Industrial application integration using the unification approach to agent-enabled semantic SOA

Within the enterprise, information infrastructure as the mean to bring together different software applications is the key component to enable cooperation and information and knowledge exchange in an open-distributed environment. The paper addresses the issues of industrial application integration in business processes using agent-enabled service-oriented architectures (SOA). In this paper, we show that agent-enabled SOA can play an important role for service integration. Our framework combines Web services (WS) and intelligent agent technologies orchestrated by a business process management system. This framework looks for unification of agent and WS service models, is grounded in a semantic SOA of an agent platform and is supported by the Component Agent Platform Over .NET Framework agent platform tools. We describe the architecture and illustrate the approach by an industrial application scenario from petroleum wells’ drilling.     

Mole – Concepts of a mobile agent system

Due to its salient properties, mobile agent technology has received a rapidly growing attention over the last few years. Many developments of mobile agent systems are under way in both academic and industrial environments. In addition, there are already various efforts to standardize mobile agent facilities and architectures. Mole is the first mobile agent system that has been developed in the Java language. The first version was finished in 1995, and since then Mole has been constantly improved. Mole provides a stable environment for the development and usage of mobile agents in the area of distributed applications. In this paper we describe the basic concepts of a mobile agent system, i.e., mobility, communication and security, discuss different implementation techniques, present the decisions made in Mole and give an overview of the system services implemented in Mole.     

Inside an agent

When we discuss agent-based system construction with software developers or ask students to implement common agent architectures using object-oriented techniques, we find that it is not trivial for them to create an elegant system design from the standard presentation of these architectures in textbooks or research papers. To better communicate our interpretation of popular agent architectures, we draw UML (Unified Modeling Language) diagrams to guide an implementer's design. However, before we describe these diagrams, we need to review some basic features of agents. The paper considers an architecture showing a simple agent interacting with an environment. The agent senses its environment, uses what it senses to choose an action, and then performs the action through its effectors. Sensory input can include received messages, and action can be the sending of messages. To construct an agent, we need a more detailed understanding of how it functions. In particular, if we are to build one using conventional object-oriented analysis and design techniques, we should know in what ways an agent is more than just a simple object     

Multidimensional characterization of evolutionary clusters: An experience report

Context: Software architects try to decompose software systems such that their constituent parts can evolve independently from one another. During the actual evolution, identifying groups of software entities from different parts which frequently changed together, is one way to help architects evaluate how independently the different parts can evolve. However, there may be many such groups from which the architects have to select the ones denoting issues in the decomposition worth addressing.ObjectiveIn this paper, we use a number of properties of those groups of entities, such as whether they involve more than one site, how many entities are involved, how often these entities are changed, and so on, to identify a subset of groups indicating issues the architect does want to address.MethodWe describe (1) a number of properties of groups of co-changing entities, (2) scenarios expressing issues to be addressed, in terms of values for the different properties, and (3) the mapping of such scenarios to queries on a set of groups of software entities that changed together. Executing that query results in a subset pointing to issues worth addressing according to that scenario.ResultsWe apply our method to a large embedded software system having a development history of more than a decade. For several scenarios worked out, the number of issues selected for further investigation by the architect is less than half the number of issues selected using only one or two properties.ConclusionOur experience suggests that using multiple properties of groups of co-changing entities is a useful way to accurately identify the set of issues an architect has to address when improving the decomposition of a system.     

