Application Of Multi-agent Systems Research Articles

Assessing the Effect of Aspect Refactoring on Multi-Agent Applications

Multi Agent Systems (MAS) are increasingly gaining importance as a powerful paradigm to designing and implementing distributed applications. However, existing multi-agent applications are developed without considering the separation of non-functional concerns from the functional ones. This makes the implementation, comprehension and maintenance of multi-agent applications hard tasks. Aspect-Oriented Refactoring (AOR) is a promising technique for improving modularity and reducing complexity of existing object oriented software systems by encapsulating crosscutting concerns. The authors present, in this paper, a new dynamic approach for investigating empirically the effect of AOR on MAS applications. They focus, particularly, on the effect of AOR on agent behavior in terms of communication. The proposed approach is supported by a multi-agent profiling tool working on AgentFactory platform.

Profile trees for Büchi word automata, with application to determinization

The determinization of Büchi automata is a celebrated problem, with applications in synthesis, probabilistic verification, and multi-agent systems. Since the 1960s, there has been a steady progress of constructions: by McNaughton, Safra, Piterman, Schewe, and others. Despite the proliferation of solutions, they are all essentially ad-hoc constructions, with little theory behind them other than proofs of correctness. Since Safra, all optimal constructions employ trees as states of the deterministic automaton, and transitions between states are defined operationally over these trees. The operational nature of these constructions complicates understanding, implementing, and reasoning about them, and should be contrasted with complementation, where a solid theory in terms of automata run dags underlies modern constructions.In 2010, we described a profile-based approach to Büchi complementation, where a profile is simply the history of visits to accepting states. We developed a structural theory of profiles and used it to describe a complementation construction that is deterministic in the limit. Here we extend the theory of profiles to prove that every run dag contains a profile tree with at most a finite number of infinite branches. We then show that this property provides a theoretical grounding for a new determinization construction where macrostates are doubly preordered sets of states. In contrast to extant determinization constructions, transitions in the new construction are described declaratively rather than operationally.

Realizing the NOSHAPE MAS Organizational Model

Dynamic reorganization of multi-agent systems (MAS) is currently an interesting and active research area. New emerging concepts such as self-organization and emergence are also getting great focus by MAS research community because of their captivating benefits for engineering complex large-scale MAS applications. Dynamic reorganization is considered as an effective way for building adaptive MAS. This paper provides an operational view of the conceptually proposed NOSHAPE dynamic organizational model for dynamically reorganized multi-agent systems (Abbas, 2014). The operational view concerns possible design decisions, implementation issues, and suggested usage of the NOSHAPE model for practically engineering real life applications. This paper also identifies the possible solutions to be applied throughout the system design to resolve these concerns.

Evolutionary multi-agent systems

AbstractThe aim of this paper is to give a survey on the development and applications of evolutionary multi-agent systems (EMAS). The paper starts with a general introduction describing the background, structure and behaviour of EMAS. EMAS application to solving global optimisation problems is presented in the next section along with its modification targeted at lowering the computation costs by early removing certain agents based on immunological inspirations. Subsequent sections deal with the elitist variant of EMAS aimed at solving multi-criteria optimisation problems, and the co-evolutionary one aimed at solving multi-modal optimisation problems. Each variation of EMAS is illustrated with selected experimental results.

Zer0: An Emergent and Autopoietic Multi-agent System for Novelty Creation in Game Art through Gesture Interaction

This paper describes a transdisciplinary theoretical-practical research, which address on the discussion about the possible applications of Multi-agent Systems, underlying the Maturana and Varela's autopoietic concept considering the achievement of emergent results as heuristics to creativity. Autopoiesis (from the Greek “auto” which means “itself” and “poiesis” which means “creation”) describes the autonomous systems, able to self-reproduce and self-regulate, while iterating with the environment. In order to explore those concepts, we present Zer0, a game that invites the player to drift in a universe ruled by geometric shapes. Through interactions with other shapes, the player is able to evolve from a single line shape to more complex ones. Zer0 is a multi-agent system able to compose emergent music in real time. As interactions occur, chain reactions create the game soundtrack. There are two main agents involved: the player and the other shapes. While the player enjoys the ride, the other shapes are trying to interact with each other in order to expand their lifespan. The communication between agents is made through generated pulses, which are emitted by them and also serves as sonar, in order to perceive the environment.

