Abstract: Recent developments on multi-agent system have brought a new research field on image processing. Several algorithms are used simultaneously and improved in deferent applications while new methods are investigated. This paper presents a new automatic method for edge detection using several agents and many different actions. The proposed multi-agent system is based on parallel agents that locally perceive their environment, that is to say, pixels and additional environmental information. This environment is built using Vector Field Convolution that attract free agent to the edges. Problems of partial, hidden or edges linking are solved with the cooperation between agents. The presented method was implemented and evaluated using several examples on different synthetic and medical images. The obtained experimental results suggest that this approach confirm the efficiency and accuracy of detected edge.
Abstract: The rapid generation of high volume and a broad variety of data from the application of new technologies pose challenges for the generation of business-intelligence. Most organizations and business owners need to extract data from multiple sources and apply analytical methods for the purposes of developing their business. Therefore, the recently decentralized data management environment is relying on a distributed computing paradigm. While data are stored in highly distributed systems, the implementation of distributed data-mining techniques is a challenge. The aim of this technique is to gather knowledge from every domain and all the datasets stemming from distributed resources. As agent technologies offer significant contributions for managing the complexity of distributed systems, we consider this for next-generation data-mining processes. To demonstrate agent-based business intelligence operations, we use agent-oriented modeling techniques to develop a new artifact for mining massive datasets.
Abstract: This paper discusses the intake of combining multi-criteria
decision analysis (MCDA) with OLAP systems, to generate
an integrated analysis process dealing with complex multi-criteria
decision-making situations. In this context, a multi-agent modeling is
presented for decision support systems by combining multi-criteria
decision analysis (MCDA) with OLAP systems. The proposed
modeling which consists in performing the multi-agent system
(MAS) architecture, procedure and protocol of the negotiation model
is elaborated as a decision support tool for complex decision-making
environments. Our objective is to take advantage from the multi-agent
system which distributes resources and computational
capabilities across interconnected agents, and provide a problem
modeling in terms of autonomous interacting component-agents.
Thus, the identification and evaluation of criteria as well as the
evaluation and ranking of alternatives in a decision support situation
will be performed by organizing tasks and user preferences between
different agents in order to reach the right decision. At the end, an
illustrative example is conducted to demonstrate the function and
effectiveness of our MAS modeling.
Abstract: The teaching of computer programming for beginners
has been generally considered as a difficult and challenging task.
Several methodologies and research tools have been developed,
however, the difficulty of teaching still remains. Our work integrates
the state of the art in teaching programming with game software and
further provides metrics for the evaluation of student performance in
a collaborative activity of playing games. This paper aims to present a
multi-agent system architecture to be incorporated to the educational
collaborative game software for teaching programming that monitors,
evaluates and encourages collaboration by the participants. A
literature review has been made on the concepts of Collaborative
Learning, Multi-agents systems, collaborative games and techniques
to teach programming using these concepts simultaneously.
Abstract: The design of Reverse logistics Network has attracted
growing attention with the stringent pressures from both
environmental awareness and business sustainability. Reverse
logistical activities include return, remanufacture, disassemble and
dispose of products can be quite complex to manage. In addition,
demand can be difficult to predict, and decision making is one of the
challenges task in such network. This complexity has amplified the
need to develop an integrated architecture for product return as an
enterprise system. The main purpose of this paper is to design Multi
Agent System (MAS) architecture using the Prometheus
methodology to efficiently manage reverse logistics processes. The
proposed MAS architecture includes five types of agents: Gate
keeping Agent, Collection Agent, Sorting Agent, Processing Agent
and Disposal Agent which act respectively during the five steps of
reverse logistics Network.
Abstract: Supply chains are the backbone of trade and
commerce. Their logistics use different transport corridors on regular
basis for operational purpose. The international supply chain
transport corridors include different infrastructure elements (e.g.
weighbridge, package handling equipments, border clearance
authorities, and so on). This paper presents the use of multi-agent
systems (MAS) to model and simulate some aspects of transportation
corridors, and in particular the area of weighbridge resource
optimization for operational profit. An underlying multi-agent model
provides a means of modeling the relationships among stakeholders
in order to enable coordination in a transport corridor environment.
Simulations of the costs of container unloading, reloading, and
waiting time for queuing up tracks have been carried out using data
sets. Results of the simulation provide the potential guidance in
making decisions about optimal service resource allocation in a trade
corridor.
