Modelling the Provenance of Data in Autonomous Systems

Determining the provenance of data, i.e. the process that led to that data, is vital in many disciplines. For example, in science, the process that produced a given result must be demonstrably rigorous for the result to be deemed reliable. A provenance system supports applications in recording adequate documentation about process executions to answer queries regarding provenance, and provides functionality to perform those queries. Several provenance systems are being developed, but all focus on systems in which the components are textitreactive, for example Web Services that act on the basis of a request, job submission system, etc. This limitation means that questions regarding the motives of autonomous actors, or textitagents, in such systems remain unanswerable in the general case. Such questions include: who was ultimately responsible for a given effect, what was their reason for initiating the process and does the effect of a process match what was intended to occur by those initiating the process? In this paper, we address this limitation by integrating two solutions: a generic, re-usable framework for representing the provenance of data in service-oriented architectures and a model for describing the goal-oriented delegation and engagement of agents in multi-agent systems. Using these solutions, we present algorithms to answer common questions regarding responsibility and success of a process and evaluate the approach with a simulated healthcare example.

Modelling and Administration of Contract-Based Systems

Mirroring the paper versions exchanged between businesses today, electronic contracts offer the possibility of dynamic, automatic creation and enforcement of restrictions and compulsions on agent behaviour that are designed to ensure business objectives are met. However, where there are many contracts within a particular application, it can be difficult to determine whether the system can reliably fulfil them all; computer-parsable electronic contracts may allow such verification to be automated. In this paper, we describe a conceptual framework and architecture specification in which normative business contracts can be electronically represented, verified, established, renewed, etc. In particular, we aim to allow systems containing multiple contracts to be checked for conflicts and violations of business objectives. We illustrate the framework and architecture with an aerospace example.

Aplicação de sistemas multi-classificadores no diagnóstico de falhas em motores de indução trifásicos

Equipment maintenance is the major cost factor in industrial plants, it is very important the development of fault predict techniques. Three-phase induction motors are key electrical equipments used in industrial applications mainly because presents low cost and large robustness, however, it isn t protected from other fault types such as shorted winding and broken bars. Several acquisition ways, processing and signal analysis are applied to improve its diagnosis. More efficient techniques use current sensors and its signature analysis. In this dissertation, starting of these sensors, it is to make signal analysis through Park s vector that provides a good visualization capability. Faults data acquisition is an arduous task; in this way, it is developed a methodology for data base construction. Park s transformer is applied into stationary reference for machine modeling of the machine s differential equations solution. Faults detection needs a detailed analysis of variables and its influences that becomes the diagnosis more complex. The tasks of pattern recognition allow that systems are automatically generated, based in patterns and data concepts, in the majority cases undetectable for specialists, helping decision tasks. Classifiers algorithms with diverse learning paradigms: k-Neighborhood, Neural Networks, Decision Trees and Naïves Bayes are used to patterns recognition of machines faults. Multi-classifier systems are used to improve classification errors. It inspected the algorithms homogeneous: Bagging and Boosting and heterogeneous: Vote, Stacking and Stacking C. Results present the effectiveness of constructed model to faults modeling, such as the possibility of using multi-classifiers algorithm on faults classification

Experience generalization for multi-agent reinforcement learning

On-line learning methods have been applied successfully in multi-agent systems to achieve coordination among agents. Learning in multi-agent systems implies in a non-stationary scenario perceived by the agents, since the behavior of other agents may change as they simultaneously learn how to improve their actions. Non-stationary scenarios can be modeled as Markov Games, which can be solved using the Minimax-Q algorithm a combination of Q-learning (a Reinforcement Learning (RL) algorithm which directly learns an optimal control policy) and the Minimax algorithm. However, finding optimal control policies using any RL algorithm (Q-learning and Minimax-Q included) can be very time consuming. Trying to improve the learning time of Q-learning, we considered the QS-algorithm. in which a single experience can update more than a single action value by using a spreading function. In this paper, we contribute a Minimax-QS algorithm which combines the Minimax-Q algorithm and the QS-algorithm. We conduct a series of empirical evaluation of the algorithm in a simplified simulator of the soccer domain. We show that even using a very simple domain-dependent spreading function, the performance of the learning algorithm can be improved.

