Previous work introducing the idea of distribution-based network control determined that some seemingly similar networks can have significantly different levels of controllability. This work investigates these differences in controllability in more detail and finds that one of the driving factors behind controllability may be the influence dynamics within the network. These results suggest that existing structural heuristics for control set selection that do not take into account influence, such as the FAR heuristic used here and in previous network control works, can produce substandard control sets that result in poor controller performance. The development of an algorithm for control set selection based on network influence measurements is identified as an important direction of future work.

One of the issues to be resolved in social recommender systems is the identification of opinion leaders in a network. Finding effective people in societies has been a key question for many groups, e.g., marketers. The research undertaken in this paper focuses on finding important nodes in a network based on their behaviour as well as the structure of the network. This paper views the propagation of information in a social network as a process of infection. The paper proposes an algorithm called the Probability Propagation Method for measuring the probability of infection of all the nodes in a network starting from a given node in the network. Then, assuming independence in activation of nodes in a network, a method is proposed for ranking nodes according to their capabilities in infecting a larger number of nodes in a network. These methods are validated using simulation software in which a non-deterministic model of information diffusion is simulated on several classes of network.

Artificial societies—distributed systems of autonomous agents—are becoming increasingly important in open distributed environments, especially in e‐commerce. Agents require trust and reputation concepts to identify communities of agents with which to interact reliably. We have noted in real environments that adversaries tend to focus on exploitation of the trust and reputation model. These vulnerabilities reinforce the need for new evaluation criteria for trust and reputation models called exploitation resistance which reflects the ability of a trust model to be unaffected by agents who try to manipulate the trust model. To examine whether a given trust and reputation model is exploitation‐resistant, the researchers require a flexible, easy‐to‐use, and general framework. This framework should provide the facility to specify heterogeneous agents with different trust models and behaviors. This paper introduces a Distributed Analysis of Reputation and Trust (DART) framework. The environment of DART is decentralized and game‐theoretic. Not only is the proposed environment model compatible with the characteristics of open distributed systems, but it also allows agents to have different types of interactions in this environment model. Besides direct, witness, and introduction interactions, agents in our environment model can have a type of interaction called a reporting interaction, which represents a decentralized reporting mechanism in distributed environments. The proposed environment model provides various metrics at both micro and macro levels for analyzing the implemented trust and reputation models. Using DART, researchers have empirically demonstrated the vulnerability of well‐known trust models against both individual and group attacks.

Recent work has demonstrated that several trust and reputation models can be exploited by malicious agents with cyclical behaviour. In each cycle, the malicious agent with cyclical behaviour first regains a high trust value after a number of cooperations and then abuses its gained trust by engaging in a bad transaction. Using a game theoretic formulation, Salehi-Abari and White have proposed the AER model that is resistant to exploitation by cyclical behaviour. Their simulation results imply that FIRE, Regret, and a model due to Yu and Singh, can always be exploited with an appropriate value for the period of cyclical behaviour. Furthermore, their results demonstrate that this is not so for the proposed adaptive scheme. This paper provides a mathematical analysis of the properties of five trust models when faced with cyclical behaviour of malicious agents. Three main results are proven. First, malicious agents can always select a cycle period that allows them to exploit the four models of FIRE, Regret, Probabilistic models, and Yu and Singh indefinitely. Second, malicious agents cannot select a single, finite cycle period that allows them to exploit the AER model forever. Finally, the number of cooperations required to achieve a given trust value increases monotonically with each cycle. In addition to the mathematical analysis, this paper empirically shows how malicious agents can use the theorems proven in this paper to mount efficient attacks on trust models.

In many marketplaces consumers may interact with more than one supplier in order to achieve a particular goal. This paper takes the position that trust mediates agent interactions and that agent demands are likely to increase with increasing trust. Furthermore, trust of one agent in another is updated based upon a trust model. The model proposed in this paper is one in which agent demands can be divided amongst suppliers with the division determined by trust. The model evaluates five prominent trust models in a hypothetical marketplace and demonstrates empirically that FIRE, Regret, Probabilistic Trust Models and a model due to Yu and Singh can be exploited such that a consumer agent will continue to interact with a malicious agent despite periodic contract defaults by the malicious agent. Empirical studies support the hypothesis that adaptive trust models such as AER are resistant to this type of exploitation and discourage periodic or cyclical defaulting on contractual obligations.

This paper begins by reviewing different methods of automatic programming while emphasizing the technique of Ant Programming (AP). AP uses an ant foraging metaphor in which ants generate a program by moving through a graph. Generalized Ant Programming (GAP) uses a context-free grammar and an Ant Colony System (ACS) to guide the program generation search process. There are two enhancements to GAP that are proposed in this paper. These are: providing a heuristic for path termination inspired by building construction and a novel pheromone placement algorithm. Three well-known problems -- Quartic symbolic regression, multiplexer, and an ant trail problem -- are experimentally compared using enhanced GAP (EGAP) and GAP. The results of the experiments show the statistically significant advantage of using this heuristic function and pheromone placement algorithm of EGAP over GAP.