Abstract: This article presents our prototype MASET (Multi
Agents System for E-Tutoring Learners engaged in online
collaborative work). MASET that we propose is a system which
basically aims to help tutors in monitoring the collaborative work of
students and their various interactions. The evaluation of such
interactions by the tutor is based on the results provided by the
automatic analysis of the interaction indicators. This system is
predicated upon the middleware JADE (Java Agent Development
Framework) and e-learning Moodle platform. The MASET
environment is modeled by AUML which allows structuring the
different interactions between agents for the fulfillment and
performance of online collaborative work. This multi-agent system
has been the subject of a practical experimentation based on the
interactions data between Master Computer Engineering and System
students.
Abstract: These days customer satisfaction plays vital role in
any business. When customer searches for a product, significantly a
junk of irrelevant information is what is given, leading to customer
dissatisfaction. To provide exactly relevant information on the
searched product, we are proposing a model of KaaS (Knowledge as
a Service), which pre-processes the information using decision
making paradigm using Multi-agents.
Information obtained from various sources is taken to derive
knowledge and they are linked to Cloud to capture new idea. The
main focus of this work is to acquire relevant information
(knowledge) related to product, then convert this knowledge into a
service for customer satisfaction and deploy on cloud.
For achieving these objectives we are have opted to use multi
agents. They are communicating and interacting with each other,
manipulate information, provide knowledge, to take decisions. The
paper discusses about KaaS as an intelligent approach for Knowledge
acquisition.
Abstract: In this paper, reliable consensus of multi-agent systems
with sampled-data is investigated. By using a suitable
Lyapunov-Krasovskii functional and some techniques such as
Wirtinger Inequality, Schur Complement and Kronecker Product, the
results of such system are obtained by solving a set of Linear Matrix
Inequalities (LMIs). One numerical example is included to show the
effectiveness of the proposed criteria.
Abstract: In this paper, we propose an intelligent system that is
used for monitoring the health conditions of patients. Monitoring the
health condition of patients is a complex problem that involves
different medical units and requires continuous monitoring especially
in rural areas because of inadequate number of available specialized
physicians. The proposed system will improve patient care and drive
costs down comparing to the existing system in Jordan. The proposed
system will be the start point to faster and improve the
communication between different units in the health system in
Jordan. Connecting patients and their physicians beyond hospital
doors regarding their geographical area is an important issue in
developing the health system in Jordan. The ability of making
medical decisions, the quality of medical is expected to be improved.
Abstract: The Great East Japan Earthquake occurred at 14:46 on Friday, March 11, 2011. It was the most powerful known earthquake to have hit Japan. The earthquake triggered extremely destructive tsunami waves of up to 40.5 meters in height. We focus on the ship’s evacuation from tsunami. Then we analyze about ships evacuation from tsunami using multi-agent simulation and we want to prepare for a coming earthquake. We developed a simulation model of ships that set sail from the port in order to evacuate from the tsunami considering the ship carrying dangerous goods.
Abstract: This paper investigates the joint effect of the
interconnected (n,k)-star network topology and Multi-Agent
automated control on restoration and reconfiguration of power
systems. With the increasing trend in development in Multi-Agent
control technologies applied to power system reconfiguration
in presence of faulty components or nodes. Fault tolerance is
becoming an important challenge in the design processes of the
distributed power system topology. Since the reconfiguration of a
power system is performed by agent communication, the (n,k)-star
interconnected network topology is studied and modeled in this
paper to optimize the process of power reconfiguration. In this paper,
we discuss the recently proposed (n,k)-star topology and examine its
properties and advantages as compared to the traditional multi-bus
power topologies. We design and simulate the topology model for
distributed power system test cases. A related lemma based on the
fault tolerance and conditional diagnosability properties is presented
and proved both theoretically and practically. The conclusion is
reached that (n,k)-star topology model has measurable advantages
compared to standard bus power systems while exhibiting fault
tolerance properties in power restoration, as well as showing
efficiency when applied to power system route discovery.
Abstract: Frequency stability of microgrids under islanded
operation attracts particular attention recently. A new cooperative
frequency control strategy based on centralized multi-agent system
(CMAS) is proposed in this study. Based on this strategy, agents sent
data and furthermore each component has its own to center operating
decisions (MGCC).After deciding on the information, they are
returned. Frequency control strategies include primary and secondary
frequency control and disposal of multi-stage load in which this study
will also provide a method and algorithm for load shedding. This
could also be a big problem for the performance of micro-grid in
times of disaster. The simulation results show the promising
performance of the proposed structure of the controller based on
multi agent systems.
