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Using multi-agent system for code and data propagation
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Multi-agent systems (MASs) include multiple interacting agents within an environment to provide a solution for complex systems that cannot be easily solved with individual agents or monolithic systems. However, the development of MASs is not trivial due to the various agent properties such as autonomy, responsiveness, and proactiveness, and the need for realization of the many different agent interactions. To support the development of MASs various domain specific modeling languages (DSMLs) have been introduced that provide a declarative approach for modeling and supporting the generation of agent based systems. To be effective, the proposed DSMLs need to meet the various stakeholder concerns and the related quality criteria for the corresponding MASs. Unfortunately, very often the evaluation of the DSML is completely missing or has been carried out in idiosyncratic approach. If the DSMLs are not well-defined then implicitly this will have an impact on the quality of the MASs. In this paper, we present an evaluation framework and systematic approach for assessing existing or newly defined DSMLs for MASs. The evaluation is specific for MAS DSMLs and targets both the language and the corresponding tools. To illustrate the evaluation approach, we first present SEA_ML, which is a model driven MAS DSML for supporting the modeling and generation of agent-based systems. The evaluation of SEA_ML is based on a multi case study research approach and provides both qualitative evaluation and quantitative analysis. We report on the lessons learned considering the adoption of the evaluation approach as well as the SEA_ML for supporting the generation of agent-based systems.
We briefly review previous work on the welfare engineering framework where au- tonomous software agents negotiate on the allocation of a number of discrete re- sources, and point out connections to combinatorial optimisation problems, includ- ing combinatorial auctions, that shed light on the computational complexity of the framework. We give particular consideration to scenarios where the preferences of agents are modelled in terms of k-additive utility functions, i.e. scenarios where syn- ergies between different resources are restricted to bundles of at most k items.
Mobile-agent performance is a critical issue for mobile-agent based systems and their feasibility. We have utilized discrete-event simulation technique to build a performance evaluation scheme for generic mobile-agent systems. We introduced discrete event models for three different cases: known destination agents, unknown destination agents and rendez-vous agents. These three models generalize almost all of the possible agent migration scenarios. We have also implemented our models in a discrete event simulator and evaluated total service time and useful time utilization, for scenarios with different agent sizes and arrival rates. Mobile agents programming is the new paradigm that has emerged for structuring distributed applications and heterogeneous systems. In this model, an agent is a process that can move with its own code and execution context from machine to machine in order to perform its task. Mobile agents are primarily intended to be used for applications distributed over large-scale networks since they permit reduction in communication costs by moving computation to the host on which the target data resides. The use of Mobile Agents in this kind of applications represents a fresh approach, which potentially solves most of the problems that exist in centralized client/server solutions. The applications can be more scalable, more robust, can be easily upgraded or customized and reduce the traffic in the network. Most justifications for using mobile agents can be divided into performance benefits and software engineering benefits categories. The performance benefits of mobile agents include decrease in network bandwidth consumption, reduced computation, reduced latency, and better fault-tolerance. The software engineering benefits affirm that the mobile agent paradigm can help application programmers design solutions more naturally suited to certain types of applications that can help improving code modularity and reusability, and hide network, system and protocol heterogeneity. The main contribution of this paper is the application of discrete event simulation technique for performance analysis of Mobile-agent systems. We introduce and analyze three generic models, that are general enough to cover several real-life systems. We propose models and examine the performance of mobile-agents on these generic cases over different network types, with different parameters for agent size, agent flow throughput and agent migration patterns.
The purpose of this paper is twofold. On the first hand, we review a novel implementation of explicit continuation handling in Java, Javaflow. This development avoids many of the pitfalls of previous attempts of enabling this powerful mechanisim in the JVM. On the second hand, we show how this de-velopment is of relevance for the Mobile Agent Systems community. More con-cretely, continuations are used to model and implement strong agent migration, and the arquitecture and design of a basic MAS based on this paradigm is de-scribed. The feasibility of all the proposed solutions is backed by explicit imple-mentations.