Utilizando Pesos estáticos e dinâmicos em sistemas multi-classificadores com diferentes níveis de diversidade

Although some individual techniques of supervised Machine Learning (ML), also known as classifiers, or algorithms of classification, to supply solutions that, most of the time, are considered efficient, have experimental results gotten with the use of large sets of pattern and/or that they have a expressive amount of irrelevant data or incomplete characteristic, that show a decrease in the efficiency of the precision of these techniques. In other words, such techniques can t do an recognition of patterns of an efficient form in complex problems. With the intention to get better performance and efficiency of these ML techniques, were thought about the idea to using some types of LM algorithms work jointly, thus origin to the term Multi-Classifier System (MCS). The MCS s presents, as component, different of LM algorithms, called of base classifiers, and realized a combination of results gotten for these algorithms to reach the final result. So that the MCS has a better performance that the base classifiers, the results gotten for each base classifier must present an certain diversity, in other words, a difference between the results gotten for each classifier that compose the system. It can be said that it does not make signification to have MCS s whose base classifiers have identical answers to the sames patterns. Although the MCS s present better results that the individually systems, has always the search to improve the results gotten for this type of system. Aim at this improvement and a better consistency in the results, as well as a larger diversity of the classifiers of a MCS, comes being recently searched methodologies that present as characteristic the use of weights, or confidence values. These weights can describe the importance that certain classifier supplied when associating with each pattern to a determined class. These weights still are used, in associate with the exits of the classifiers, during the process of recognition (use) of the MCS s. Exist different ways of calculating these weights and can be divided in two categories: the static weights and the dynamic weights. The first category of weights is characterizes for not having the modification of its values during the classification process, different it occurs with the second category, where the values suffers modifications during the classification process. In this work an analysis will be made to verify if the use of the weights, statics as much as dynamics, they can increase the perfomance of the MCS s in comparison with the individually systems. Moreover, will be made an analysis in the diversity gotten for the MCS s, for this mode verify if it has some relation between the use of the weights in the MCS s with different levels of diversity

Multi-Agent Framework for Spatial Load Forecasting

A multi-agent framework for spatial electric load forecasting, especially suited to simulate the different dynamics involved on distribution systems, is presented. The service zone is divided into several sub-zones, each subzone is considered as an independent agent identified with a corresponding load level, and their relationships with the neighbor zones are represented as development probabilities. With this setting, different kind of agents can be developed to simulate the growth pattern of the loads in distribution systems. This paper presents two different kinds of agents to simulate different situations, presenting some promissory results.

A multi-agent system with a percolation approach to simulate the driving pattern of Plug-In ELectric Vehicles

A multi-agent system with a percolation approach to simulate the driving pattern of Plug-In Electric Vehicle (PEV), especially suited to simulate the PEVs behavior on any distribution systems, is presented. This tool intends to complement information about the driving patterns database on systems where that kind of information is not available. So, this paper aims to provide a framework that is able to work with any kind of technology and load generated of PEVs. The service zone is divided into several sub-zones, each subzone is considered as an independent agent identified with corresponding load level, and their relationships with the neighboring zones are represented as network probabilities. A percolation approach is used to characterize the autonomy of the battery of the PVEs to move through the city. The methodology is tested with data from a mid-size city real distribution system. The result shows the sub-area where the battery of PEVs will need to be recharge and gives the planners of distribution systems the necessary input for a medium to long term network planning in a smart grid environment. © 2012 IEEE.

Planejamento e despacho ótimo de reativos (VAr) em sistemas de energia elétrica multi-áreas

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A multi-objective adaptive immune algorithm for multi-application NoC mapping

Current SoC design trends are characterized by the integration of larger amount of IPs targeting a wide range of application fields. Such multi-application systems are constrained by a set of requirements. In such scenario network-on-chips (NoC) are becoming more important as the on-chip communication structure. Designing an optimal NoC for satisfying the requirements of each individual application requires the specification of a large set of configuration parameters leading to a wide solution space. It has been shown that IP mapping is one of the most critical parameters in NoC design, strongly influencing the SoC performance. IP mapping has been solved for single application systems using single and multi-objective optimization algorithms. In this paper we propose the use of a multi-objective adaptive immune algorithm (M(2)AIA), an evolutionary approach to solve the multi-application NoC mapping problem. Latency and power consumption were adopted as the target multi-objective functions. To compare the efficiency of our approach, our results are compared with those of the genetic and branch and bound multi-objective mapping algorithms. We tested 11 well-known benchmarks, including random and real applications, and combines up to 8 applications at the same SoC. The experimental results showed that the M(2)AIA decreases in average the power consumption and the latency 27.3 and 42.1 % compared to the branch and bound approach and 29.3 and 36.1 % over the genetic approach.