Multi-Agent System Applications in Healthcare: Current Technology and Future Roadmap

One of the most rapidly evolving government sectors in any country is the healthcare industry. Today, the healthcare system of developed nations is facing the various challenges presented by an aging population. Increasing demand for health services presents a challenge for the existing healthcare industry, as vast amounts of resources will be required. The Multi-Agent System (MAS) approach provides a powerful platform for modeling and solving real world problems such as healthcare. This makes it possible for patients to remain at home and consequently reducing costs. This paper presents developed MAS applications in healthcare, as well as explores the future of developed MAS applications. In particular, it will be argued that these health based MAS applications can provide a reasonable way to mitigate the cost due to increased demand for services.

A Trust-based Mechanism for Avoiding Liars in Referring of Reputation in Multiagent System

Trust is considered as the crucial factor for agents in decision making to choose the most trustworthy partner during their interaction in open distributed multiagent systems. Most current trust models are the combination of experience trust and reference trust, in which the reference trust is estimated from the judgements of agents in the community about a given partner. These models are based on the assumption that all agents are reliable when they share their judgements about a given partner to the others. However, these models are no more longer appropriate to applications of multiagent systems, where several concurrent agents may not be ready to share their private judgement about others or may share the wrong data by lying to their partners. In this paper, we introduce a combination model of experience trust and experience trust with a mechanism to enable agents take into account the trustworthiness of referees when they refer their judgement about a given partner. We conduct experiments to evaluate the proposed model in the context of the e-commerce environment. Our research results suggest that it is better to take into account the trustworthiness of referees when they share their judgement about partners. The experimental results also indicate that although there are liars in the multiagent systems, combination trust computation is better than the trust computation based only on the experience trust of agents.

Strategic Reorganization in Multi-Agent Systems: Inspired by Intelligent Human Organizations

The field of organizational design in multi-agent systems is still in need of powerful tools and methods for enabling effective design, control, and transformation of organizations of different kinds. In this work, we address the problem of reorganization in a distributed problem solving model as a typical multi-agent system application. We look into adaptation of concepts and theories from social organization theory. In particular, we get insights from Schwaninger's model of Intelligent Human Organizations. To this goal, we implement and utilize a multi-level organizational control model which uses a strategic management and an operative management layer to exert organizational control and also to make interactions between these two levels of control possible. Experimental evaluations are performed using a modified model of pursuit game. The results indicate that the proposed model allows the system to stay ahead of the organizational change, resulting in performance improvements, despite the additional costs associated to reorganization.

Design of Adaptive Vehicular Agents Model

Traffic system, in general, like ruralized/urbanized area expansion traffic or pedestrian signals, and footslogging crowds consist of many autonomous, intelligent entities, which are distributed over a large area and interact with each other to achieve certain goals. However, these entities many represent completely different things, like traffic lights, trucks or even road users. According to these entities, we aim at giving insight into the concept of adaptive vehicular agents. Additionally, different applications of multi-agent system to the traffic domain are presented. Furthermore, we propose to traffic flow models which describe driver’s behavior not only on a reactive but also on a cognitive level.

Applications and design of cooperative multi-agent ARN-based systems

The Artificial Reaction Network (ARN) is an artificial chemistry inspired by Cell Signalling Networks. Its purpose is to represent chemical circuitry and to explore the computational properties responsible for generating emergent high-level behaviour. In this paper, the design and application of ARN-based cell-like agents termed “Cytobots” are explored. Such agents provide a facility to explore the dynamics and emergent properties of multicellular systems. The Cytobot ARN is constructed by combining functional motifs found in real biochemical networks. By instantiating this ARN, multiple Cytobots are created, each of which is capable of recognising environmental patterns, stigmergic communication with others and controlling its own trajectory. Applications in biological simulation and robotics are investigated by first applying the agents to model the life-cycle phases of the cellular slime mould D. discoideum and then to simulate an oil-spill clean-up operation. The results demonstrate that an ARN-based approach provides a powerful tool for modelling multi-agent biological systems and also has application in swarm robotics.

