Repeated games are an important mathematical formalism to model and study long-term economic interactions between multiple self-interested parties (individuals or groups of individuals). They open attractive perspectives in modeling long-term multiagent interactions. This overview paper discusses the most important results that actually exist for repeated games. These results arise from both economics and computer science. Contrary to a number of existing surveys of repeated games, most of which originated from the economic research community, we are first to pay a special attention to a number of important distinctive features proper to artificial agents. More precisely, artificial agents, as opposed to the human agents mainly aimed by the economic research, are usually bounded whether in terms of memory or performance. Therefore, their decisions have to be based on the strategies defined using finite representations. Furthermore, these strategies have to be efficiently computed or approximated using a limited computational resource usually available to artificial agents.

Distributed systems are populated by a large number of heterogeneous entities that join and leave the systems dynamically. These entities act as clients and providers and interact with each other in order to get a resource or to achieve a goal. To facilitate the collaboration between entities, the system should provide mechanisms to manage the information about which entities or resources are available in the system at a certain moment, as well as how to locate them in an efficient way. However, this is not an easy task in open and dynamic environments where there are changes in the available resources and global information is not always available. In this paper, we present a comprehensive vision of search in distributed environments. This review not only considers the approaches of the peer-to-peer area, but also the approaches from three more areas: service-oriented environments, multi-agent systems, and complex networks. In these areas, the search for resources, services, or entities plays a key role for the proper performance of the systems built on them. The aim of this analysis is to compare approaches from these areas taking into account the underlying system structure and the algorithms or strategies that participate in the search process.

In the last decades, most works in the literature have been devoted to study argumentation formalisms that focus on a defeat relation among arguments. Recently, the study of a support relation between arguments regained attention among researchers; the bulk of the research has been centered on the study of support within the context of abstract argumentation by considering support as an explicit interaction between arguments. However, there exist other approaches that take support into account in a different setting. This article surveys several interpretations of the notion of support as proposed in the literature, such as deductive support, necessary support, evidential support, subargument, and backing, among others. The aim is to provide a comprehensive study where similarities and differences among these interpretations are highlighted, as well as discuss how they are addressed by different argumentation formalisms.

There is an increasing trend in moving desktop applications to web browsers, even when the web server is running on the same desktop machine. In this paper, we go further in this direction and show how to combine a web server, a web application framework (enhanced to support desktop-like Model–View–Controller interaction) and a context-aware architecture to develop web-based mobile context-aware applications. By using this approach we take advantage of the well-established web paradigm to design the graphical user interfaces (GUIs) and the inherent ability of the web to mash up applications with external components (such as Google Maps). On top of that, since the web server runs on the device itself, the application can access local resources (such as disk space or sensing devices, which are indispensable for context-aware systems) avoiding the sandbox model of the web browsers. To illustrate our approach we show how a mobile hypermedia system has been built on top of our platform.

The combination of multiple classifiers, commonly referred to as a classifier ensemble, has previously demonstrated the ability to improve classification accuracy in many application domains. As a result this area has attracted significant amount of research in recent years. The aim of this paper has therefore been to provide a state of the art review of the most well-known ensemble techniques with the main focus on bagging, boosting and stacking and to trace the recent attempts, which have been made to improve their performance. Within this paper, we present and compare an updated view on the different modifications of these techniques, which have specifically aimed to address some of the drawbacks of these methods namely the low diversity problem in bagging or the over-fitting problem in boosting. In addition, we provide a review of different ensemble selection methods based on both static and dynamic approaches. We present some new directions which have been adopted in the area of classifier ensembles from a range of recently published studies. In order to provide a deeper insight into the ensembles themselves a range of existing theoretical studies have been reviewed in the paper.

Privacy has been a concern for humans long before the explosive growth of the Internet. The advances in information technologies have further increased these concerns. This is because the increasing power and sophistication of computer applications offers both tremendous opportunities for individuals, but also significant threats to personal privacy. Autonomous agents and multi-agent systems are examples of the level of sophistication of computer applications. Autonomous agents usually encapsulate personal information describing their principals, and therefore they play a crucial role in preserving privacy. Moreover, autonomous agents themselves can be used to increase the privacy of computer applications by taking advantage of the intrinsic features they provide, such as artificial intelligence, pro-activeness, autonomy, and the like. This article introduces the problem of preserving privacy in computer applications and its relation to autonomous agents and multi-agent systems. It also surveys privacy-related studies in the field of multi-agent systems and identifies open challenges to be addressed by future research.

The emerging Grid is extending the scope of resources to mobile devices and sensors that are connected through loosely connected networks. Nowadays, the number of mobile device users is increasing dramatically and the mobile devices provide various capabilities such as location awareness that are not normally incorporated in fixed Grid resources. Nevertheless, mobile devices exhibit inferior characteristics such as poor performance, limited battery life, and unreliable communication, compared with fixed Grid resources. Especially, the intermittent disconnection from network owing to users’ movements adversely affects performance, and this characteristic makes it inefficient and troublesome to adopt the synchronous message delivery in mobile Grid. This paper presents a mobile Grid system architecture based on mobile agents that support the location management and the asynchronous message delivery in a multi-domain proxy environment. We propose a novel balanced scheduling algorithm that takes users’ mobility into account in scheduling. We analyzed users mobility patterns to quantitatively measure the resource availability, which is classified into three types: full availability, partial availability, and unavailability. We also propose an adaptive load-balancing technique by classifying mobile devices into nine groups depending on availability and by utilizing adaptability based on the multi-level feedback queue to handle the job type change. The experimental results show that our scheduling algorithm provides a superior performance in terms of execution times to the one without considering mobility and adaptive load-balancing.