A swarm-based improvement to Genetic Programming (GP) is described and tested on the domain of symbolic regression in this paper. The motivating idea is to keep all of the benefits of genetic programming such as crossover and fitness proportional selection within a population of candidate solutions. The improvement comes in using swarm-based ideas similar to Ant Colony Optimization (ACO) to improve the operation of the crossover operator. Statistically significant results are reported in support of the hypothesis that ACO-inspired crossover can improve GP.

Time series have been a major topic of interest and analysis for hundreds of years, with forecasting a central problem. A large body of analysis techniques has been developed, particularly from methods in statistics and signal processing. Evolutionary techniques have only recently have been applied to time series problems. To date, applications of artificial immune system (AIS) techniques have been in the area of anomaly detection. In this paper we apply AIS techniques to the forecasting problem. We characterize a class of search algorithms we call antigenic search and show their ability to give a good forecast of next elements in series generated from Mackey-Glass and Lorenz equations.

The systems biology community is building increasingly complex models and simulations of cells and other biological entities. This community is beginning to look at alternatives to traditional representations such as those provided by ordinary differential equations (ODE). Making use of the object-oriented (OO) paradigm, the Unified Modeling Language (UML) and Real-time Object-Oriented Modeling (ROOM) visual formalisms, we describe a simple model that includes membranes with lipid bilayers, multiple compartments including a variable number of mitochondria, substrate molecules, enzymes with reaction rules, and metabolic pathways. We demonstrate the validation of the model by comparison with Gepasi and comment on the reusability of model components.

Spam, the electronic equivalent of junk mail, affects over 600 million users worldwide. Even as anti-spam solutions change to limit the amount of spam sent to users, the senders adapt to make sure their messages are seen. This paper looks at application of the artificial immune system model to protect email users effectively from spam. In particular, it tests the spam immune system against the publicly available spam assassin corpus of spam and non-spam, and extends the original system by looking at several methods of classifying email messages with the detectors produced by the immune system. The resulting system classifies the messages with similar accuracy to other spam filters, but uses fewer detectors to do so, making it an attractive solution for circumstances where processing time is at a premium.

The management of mobile agent systems that solve problems in a network is an issue that must be addressed if mobile agents are to be deployed industrially. It is clear that insufficient or excessive numbers of agents can cause the problem solving capabilities of an agent-based system to be impaired. Also, agents being software entities are almost always flawed therefore requiring the upgrade problem to be solved. This paper presents distributed algorithms based upon ant social behaviour that solve the problems of agent density maintenance and the agent upgrade problem.

The computer communications world is very dynamic, requiring continual software updating for correction, perfection, and increased functionality. The problem addressed here is that of providing an evolutionary path for software that permits updating without disrupting the operation and management of the network. This problem is relevant to network management software which is also dynamic. For example SNMPv3 is not yet a standard and is not yet widely deployed. Its initial installations will need to be perfected as more experience is acquired. This paper examines a software hot-swapping solution to the problem, whereby management system software modules can be replaced dynamically without disrupting the management process. The paper also discusses application of the technique to a modular SNMPv3 system implemented in Java.

The intelligent network (IN) represents the world-wide accepted basis for uniform provisioning of advanced telecommunication services. On the other hand IP-based communication is fast becoming a viable alternative for voice communications. Mobile agents offer unique opportunities for structuring and implementing open distributed service architectures, facilitated by the dynamic downloading and movement of service code to specific network nodes. In this paper, a new service architecture for IP telephony, based on the ITU-T standard H.323, is proposed. The implementation uses mobile agents and Jini as enabling technologies and existing architectural concepts taken from IN. This IP service architecture enables telecommunication services deployed through mobile service agents on a per-user basis, which results in several advantages when compared to centralized service architectures. The paper demonstrates that the flexible extensible architecture can accommodate not only existing services but is flexible enough to accommodate a wide variety of future services. In addition we show how the architecture addresses the full management life cycle of advanced services, from open third party creation, to subscription and utilization, and ultimately to maintenance and withdrawal.

Mobile agents have been proposed as a solution to the problem of the management of increasingly heterogeneous networks. However, the proposed solutions often ignore the value of legacy solutions and protocols. This paper extends an existing mobile agent framework targeted at network management with an architecture and design for integration with an SNMP agent. Brief descriptions of the use of this solution for the provisioning of ATM permanent virtual circuits and providing a mobile agent MIB are also provided.

Configuring a network service is not an easy task due to the manual schemes that are deployed currently. This paper proposes a plug-and-play approach for service configuration and demonstrates its workability through various distributed computing technologies.