A multiagent network for peer norm enforcement

In a multiagent system where norms are used to regulate the actions agents ought to execute, some agents may decide not to abide by the norms if this can benefit them. Norm enforcement mechanisms are designed to counteract these benefits and thus the motives for not abiding by the norms. In this work we propose a distributed mechanism through which agents in the multiagent system that do not abide by the norms can be ostracised by their peers. An ostracised agent cannot interact anymore and looses all benefits from future interactions. We describe a model for multiagent systems structured as networks of agents, and a behavioural model for the agents in such systems. Furthermore, we provide analytical results which show that there exists an upper bound to the number of potential norm violations when all the agents exhibit certain behaviours. We also provide experimental results showing that both stricter enforcement behaviours and larger percentage of agents exhibiting these behaviours reduce the number of norm violations, and that the network topology influences the number of norm violations. These experiments have been executed under varying scenarios with different values for the number of agents, percentage of enforcers, percentage of violators, network topology, and agent behaviours. Finally, we give examples of applications where the enforcement techniques we provide could be used.     

Can a rational agent afford to be affectless? a formal approach

In this article, we expose some of the issues raised by the critics of the neoclassical approach to rational agent modeling and we propose a formal approach for the design of artificial rational agents that includes some of the functions of emotions found in the human system. We suggest that emotions and rationality are closely linked in the human mind (and in the body, for that matter) and, therefore, need to be included in architectures for designing rational artificial agents, whether these agents are to interact with humans, to model humans' behaviors and actions, or both. We describe an Affective Knowledge Representation (AKR) scheme to represent emotion schemata, which we developed to guide the design of a variety of socially intelligent artificial agents. Our approach focuses on the notion of "social expertise" of socially intelligent agents in terms of their external behavior and internal motivational goal-based abilities. AKR, which uses probabilistic frames, is derived from combining multiple emotion theories into a hierarchical model of affective phenomena useful for artificial agent design. AKR includes a taxonomy of affect, mood, emotion, and personality, and a framework for emotional state dynamics using probabilistic Markov Models.     

Q: a scenario description language for interactive agents

Agent internal mechanisms form the basis for many of the languages proposed for describing agent behavior and interagent protocols. For the Web, however, we should also consider the needs of application designers such as sales managers, travel agents, and schoolteachers. To this end, the author is developing Q, a language for describing interaction scenarios between agents and users based on agent external roles. Q also provides an interface between computing professionals and scenario writers. Rather than depending on agent internal mechanisms, Q seeks to describe how scenario writers should request that agents behave. Scenarios help establish a bridge between the computing professionals who design agents and the scenario writers who design applications. Thus, we can expect an effective dialog to emerge from the interplay between the two different perspectives during the process of formalizing interaction patterns.     

Evaluation of Flow-Aware Networking (FAN) architectures under GridFTP traffic

QoS in grid networks is a very important issue. In this paper, we compare two scheduling algorithms of the Flow Aware Networking (FAN) architecture under Grid traffic. The metrics used to compare both serve to allocate the bandwidth. Additionally, we provide the comparison of one of the FAN architectures against DiffServ and under Grid traffic. Metrics studied here are average goodput and average delay. Finally, we provide a survey of the most important IP-based QoS architectures for Grid networks. We assume that Grid traffic is only composed by GridFTP sessions that arrive following a stationary Poisson process. We conclude that FAN based on the Priority Fair Queuing (PFQ) scheduling algorithm gets the better performance and that FAN architectures perform better than Diffserv.     

Modeling software architectures with goals in virtual university environment

Recently, the coupling of goal-based and user-centered approaches has resulted in a tremendous impact on the research of software engineering. However, there is no systematic way in the extant approaches to handling the effects of requirements on the structuring of software architectures. As an attempt towards the investigation of the interactions among goals, scenarios, and software architectures, we proposed, in this paper, a goal-driven architecture trade-off analysis method to analyze and construct software architectures in an incremental manner. We also identified criteria for architecture evaluation and verification and explored the possible types of realization of software architectures for goals. The proposed approach is illustrated using the problem domain of virtual university environment.     