Topological analysis of intelligent agent networks provides crucial information about the structure of agent distribution over a network. Performance analysis of agent network topologies helps multi-agent system developers to understand the impact of topology on system efficiency and effectiveness. Appropriate topology analysis enables the adoption of suitable frameworks for specific multi-agent systems. In this paper, we systematically classify agent network topologies and propose a novel hybrid topology for distributed multi-agent systems. We compare the performance of this topology with two other common agent network topologies—centralised and decentralised topologies—within a new multi-agent framework, called Agent-based Open Connectivity for DSS (AOCD). Three major aspects are studied for estimating topology performance, which include (i) transmission time for a set of requests; (ii) waiting time for processing requests; and (iii) memory consumption for storing agent information. We also conduct a set of AOCD topological experiments to compare the performance of hybrid and centralised agent network topologies and illustrate our experimental results in this paper.
Remote reprogramming of in situ wireless sensor networks (WSNs) via the wireless link is an important capability. Se- curing the process of reprogramming allows each sensor node to authenticate each received code image. Due to the re- source constraints of WSNs, public key schemes must be used sparingly. This paper introduces a mechanism for se- cure and efficient code distribution that employs public key cryptography only to sign the root of a combined structure consisting of both hash chains and hash trees. The chain based scheme works best when packets are received in the order they are sent with very few losses. Our hash tree based scheme allows nodes to authenticate packets and verify their integrity quickly, even when the packets may arrive out of order, but can result in too many public key operations. In- tegrating hash chains and hash trees produces a mechanism that is both resilient to losses and lightweight in terms of re- ducing memory consumption and the number of public key operations that a node has to perform. Simulation shows that the proposed secure reprogramming schemes add only a modest amount of overhead to a conventional non-secure reprogramming scheme, namely Deluge, and are therefore feasible and practical in a wireless sensor network.
The availability of reliable models of computer virus propagation would prove useful in a number of ways, in order both to predict future threats, and to develop new containment measures. In this paper, we review the most popular models of virus propagation, analyzing the underlying assumptions of each of them, their strengths and their weaknesses. We also introduce a new model, which extends the Random Constant Spread modeling technique, allowing us to draw some conclusions about the behavior of the Internet infrastructure in presence of a self-replicating worm. A comparison of the results of the model with the actual behavior of the infrastructure during recent worm outbreaks is also presented.
In this paper, we present an adaptive organizational policy for multi-agent systems called \acro{trace}. \acro{trace} allows a collection of multi-agent organizations to dynamically allocate tasks and resources between themselves in order to efficiently process an incoming stream of task requests. \acro{trace} is intended to cope with environments in which tasks have time constraints, and environments that are subject to load variations. \acro{trace} is made up of two key elements: the task allocation protocol (\acro{tap}) and the resource allocation protocol (\acro{rap}). The \acro{tap} allows agents to cooperatively allocate their tasks to other agents with the capability and opportunity to successfully carry them out. As requests arrive arbitrarily, at any instant, some organizations could have surplus resources while others could become overloaded. In order to minimize the number of lost requests caused by an overload, the allocation of resources to organizations is changed dynamically by the resource allocation protocol (\acro{rap}), which uses ideas from computational market systems to allocate resources (in the form of problem solving agents) to organizations. We begin by formally defining the task allocation problem, and show that it is \acro{NP}-complete, and hence that centralized solutions to the problem are unlikely to be feasible. We then introduce the task and resource allocation protocols, focussing on the way in which resources are allocated by the \acro{rap}. We then present some experimental results, which show that \acro{trace} exhibits high performance despite unanticipated changes in the environment.
This paper provides an overview of research and development activities in the field of autonomous agents and multi-agent systems. It aims to identify key concepts and applications, and to indicate how they relate to one-another. Some historical context to the field of agent-based computing is given, and contemporary research directions are presented. Finally, a range of open issues and future challenges are highlighted.
