs should be abandoned. Upon solving a conflict, the Specialist instructs the involved agents to revise their beliefs accordingly. Conflicts in the problem domain are mapped into conflicting beliefs of the distributed belief revision system, where they can be handled by proven formal methods. This technique builds on well established concepts and combines them in a new way to solve important problems. We find this approach generally applicable in several domains.

Multi-agent system for distributed manufacturing scheduling with genetic algorithms and tabu search

Computerized scheduling methods and computerized scheduling systems according to exemplary embodiments. A computerized scheduling method may be stored in a memory and executed on one or more processors. The method may include defining a main multi-machine scheduling problem as a plurality of single machine scheduling problems; independently solving the plurality of single machine scheduling problems thereby calculating a plurality of near optimal single machine scheduling problem solutions; integrating the plurality of near optimal single machine scheduling problem solutions into a main multi-machine scheduling problem solution; and outputting the main multi-machine scheduling problem solution.

Multi-elemental analysis of ready-to-eat “baby leaf” vegetables using microwave digestion and high-resolution continuum source atomic absorption spectrometry

The mineral content (phosphorous (P), potassium (K), sodium (Na), calcium (Ca), magnesium (Mg), iron (Fe), manganese (Mn), zinc (Zn) and copper (Cu)) of eight ready-to-eat baby leaf vegetables was determined. The samples were subjected to microwave-assisted digestion and the minerals were quantified by High-Resolution Continuum Source Atomic Absorption Spectrometry (HR-CS-AAS) with flame and electrothermal atomisation. The methods were optimised and validated producing low LOQs, good repeatability and linearity, and recoveries, ranging from 91% to 110% for the minerals analysed. Phosphorous was determined by a standard colorimetric method. The accuracy of the method was checked by analysing a certified reference material; results were in agreement with the quantified value. The samples had a high content of potassium and calcium, but the principal mineral was iron. The mineral content was stable during storage and baby leaf vegetables could represent a good source of minerals in a balanced diet. A linear discriminant analysis was performed to compare the mineral profile obtained and showed, as expected, that the mineral content was similar between samples from the same family. The Linear Discriminant Analysis was able to discriminate different samples based on their mineral profile.

Calibração automática para sistema de localização Multi-câmara

Mestrado em Engenharia Electrotécnica e de Computadores - Sistemas Autónomos

Aplicação do multi-layer stream mapping para a gestão da eficiência de recursos e produtividade de um sistema de produção

Mestrado em Engenharia Mecânica – Especialização Gestão Industrial

Multi-objective Parallel Particle Swarm Optimization for Day-ahead Vehicle-To-Grid Scheduling

This paper presents a methodology for multi-objective day-ahead energy resource scheduling for smart grids considering intensive use of distributed generation and Vehicle- To-Grid (V2G). The main focus is the application of weighted Pareto to a multi-objective parallel particle swarm approach aiming to solve the dual-objective V2G scheduling: minimizing total operation costs and maximizing V2G income. A realistic mathematical formulation, considering the network constraints and V2G charging and discharging efficiencies is presented and parallel computing is applied to the Pareto weights. AC power flow calculation is included in the metaheuristics approach to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method.

Scenarios Generation for Multi-Agent simulation of Electricity Markets based on Intelligent Data Analysis

This document presents a tool able to automatically gather data provided by real energy markets and to generate scenarios, capture and improve market players’ profiles and strategies by using knowledge discovery processes in databases supported by artificial intelligence techniques, data mining algorithms and machine learning methods. It provides the means for generating scenarios with different dimensions and characteristics, ensuring the representation of real and adapted markets, and their participating entities. The scenarios generator module enhances the MASCEM (Multi-Agent Simulator of Competitive Electricity Markets) simulator, endowing a more effective tool for decision support. The achievements from the implementation of the proposed module enables researchers and electricity markets’ participating entities to analyze data, create real scenarios and make experiments with them. On the other hand, applying knowledge discovery techniques to real data also allows the improvement of MASCEM agents’ profiles and strategies resulting in a better representation of real market players’ behavior. This work aims to improve the comprehension of electricity markets and the interactions among the involved entities through adequate multi-agent simulation.

Intelligent Micro Grid Management using a Multi-Agent approach

Recent changes in electricity markets (EMs) have been potentiating the globalization of distributed generation. With distributed generation the number of players acting in the EMs and connected to the main grid has grown, increasing the market complexity. Multi-agent simulation arises as an interesting way of analysing players’ behaviour and interactions, namely coalitions of players, as well as their effects on the market. MASCEM was developed to allow studying the market operation of several different players and MASGriP is being developed to allow the simulation of the micro and smart grid concepts in very different scenarios This paper presents a methodology based on artificial intelligence techniques (AI) for the management of a micro grid. The use of fuzzy logic is proposed for the analysis of the agent consumption elasticity, while a case based reasoning, used to predict agents’ reaction to price changes, is an interesting tool for the micro grid operator.

Multi-Agent Based Smart Grid Management and Simulation: Situation Awareness and Learning in a Test Bed with Simulated and Real Installations and Players

The rising usage of distributed energy resources has been creating several problems in power systems operation. Virtual Power Players arise as a solution for the management of such resources. Additionally, approaching the main network as a series of subsystems gives birth to the concepts of smart grid and micro grid. Simulation, particularly based on multi-agent technology is suitable to model all these new and evolving concepts. MASGriP (Multi-Agent Smart Grid simulation Platform) is a system that was developed to allow deep studies of the mentioned concepts. This paper focuses on a laboratorial test bed which represents a house managed by a MASGriP player. This player is able to control a real installation, responding to requests sent by the system operators and reacting to observed events depending on the context.

Upper Ontology for Multi-Agent Energy Systems’ Applications

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors research group has developed three multi-agent systems: MASCEM, which simulates the electricity markets; ALBidS that works as a decision support system for market players; and MASGriP, which simulates the internal operations of smart grids. To take better advantage of these systems, their integration is mandatory. For this reason, is proposed the development of an upper-ontology which allows an easier cooperation and adequate communication between them. Additionally, the concepts and rules defined by this ontology can be expanded and complemented by the needs of other simulation and real systems in the same areas as the mentioned systems. Each system’s particular ontology must be extended from this top-level ontology.