A collaborative food safety service agent architecture with alerts and trust

With the recent advances in Internet and mobile technologies, there are increasing demands for ubiquitous access to food safety information for service integration and gathering first hand information. However, due to disparate food trading information among different food suppliers throughout the food supply chain such as food importers, food wholesalers, food retailers, it is still difficult for citizens to use them effectively during their marketplace shopping. To overcome this problem, we propose a Collaborative Food Safety Agent System (CFSAS) based on a scalable, flexible, and intelligent Multi-Agent Information System (MAIS) architecture for proactive aids and trust-based decision support on food purchasing to citizens. We formulate our MAIS architecture for CFSAS further with agent clusters based on a case study of the Center for Food Safety (CFS) in Hong Kong. Agent clusters may comprise several types of agents to achieve the goals involved in the major processes of a food safety mechanism. We show how agents help citizens better plan, understand, and specify their preferences collaboratively with the CFSAS. We further illustrate how this can be implemented with Web service technologies to integrate disparate food information resources along the food supply chain.     

交互式智能体体系结构应用研究

介绍和分析了目前关于智能体体系结构的研究,重点以无人作战武器防御系统为背景,深入研究交互式智能体体系结构的应用,并对智能体的协调机制、通信机制、不完全全局规划方法等作了较深入的阐述。     

A Context/Communication Information Agent

We are developing a Context/Communication Information Agent (CIA), an autonomous software agent that proactively searches for the right information at the right time. Our goal is to design and evaluate a system that leverages what people naturally do, using this knowledge to retrieve information, and presenting it with a minimal cost of disruption to the users. In this paper, we describe the results a low-fidelity prototype performed in a meeting situation, the design space for such an application, and our plans for continued investigation.     

Performance analysis of hybrid network multiprocessor architecture

In this paper we investigate architectures that combine message‐passing and shared‐memory technologies, called hereinafter hybrid architectures. We introduced hybrid architectures in which large buses of the shared‐memory are split into a number of small high‐performance shared‐memory blocks, which are connected via message‐passing architecture, such as hypercube, grid or ring. This way we avoid the possible degradation of the achieved performance due to the fact that the bus performance does not scale well when the number of processors it connects increases. We study the saturation situations of several hybrid network architectures, where adding processors does not reduce the overall execution time. We show that the use of hybrid network architectures leads to significant improvement of the systems price/performance ratio, by significantly improving the performance with almost no system cost increment. Therefore, the usage of hybrid architectures demonstrates how minimal ‘cost’ spending could significantly increase the system performance. In addition, we show that different types of applications have different best hybrid architectures. Copyright © 2000 John Wiley & Sons, Ltd.     

COLLECT: COLLaborativE ConText-aware service oriented architecture for intelligent decision-making in the Internet of Things

Internet of Things (IoT) has radically transformed the world; currently, every device can be connected to the Internet and provide valuable information for decision-making. In spite of the fast evolution of technologies accompanying the grow of IoT, we are still faced with the challenge of providing a service oriented architecture, which facilitates the inclusion of data coming together from several IoT devices, data delivery among a system's agents, real-time data processing and service provision to users. Furthermore, context-aware data processing and architectures still pose a challenge, in spite of being key requirements in order to get stronger IoT architectures. To face this challenge, we propose a COLLaborative ConText Aware Service Oriented Architecture (COLLECT), which facilitates both the integration of IoT heterogeneous domain context data — through the use of a light message broker — and easy data delivery among several agents and collaborative participants in the system — making use of an enterprise service bus —. In addition, this architecture provides real-time data processing thanks to the use of a complex event processing engine as well as services and intelligent decision-making procedures to users according to the needs of the domain in question. As a result, COLLECT has a great impact on context-aware decentralized and collaborative reasoning for IoT, promoting context-aware intelligent decision making in such scope. Since context-awareness is key for a wide range of recommender and intelligent systems, the presented novel solution improves decision making in a large number of fields where such systems require to promptly process a variety of ubiquitous collaborative and context-aware data.     