Agent-based computing is a promising approach for developing applications in complex do-mains. However, despite the great deal of research in the area, a number of challenges still need to be faced (i) to make agent-based computing a widely accepted paradigm in software engineering practice, and (ii) to turn agent-oriented software abstractions into practical tools for facing the complexity of modern application areas. In this paper, after a short introduction to the key concepts of agent-based computing (as they pertain to software engineering), we characterise the emerging key issues in multiagent systems (MASs) engineering. In particular, we show that such issues can be analysed in terms of three different ''scales of observation'', i.e., in analogy with the scales of observation of physical phenomena, in terms of micro, macro, and meso scales. Based on this characterisation, we discuss, for each scale of observation, what are the peculiar engineering issues arising, the key research challenges to be solved, and the most promising research directions to be explored in the future.
We introduce the concept of keylets, which are mobile code used to control the propagation of keys in a system, as well as a technique for mobile agent code security that involves encryption of partitioned code components. Keylets are used to support this technique by directing the distribution of keys that decrypt the encrypted components. Formalisation for keylet operations is presented, and a scenario illustrating the use of keylets to implement the technique for mobile agent code security is detailed.
This paper is dedicated to the issue of structural performance of multi-agent platforms. Due to the wide range of all available architectures, we have concentrated only on Java RMI implementations. The main goal of this paper consists of two parts. The first one is to investigate and develop the performance metrics to enable evaluation of distributed systems without reorganization of the running system. The second part is the programming verification of two considered Java RMI multi-agent solutions: Aglets and Jade. We have examined the defined metrics in many experiments with different network and environment configurations to provide experimental evidence that these metrics are adequate in variety of conditions.
The process of agent migration is the major difference between logical code mobility of software agents and physical mobility of mobile nodes in ad hoc networks. Without considering agent transfer, it would make little sense to mention the modeling of strong code mobility, which aims to make a migrated agent restarted exactly from the state when it was stopped before migration. From the perspective of system's architecture, this paper proposes a two-layer approach for the formal modeling of logical agent mobility (LAM) using predicate/transition (PrT) nets. We view a mobile agent system as a set of agent spaces and agents could migrate from one space to another. Each agent space is explicitly abstracted to be a component, consisting of an environmental part and an internal connector dynamically binding agents with their environment. We use a system net, agent nets, and a connector net to model the environment, agents, and the connector, respectively. In particular, agent nets are packed up as parts of tokens in system nets, so that agent transfer and location change are naturally captured by transition firing (token game) in Petri nets. Agent nets themselves are active only at specific places and disabled at all the other places in a system net. The semantics of such a two-layer LAM model is defined by transforming it into a PrT net. This facilitates the analysis of several properties about location, state, and connection. In addition, this paper also presents a case study of modeling and analyzing an information retrieval system with mobile agents.
This paper describes a series of simulations run to estimate various worm growth patterns and their corresponding propagation algorithms. It also tests and verifies the impact of various improvements, starting from a trivial simulation of worm propagation and the underlying network infrastructure to more refined models, it attempts to determine the theoretical maximum propagation speed of worms and how it can be achieved. It also estimates the impact a malicious worm could have on the overall infrastructure.
Multi-agent systems are particularly appropriate for resource allocation, but configuring them for efficient operation requires understanding their dynamics. Concepts from statistical physics, such as phase transitions, can help. In decision problems such as constraint satisfaction, such transitions exhibit an easy-hard-easy effort profile, so that highly overconstrained problems are easier to solve than those near the transition. The conventional wisdom is that the profile in optimization problems such as resource allocation is monotonic, becoming more difficult as constraints increase.
A Multi-Agent System is an organization of coordinated autonomous agents that interact in order to achieve common goals. Considering real world organizations as an analogy, this paper proposes architectural styles for MAS which adopt concepts from organizational theories. The styles are modeled using the i* framework which o#ers the notions of actor, goal and actor dependency and specified in Formal Tropos. They are evaluated with respect to a set of software quality attributes, such as predictability and adaptability. In addition, we conduct a comparative study of organizational and conventional software architectures using the mobile robot control example from the Software Engineering literature. The research is conducted in the context of Tropos, a comprehensive software system development methodology.