Agreement Technologies are needed for autonomous agents to come to mutually acceptable agreements, typically on behalf of humans. These technologies include trust computing, negotiation, argumentation and semantic alignment. In this paper, we identify a number of open questions regarding the integration of computational models and tools for trust computing with negotiation, argumentation and semantic alignment. We consider these questions in general and in the context of applications in open, distributed settings such as the grid and cloud computing.

In theories and models of computational intelligence, cognition and action have historically been investigated on separate grounds. We conjecture that the main mechanism of case-based reasoning (CBR) applies to cognitive tasks at various levels and of various granularity, and hence can represent a bridge—or a continuum—between the higher and lower levels of cognition. CBR is an artificial intelligence (AI) method that draws upon the idea of solving a new problem reusing similar past experiences. In this paper, we re-formulate the notion of CBR to highlight the commonalities between higher-level cognitive tasks such as diagnosis, and lower-level control such as voluntary movements of an arm. In this view, CBR is envisaged as a generic process independent from the content and the detailed format of cases. Diagnostic cases and internal representations underlying motor control constitute two instantiations of the case representation. In order to claim such a generic mechanism, the account of CBR needs to be revised so that its position in non-symbolic AI becomes clearer. The paper reviews the CBR literature that targets lower levels of cognition to show how CBR may be considered as a step toward bridging the gap between symbolic and non-symbolic AI.

System adaptivity is increasingly demanded in high-performance embedded systems, particularly in multimedia system-on-chip (SoC), owing to growing quality-of-service requirements. This paper presents a reactive control model that has been introduced in Gaspard, our framework dedicated to SoC hardware/software co-design. This model aims at expressing adaptivity as well as reconfigurability in systems performing data-intensive computations. It is generic enough to be used for description in the different parts of an embedded system, for example, specification of how different data-intensive algorithms can be chosen according to some computation modes at the functional level; and expression of how hardware components can be selected via the usage of a library of intellectual properties according to execution performances. The transformation of this model toward synchronous languages is also presented, in order to allow an automatic code generation usable for formal verification, based on techniques such as model checking and controller synthesis, as illustrated in the paper. This work, based on Model-Driven Engineering and the standard UML MARTE profile, has been implemented in Gaspard.

One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper, we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.

The collection and analysis of observational and experimental data represent the main tools for assessing the presence, the extent, the nature, and the trend of discrimination phenomena. Data analysis techniques have been proposed in the last 50 years in the economic, legal, statistical, and, recently, in the data mining literature. This is not surprising, since discrimination analysis is a multidisciplinary problem, involving sociological causes, legal argumentations, economic models, statistical techniques, and computational issues. The objective of this survey is to provide a guidance and a glue for researchers and anti-discrimination data analysts on concepts, problems, application areas, datasets, methods, and approaches from a multidisciplinary perspective. We organize the approaches according to their method of data collection as observational, quasi-experimental, and experimental studies. A fourth line of recently blooming research on knowledge discovery based methods is also covered. Observational methods are further categorized on the basis of their application context: labor economics, social profiling, consumer markets, and others.

Limitations of sensors and the situation of a specific measurement can affect the quality of context information that is implicitly collected in pervasive environments. The lack of information about Quality of Context (QoC) can result in degraded performance of context-aware systems in pervasive environments, without knowing the actual problem. Context-aware systems can take advantage of QoC if context producers also provide QoC metrics along with context information. In this paper, we analyze QoC and present our model for processing QoC metrics. We evaluate QoC metrics considering the capabilities of sensors, circumstances of specific measurement, requirements of context consumer, and the situation of the use of context information. We also illustrate how QoC metrics can facilitate in enhancing the effectiveness and efficiency of different tasks performed by a system to provide context information in pervasive environments.

Bagging and boosting are two of the most well-known ensemble learning methods due to their theoretical performance guarantees and strong experimental results. Since bagging and boosting are an effective and open framework, several researchers have proposed their variants, some of which have turned out to have lower classification error than the original versions. This paper tried to summarize these variants and categorize them into groups. We hope that the references cited cover the major theoretical issues, and provide access to the main branches of the literature dealing with such methods, guiding the researcher in interesting research directions.

In the last few years, the number of papers devoted to applications of agent-based technologies to traffic and transportation engineering has grown enormously. Thus, it seems to be the appropriate time to shed light over the achievements of the last decade, on the questions that have been successfully addressed, as well as on remaining challenging issues. In the present paper, we review the literature related to the areas of agent-based traffic modelling and simulation, and agent-based traffic control and management. Later we discuss and summarize the main achievements and the challenges.

In order to be able to specify and design ubiquitous applications, it is necessary to rely on a precise and formal representation of context. Aiming at reducing the gap between the formal specification and the design of a concrete ubiquitous application, we have proposed an approach to specify a context-aware system at three levels of abstraction. This specification uses the Algebra of Contextualized Ontologies, which is based on Category Theory and takes contextualization as a basic notion, proposing a small set of simple and powerful operations to compose and decompose contextualized ontologies. In this paper, we present a revised version of the algebra and algorithms to compute the algebraic operations. We exemplify our approach along with a concrete ubiquitous computing scenario. Moreover, by taking advantage of Category Theory foundations, we show that it is possible to derive the algorithms for each algebraic operation from simple modifications of a single basic algorithm.

WiMAX was proposed as the new wireless network standard in recent years. In the wide cover area of WiMAX environment, there are many nodes and interference. In order to obtain the efficiency, the avoidance to the interference is important. In this paper, we propose a two-tier cluster-based route scheduling mechanism on WiMAX Mesh mode. It achieves both fairness and efficiency inside and outside the cluster. The simulation result shows that our mechanism provides great improvement in average delay time and throughput than other research.