The Agent Web Model: modeling web hacking for reinforcement learning

Website hacking is a frequent attack type used by malicious actors to obtain confidential information, modify the integrity of web pages or make websites unavailable. The tools used by attackers are becoming more and more automated and sophisticated, and malicious machine learning agents seem to be the next development in this line. In order to provide ethical hackers with similar tools, and to understand the impact and the limitations of artificial agents, we present in this paper a model that formalizes web hacking tasks for reinforcement learning agents. Our model, named Agent Web Model, considers web hacking as a capture-the-flag style challenge, and it defines reinforcement learning problems at seven different levels of abstraction. We discuss the complexity of these problems in terms of actions and states an agent has to deal with, and we show that such a model allows to represent most of the relevant web vulnerabilities. Aware that the driver of advances in reinforcement learning is the availability of standardized challenges, we provide an implementation for the first three abstraction layers, in the hope that the community would consider these challenges in order to develop intelligent web hacking agents.

     

Performance evaluation of SDIAGENT, a multi-agent system for distributed fuzzy geospatial data conflation

A rapid growth of available geospatial data requires development of systems capable of autonomous data retrieval, integration and validation. Mobile agents may provide the suitable framework for developing such systems since this technology, in a natural way, can deal with the distributed heterogeneous nature of such data. In this paper, we evaluate SDIAGENT our, recently introduced, multi-agent architecture for geospatial data integration and conflation, and compare its model performance with that of client/server and single-agent approaches. Experimental results for several realistic scenarios, under varying conditions, are presented for these three system architectures. We analyze the performance alteration for various numbers of participating nodes, varying amount of database accesses, processing loads, and network loads.     

Secure computations on non-integer values with applications to privacy-preserving sequence analysis

In this work we describe a framework which allows to perform secure computations on non-integer values. To this end, we encode values in a way similar to floating point representation and describe protocols that allow to perform efficient secure two party computations on such encoded values. We present two approaches to realize the functionality of the framework. Both approaches come with different properties and are ready to use in various application scenarios. We implemented the framework in C++ and ran several experiments. This allows for a complexity analysis and for a comparison of the two different approaches. We further describe applications to privacy-preserving computations, which greatly benefit from the use of the new framework. In particular, we show how to run an important algorithm in the context of data analysis using Hidden Markov Models (HMM), namely the Viterbi algorithm, in a secure manner.     

SUPPORTING AN AUTONOMOUS SOCIAL AGENT WITHIN A COMPETITIVE ENVIRONMENT

We describe how adversarial risk analysis may be used to support the decision making of an autonomous agent that needs to interact with other agents and persons within a competitive environment. We propose several contextualizations for the problem and suggest the conceptual solution in the proposed scenarios.     

A simulation model for localization of pervasive objects using heterogeneous wireless networks

Position represents a relevant attribute needed by many applications, whose contexts are characterized by pervasiveness of the objects/things in the considered scenarios. In order to infer positions of pervasive objects, which are neither equipped with any location-sensing technologies, nor are unable to locate themselves, it is needed to support them with an appropriate infrastructure, which allows to determine their position in a manner that is transparent to applications. In this work, we aim at extending the positioning service using an agent-based approach, in order to discover and localize different kinds of objects, exploiting cheap and embedded technologies. We describe the design and the implementation of a layered architecture, that supports the localization of devices, and simple pervasive and ubiquitous objects. A simulation tool has been develop to evaluate the proposed solution in different application scenarios of every-day life.     

Finding and resolving security misusability with misusability cases

Although widely used for both security and usability concerns, scenarios used in security design may not necessarily inform the design of usability, and vice-versa. One way of using scenarios to bridge security and usability involves explicitly describing how design decisions can lead to users inadvertently exploiting vulnerabilities to carry out their production tasks. This paper describes how misusability cases, scenarios that describe how design decisions may lead to usability problems subsequently leading to system misuse, address this problem. We describe the related work upon which misusability cases are based before presenting the approach, and illustrating its application using a case study example. Finally, we describe some findings from this approach that further inform the design of usable and secure systems.