Learn how to employ JADE to build multi-agent systems! JADE (Java Agent DEvelopment framework) is a middleware for the development of applications, both in the mobile and fixed environment, based on the Peer-to-Peer intelligent autonomous agent approach. JADE enables developers to implement and deploy multi-agent systems, including agents running on wireless networks and limited-resource devices. Developing Multi-Agent Systems with JADE is a practical guide to using JADE. The text will give an introduction to agent technologies and the JADE Platform, before proceeding to give a comprehensive guide to programming with JADE. Basic features such as creating agents, agent tasks, agent communication, agent discovery and GUIs are covered, as well as more advanced features including ontologies and content languages, complex behaviours, interaction protocols, agent mobility, and the in-process interface. Issues such as JADE internals, running JADE agents on mobile devices, deploying a fault tolerant JADE platform, and main add-ons are also covered in depth. Developing Multi-Agent Systems with JADE: • Comprehensive guide to using JADE to build multi-agent systems and agent orientated programming. • Describes and explains ontologies and content language, interaction protocols and complex behaviour. • Includes material on persistence, security and a semantics framework. • Contains numerous examples, problems, and illustrations to enhance learning. • Presents a case study demonstrating the use of JADE in practice. • Offers an accompanying website with additional learning resources such as sample code, exercises and PPT-slides. This invaluable resource will provide multi-agent systems practitioners, programmers working in the software industry with an interest on multi-agent systems as well as final year undergraduate and postgraduate students in CS and advanced networking and telecoms courses with a comprehensive guide to using JADE to employ multi agent systems. With contributions from experts in JADE and multi agent technology.
Agent software is a rapidly developing area of research. However, the overuse of the word “agent” has tended to mask the fact that, in reality, there is a truly heterogeneous body of research being carried out under this banner. This overview paper presents a typology of agents. Next, it places agents in context, defines them and then goes on, inter alia, to overview critically the rationales, hypotheses, goals, challenges and state-of-the-art demonstrators of the various agent types in our typology. Hence, it attempts to make explicit much of what is usually implicit in the agents literature. It also proceeds to overview some other general issues which pertain to all the types of agents in the typology. This paper largely reviews software agents, and it also contains some strong opinions that are not necessarily widely accepted by the agent community.
ince the beginning of recorded history, people have been fascinated with the idea of non-human agencies.1 Popular notions about androids, hu- manoids, robots, cyborgs, and science fiction creatures permeate our cul- ture, forming the unconscious backdrop against which software agents are per- ceived. The word "robot," derived from the Czech word for drudgery, became popular following Karel Capek's 1921 play RUR: Rossum Universal Robots. While Capek's robots were factory workers, the public has also at times em- braced the romantic dream of robots as "digital butlers" who, like the mechani- cal maid in the animated feature "The Jetsons," would someday putter about the living room performing mundane household tasks. Despite such innocuous beginnings, the dominant public image of artificially intelligent embodied crea- tures often has been more a nightmare than a dream. Would the awesome power of robots reverse the master-slave relationship with humans? Everyday experiences of computer users with the mysteries of ordinary software, riddled with annoying bugs, incomprehensible features, and dangerous viruses rein- force the fear that the software powering autonomous creatures would pose even more problems. The more intelligent the robot, the more capable of pursu- ing its own self-interest rather than its master's. The more humanlike the robot, the more likely to exhibit human frailties and eccentricities. Such latent con- cerns cannot be ignored in the design of software agents—indeed, there is more than a grain of truth in each of them! Though automata of various sorts have existed for centuries, it is only with the development of computers and control theory since World War II that any- thing resembling autonomous agents has begun to appear. Norman (1997) ob- serves that perhaps "the most relevant predecessors to today's intelligent agents are servomechanisms and other control devices, including factory control and the automated takeoff, landing, and flight control of aircraft." However, the agents now being contemplated differ in important ways from earlier concepts.