Research on Application of Multi-Agent System in the Concentration Forecast of Dissolved Gases in Transformer Oil

In view of the research in the existing forecast model and transformer DGA methods, this paper presents the collaborative model of intelligent concentration forecast of dissolved gases in transformer oil based on multi-Agent system, for improving the predictability of transformer fault. It presents the main function of each Agent, blackboard and JADE bus in this model, discusses the collaborative process of intelligent concentration forecast of dissolved gases in transformer oil based on multi-Agent system, and designs a combination forecasting architecture and a method of composition forecasting. Finally it indicates the feasibility and effectiveness of the intelligent concentration forecast model of dissolved gases in transformer oil based on multi-Agent system according to the application examples.

Robust Distributed Model Predictive Control of Constrained Continuous-Time Nonlinear Systems: A Robustness Constraint Approach

Due to the ubiquitous existence of external disturbances, the design of distributed control algorithms with robustness is an urgent demand for multi-agent system applications. This technical note investigates the robust distributed model predictive control (MPC) problem for a group of nonlinear agents (subsystems) subject to control input constraints and external disturbances. A robustness constraint is proposed to handle the external disturbances. Based on this, a novel robust distributed MPC scheme is designed for the overall agent system. Furthermore, the feasibility of the robust distributed MPC scheme and the robust stability of the overall agent system are analyzed, respectively. The conditions under which the proposed MPC is feasible and the overall agent system is robustly stabilized are established. Finally, the application of the robust distributed MPC to a group of cart-damper-spring systems verifies the theoretical results.

Improving Emergency Response Systems Through the Use of Intelligent Information Systems

The shifts experienced in the environment of disaster operations and emergency response are reshaping the context of information acquisition and utilization in hospitals. In addition to the formulation of emergency response plans, healthcare organizations (especially hospitals) are being challenged by the growing need to maintain and share real time information necessary for the improvement of emergency response processes. Such information-related attention originates from many emergency-specific concerns including the limited focus of current emergency response information systems, decision limitations that challenge the formulation of decision support applications and the characterization of user requirements, the heterogeneity of emergency response information and the difficulty of integrating spatially distributed information sources. The increased attention in emergency response information also emerges from the recent technological developments (in terms of hardware, software functionalities, databases and telecommunication, among others) which significantly affected the processing, storage and retrieval of real time information. This paper focuses on the examination of the context of emergency response in Al Ahsaa area of Saudi Arabia and the applicability of multi-agent information systems through the proposal of an integrated architecture. Then it sheds light on the implementation concerns to ensure the contribution of the proposed architecture towards the engagement of stakeholders, the improvement of the availability and accessibility of emergency management information and the harmonization of emergency response operations. .

Semistability of switched linear systems with application to PageRank algorithms

This paper investigates semistability and its computation for discrete-time, switched linear systems under both deterministic and random switching policies. The notion of semistability pertains to a continuum of initial state dependent equilibria, and finds wide applications in multi-agent and distributed network systems. It is shown in this paper that exponential semistability on a common equilibrium space is equivalent to output exponential stability of a reduced switched linear system with a suitably defined output, under arbitrary and random switchings. Besides, their convergence rates are shown to be identical. A generating function based approach is proposed to compute convergence rates of the reduced switched systems under these switching rules. The obtained semistability results are applied to performance analysis of PageRank algorithms for distributed web-page systems subject to topology switching. The iteration processes of these algorithms are formulated as switched linear systems. Their equilibrium properties are studied, and convergence rates are characterized via the semistability techniques and the generating function approach.