Meta-models, environment and layers: agent-oriented engineering of complex systems

Traditional software engineering approaches and metaphors fall short when applied to areas of growing relevance such as electronic commerce, enterprise resource planning, and mobile computing: such areas, in fact, generally call for open architectures that may evolve dynamically over time so as to accommodate new components and meet new requirements. This is probably one of the main reasons that the agent metaphor and the agent-oriented paradigm are gaining momentum in these areas. This thesis deals with the engineering of complex software systems in terms of the agent paradigm. This paradigm is based on the notions of agent and systems of interacting agents as fundamental abstractions for designing, developing and managing at runtime typically distributed software systems. However, today the engineer often works with technologies that do not support the abstractions used in the design of the systems. For this reason the research on methodologies becomes the basic point in the scientific activity. Currently most agent-oriented methodologies are supported by small teams of academic researchers, and as a result, most of them are in an early stage and still in the first context of mostly \academic" approaches for agent-oriented systems development. Moreover, such methodologies are not well documented and very often defined and presented only by focusing on specific aspects of the methodology. The role played by meta- models becomes fundamental for comparing and evaluating the methodologies. In fact a meta-model specifies the concepts, rules and relationships used to define methodologies. Although it is possible to describe a methodology without an explicit meta-model, formalising the underpinning ideas of the methodology in question is valuable when checking its consistency or planning extensions or modifications. A good meta-model must address all the different aspects of a methodology, i.e. the process to be followed, the work products to be generated and those responsible for making all this happen. In turn, specifying the work products that must be developed implies dening the basic modelling building blocks from which they are built. As a building block, the agent abstraction alone is not enough to fully model all the aspects related to multi-agent systems in a natural way. In particular, different perspectives exist on the role that environment plays within agent systems: however, it is clear at least that all non-agent elements of a multi-agent system are typically considered to be part of the multi-agent system environment. The key role of environment as a first-class abstraction in the engineering of multi-agent system is today generally acknowledged in the multi-agent system community, so environment should be explicitly accounted for in the engineering of multi-agent system, working as a new design dimension for agent-oriented methodologies. At least two main ingredients shape the environment: environment abstractions - entities of the environment encapsulating some functions -, and topology abstractions - entities of environment that represent the (either logical or physical) spatial structure. In addition, the engineering of non-trivial multi-agent systems requires principles and mechanisms for supporting the management of the system representation complexity. These principles lead to the adoption of a multi-layered description, which could be used by designers to provide different levels of abstraction over multi-agent systems. The research in these fields has lead to the formulation of a new version of the SODA methodology where environment abstractions and layering principles are exploited for en- gineering multi-agent systems.

Self-organising coordination systems

Many research fields are pushing the engineering of large-scale, mobile, and open systems towards the adoption of techniques inspired by self-organisation: pervasive computing, but also distributed artificial intelligence, multi-agent systems, social networks, peer-topeer and grid architectures exploit adaptive techniques to make global system properties emerge in spite of the unpredictability of interactions and behaviour. Such a trend is visible also in coordination models and languages, whenever a coordination infrastructure needs to cope with managing interactions in highly dynamic and unpredictable environments. As a consequence, self-organisation can be regarded as a feasible metaphor to define a radically new conceptual coordination framework. The resulting framework defines a novel coordination paradigm, called self-organising coordination, based on the idea of spreading coordination media over the network, and charge them with services to manage interactions based on local criteria, resulting in the emergence of desired and fruitful global coordination properties of the system. Features like topology, locality, time-reactiveness, and stochastic behaviour play a key role in both the definition of such a conceptual framework and the consequent development of self-organising coordination services. According to this framework, the thesis presents several self-organising coordination techniques developed during the PhD course, mainly concerning data distribution in tuplespace-based coordination systems. Some of these techniques have been also implemented in ReSpecT, a coordination language for tuple spaces, based on logic tuples and reactions to events occurring in a tuple space. In addition, the key role played by simulation and formal verification has been investigated, leading to analysing how automatic verification techniques like probabilistic model checking can be exploited in order to formally prove the emergence of desired behaviours when dealing with coordination approaches based on self-organisation. To this end, a concrete case study is presented and discussed.