Along with the fast progress the interconnection of computer systems makes the need for enterprise-wide distributed solutions grows. These systems have to support hundreds or even thousands of sites located all over the world. The distances between the sites will impose high communication costs on distributed activities thus significantly increasing their response times. This problem can be alleviated through migrating objects to the sites where they are needed. However, migration in such systems cannot be managed globally. Therefore, we present a migration protocol for (autonomous) objects which enables them autonomously to decide whether or not and which site to migrate to, thus making the system self-tuning. We also present two migration strategies objects can use to derive their migration decisions. The first one finds the optimal placement for an object while the second strategy in some cases returns a sub-optimal location but induces lower computation costs. The two strategies are evaluated through benchmarks in a distributed system of autonomous objects. The experiments show a significant decrease in communication costs when migration is employed.
Multiagent systems is an expanding field that blends classical fields like game theory and decentralized control with modern fields like computer science and machine learning. This monograph provides a concise introduction to the subject, covering the theoretical foundations as well as more recent developments in a coherent and readable manner.
The text is centered on the concept of an agent as decision maker. Chapter 1 is a short introduction to the field of multiagent systems. Chapter 2 covers the basic theory of singleagent decision making under uncertainty. Chapter 3 is a brief introduction to game theory, explaining classical concepts like Nash equilibrium. Chapter 4 deals with the fundamental problem of coordinating a team of collaborative agents. Chapter 5 studies the problem of multiagent reasoning and decision making under partial observability. Chapter 6 focuses on the design of protocols that are stable against manipulations by self-interested agents. Chapter 7 provides a short introduction to the rapidly expanding field of multiagent reinforcement learning.
The material can be used for teaching a half-semester course on multiagent systems covering, roughly, one chapter per lecture.
Moving objects databases have become an important research issue in recent years. For modeling and querying moving objects,
there exists a comprehensive framework of abstract data types to describe objects moving freely in the 2D plane, providing
data types such as moving point or moving region. However, in many applications people or vehicles move along transportation networks. It makes a lot of sense to model the
network explicitly and to describe movements relative to the network rather than unconstrained space, because then it is much
easier to formulate in queries relationships between moving objects and the network. Moreover, such models can be better supported
in indexing and query processing. In this paper, we extend the ADT approach by modeling networks explicitly and providing
data types for static and moving network positions and regions. In a highway network, example entities corresponding to these
data types are motels, construction areas, cars, and traffic jams. The network model is not too simplistic; it allows one
to distinguish simple roads and divided highways and to describe the possible traversals of junctions precisely. The new types
and operations are integrated seamlessly into the ADT framework to achieve a relatively simple, consistent and powerful overall
model and query language for constrained and unconstrained movement.
The Grid and agent communities both develop concepts and mechanisms for open distributed systems, albeit from different perspectives. The Grid community has historically focused on “brawn”: infrastructure, tools, and applications for reliable and secure resource sharing within dynamic and geographically distributed virtual organizations. In contrast, the agents community has focused on “brain”: autonomous problem solvers that can act flexibly in uncertain and dynamic environments. Yet as the scale and ambition of both Grid and agent deployments increase, we see a convergence of interests, with agent systems requiring robust infrastructure and Grid systems requiring autonomous, flexible behaviors. Motivated by this convergence of interests, we review the current state of the art in both areas, review the challenges that concern the two communities, and propose research and technology development activities that can allow for mutually supportive efforts
The purpose of semantic Web services discovery is to use semantic knowledge to find services that can be composed to form an admissible answer to a query. In this paper we consider the advantage of adopting a knowledge-based approach to Web services discovery. Services are described by means of terminological axioms and constrained clauses. We apply a specific resolution and a constraint propagation mechanism to evaluate queries over services.
Peer-to-peer networks have gained much attention due to their attractive features of self-organization, scalability and decentralized control. The key challenge in these networks is how to efficiently locate and retrieve the correct data. In this paper, we propose propagation and routing algorithms for a fully decentralized, self-organizing network. Our goal is to maximize the probability of finding the data, minimize peer access latencies and balance the workload among many peers. Central to our approach is the Kundali data structure that represents the set of data maintained by the peers and drives the smart routing of the search requests (queries). We have implemented our algorithms in the context of a fully decentralized Internet caching service in our internal network. Our mechanism is inexpensive, highly scalable, and resilient to node failures and with low administration cost. Experimental results validate or algorithms and show that they have good performance results.