A Supermodular Optimization Framework for Leader Selection Under Link Noise in Linear Multi-Agent Systems

In many applications of multi-agent systems (MAS), a set of leader agents acts as control inputs to the remaining follower agents. In this paper, we introduce an analytical approach to selecting leader agents in order to minimize the total mean-square error of the follower agent states from their desired value in steady-state in the presence of noisy communication links. We show that, for a set of link weights based on the second-order noise statistics, the problem of choosing leaders in order to minimize this error can be solved using supermodular optimization techniques, leading to efficient algorithms that are within a provable bound of the optimum. We formulate two leader selection problems within our framework, namely the problem of choosing a fixed number of leaders to minimize the error, as well as the problem of choosing the minimum number of leaders to achieve a tolerated level of error. We study both leader selection criteria for different scenarios, including MAS with static topologies, topologies experiencing random link or node failures, switching topologies, and topologies that vary arbitrarily in time due to node mobility. In addition to providing provable bounds for all of these cases, simulation results demonstrate that our approach outperforms other leader selection methods, such as node degree-based and random selection methods, and provides comparable performance to current state of the art algorithms.

Practical Applications of Ontology-based multi-agent systems for PPHIIS

This study presents an ontology-based and multi-agent system for PPHIIS assessment, an integrated information system for the health of People’s Police (PPHIIS) was investigated, and an ontology model was designed and implemented to represent the health domain knowledge combined with the agents’ cooperation, the SOsRCE-ontology model not only solved semantic problems about heterogeneous data but also enabled medical staff to extract detailed medical information more efficiently. Meanwhile, a level of abstraction at which we envisage integrated systems carrying out cooperative work by inter-operating globally across networks connecting police, hospitals and terminals was offered by the multi-agent view.

Switching Q-Learning Using Golden Cross on Hunter Games

Recently, applications of multiagent systems are expected from the view point of the parallel and distributed processing of systems. Reinforcement learning attracts attention as an implementing method of multiagent systems. However, there is a problem that the more the number of agents to deal with increases, the slower the speed of learning becomes. To solve this problem, we propose a new reinforcement learning method that can learn quickly and reduce amount of memory. It tries to increase efficiency of the learning on a hunter game by paying attention to partial states of two agents among a large number of agents. In addition, the proposed method employs a switching algorithm and detects automatically a switching time by using a special index called golden cross.

Communication Security on MASs - A Literature Review

With widely applications of Multi-Agent System in internet and intranet, how to ensure security related to multi-agent is a big issue. This paper mainly focuses on communication security of MASs. Through investigation, there are five typical security models relating to MASs, and all these models communicate using XML data format. With traditional cryptographic technology, these models provide a coarse granularity security. The paper proposes XML Security Specifications which based on XML should be deployed within MASs communication.

Evaluation of the Ontological Knowledge Model/ Ontoloģiskā zināšanu modeļa novērtēšana/ Оценка качества онтологической модели знаний

An ontology is an explicit formal conceptualization of some domain of interest. Ontology application is widely used in multi-agent systems. Ontology provides the channel through which software agents interact; therefore, if the ontology definitions have not been sufficiently evaluated, communication between software agents may not succeed. Ontology evaluation guarantees to end users the correctness and completeness of ontology definitions and software. The goal is to detect wrong, incomplete or missed definitions in the ontology. The study is based on multi-agent system application for raw materials management task. The common ontology is used for agent cooperation; the quality of ontology is measured with ontology evaluation techniques.

A Secure Communication for a Reputation Management Model in Multi-agent System

Recent multi-agent systems are characterized by decentralized control, autonomy and local views. The application of multiagent systems on open environment such as internet creates new challenges especially with respect to security issues such as authentication, authorization and privacy. The characteristics of multiagent systems introduce vulnerabilities and threats to secured communication. One practical way to minimize the threats is to evaluate the trust and reputation of the agents. But trust is not enough for secure communication. Many trust/reputation models have given solutions, but they fail to properly evaluate trust when malicious agents start to behave in an unpredictable way and also they fail to address the problem of secure communication between agents. In this paper a secure communication for reputation system in multiagent system is proposed. It provides a flexible way to present differentiated trust and combine different aspects of trust that can meet agent’s different needs. And also it allows secure file delivery among agents. The idea of using cryptographic protocols MD5 and DES effectively encrypt the file without any overhead to the network.