Describes a security framework in distributed systems where an
intelligent agent handles the security monitoring at each host. The
agents are made responsible for alerting the system administrators about
an attempted intrusion or misuse of a particular system. Recently, there
has been an increase in the number of reports of such attacks, which are
widespread across the network and affect a chain of systems before they
attack the actual target system. To detect such attacks, the amount of
information associated within a single isolated system is inadequate for
an agent to confirm an intrusion. Therefore, the need is emphasized for
a framework that allows the agents to negotiate with their co-agents in
order to share information about an intrusion, thereby aiding in the
effective handling of intrusion detection. Our design aims at developing
such a framework in the FIPA-OS (Foundation for Intelligent Physical
Agents - Open Source) environment, which provides most of the source
code for building agents on its platform. The concept of mutual
co-operation among agents has been developed as a means of querying.
These queries are carried out by tasks associated with each agent. The
protocols to support these interactions by means of the queries are
explained. The issues and requirements involved in standardizing
formats, interaction protocols and architectures to co-manage intrusion
detection are discussed
The Grid and agent communities both develop concepts and mechanisms for open distributed systems, albeit from different perspectives. The Grid community has historically focused on "brawn": infrastructure, tools, and applications for reliable and secure resource sharing within dynamic and geographically distributed virtual organizations. In contrast, the agents community has focused on "brain": autonomous problem solvers that can act flexibly in uncertain and dynamic environments. Yet as the scale and ambition of both Grid and agent deployments increase, we see a convergence of interests, with agent systems requiring robust infrastructure and Grid systems requiring autonomous, flexible behaviors. Motivated by this convergence of interests, we review the current state of the art in both areas, review the challenges that concern the two communities, and propose research and technology development activities that can allow for mutually supportive efforts.
D'Agents is a general-purpose mobile-agent system that has been used in several informationretrieval applications. In this paper, we rst examine one such application, operational support for military eld personnel, where D'Agents greatly simpli es the task of providing ecient, application-speci c access to remote information resources. After describing the application, we discuss the key dierences between D'Agents and most other mobile-agent systems, notably its support for strong mobility and multiple agent languages. Finally, we derive a small, simple application that is representative of many information-retrieval tasks, including those in the example application, and use this application to compare the scalability of mobile agents and traditional client/server approaches. The results con rm and quantify the usefulness of mobile code, and perhaps more importantly, con rm that intuition about when to use mobile code is usually correct. Although signi cant additional experiments are needed to fully characterize the complex mobile-agent performance space, the results here help answer the basic question of when mobile agents should be considered at all, particularly for information-retrieval applications.
Mobile agent systems provide support for the execution of mobile software components, called agents. Agents acting on behalf of different users can move between execution environments hosted by different organizations. The security implications of this model are evident and these security concerns have been addressed by extending the authentication and access control mechanisms originally conceived for distributed operating systems to mobile agent systems. Other well-known security mechanisms have been neglected. In particular, satisfactory auditing mechanisms have seldom been implemented for mobile agent systems.
A mobile agent is an executing program that can migrate, at times of its own choosing, from machine to machine in a heterogeneous network. On each machine, the agent interacts with stationary service agents and other resources to accomplish its task. In this chapter, we first make the case for mobile agents, discussing six strengths of mobile agents and the applications that benefit from these strengths. Although none of these strengths are unique to mobile agents, no competing technique shares all six. In other words, a mobile-agent system provides a single general framework in which a wide range of distributed applications can be implemented eciently and easily. We then present a representative cross-section of current mobile-agent systems. 1 Introduction A mobile agent is an executing program that can migrate, at times of its own choosing, from machine to machine in a heterogeneous network. On each machine, the agent interacts with stationary service agents and other resource...
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