Abstract: In this paper, we propose a multi-agent intelligent
system that is used for monitoring the health conditions of elderly
people. Monitoring the health condition of elderly people is a
complex problem that involves different medical units and requires
continuous monitoring. Such expert system is highly needed in rural
areas because of inadequate number of available specialized
physicians or nurses. Such monitoring must have autonomous
interactions between these medical units in order to be effective. A
multi-agent system is formed by a community of agents that
exchange information and proactively help one another to achieve the
goal of elderly monitoring. The agents in the developed system are
equipped with intelligent decision maker that arms them with the
rule-based reasoning capability that can assist the physicians in
making decisions regarding the medical condition of elderly people.
Abstract: The cloud computing is an innovative paradigm that introduces several changes in technology that have resulted a new ways for cloud providers to deliver their services to cloud consumers mainly in term of security risk assessment, thus, adapting a current risk assessment tools to cloud computing is a very difficult task due to its several characteristics that challenge the effectiveness of risk assessment approaches. As consequence, there is a need of risk assessment model adapted to cloud computing. This paper requires a new risk assessment model based on multi-agent system and AHP model as fundamental steps towards the development of flexible risk assessment approach regarding cloud consumers.
Abstract: Imperial competitive algorithm (ICA) simulates a multi-agent algorithm. Each agent is like a kingdom has its country, and the strongest country in each agent is called imperialist, others are colony. Countries are competitive with imperialist which in the same kingdom by evolving. So this country will move in the search space to find better solutions with higher fitness to be a new imperialist. The main idea in this paper is using the peculiarity of ICA to explore the search space to solve the kinds of combinational problems. Otherwise, we also study to use the greed search to increase the local search ability. To verify the proposed algorithm in this paper, the experimental results of traveling salesman problem (TSP) is according to the traveling salesman problem library (TSPLIB). The results show that the proposed algorithm has higher performance than the other known methods.
Abstract: Perfectly suited for natural or man-made emergency and disaster management situations such as flood, earthquakes, tornadoes, or tsunami, multi-target search path planning for a team of rescue agents is known to be computationally hard, and most techniques developed so far come short to successfully estimate optimality gap. A novel mixed-integer linear programming (MIP) formulation is proposed to optimally solve the multi-target multi-agent discrete search and rescue (SAR) path planning problem. Aimed at maximizing cumulative probability of successful target detection, it captures anticipated feedback information associated with possible observation outcomes resulting from projected path execution, while modeling agent discrete actions over all possible moving directions. Problem modeling further takes advantage of network representation to encompass decision variables, expedite compact constraint specification, and lead to substantial problem-solving speed-up. The proposed MIP approach uses CPLEX optimization machinery, efficiently computing near-optimal solutions for practical size problems, while giving a robust upper bound obtained from Lagrangean integrality constraint relaxation. Should eventually a target be positively detected during plan execution, a new problem instance would simply be reformulated from the current state, and then solved over the next decision cycle. A computational experiment shows the feasibility and the value of the proposed approach.
Abstract: This paper deals with a stabilization problem for
multi-agent systems, when all agents in a multi-agent system receive
the same broadcasting control signal and the controller can measure
not each agent output but the sum of all agent outputs. It is
analytically shown that when the sum of all agent outputs is bounded
with a certain broadcasting controller for a given reference, each agent
output is separately bounded: stabilization of the sum of agent outputs
always results in the stability of every agent output. A numerical
example is presented to illustrate our theoretic findings in this paper.
Abstract: This paper studies a robust stabilization problem of a
single agent in a multi-agent consensus system composed of identical
agents, when the network topology of the system is completely
unknown. It is shown that the transfer function of an agent in a
consensus system can be described as a multiplicative perturbation
of the isolated agent transfer function in frequency domain. From an
existing robust stabilization result, we present sufficient conditions for
a robust stabilization of an agent against unknown network topology.
Abstract: The paper discusses the design of a .NET Windows Service based agent system called MACS (Multi-Agent Classification System). MACS is a system aims to accurately classify spreadsheet developers competency over a network. It is designed to automatically and autonomously monitor spreadsheet users and gather their development activities based on the utilization of the software multi-agent technology (MAS). This is accomplished in such a way that makes management capable to efficiently allow for precise tailor training activities for future spreadsheet development. The monitoring agents of MACS are intended to be distributed over the WWW in order to satisfy the monitoring and classification of the multiple developer aspect. The Prometheus methodology is used for the design of the agents of MACS. Prometheus has been used to undertake this phase of the system design because it is developed specifically for specifying and designing agent-oriented systems. Additionally, Prometheus specifies also the communication needed between the agents in order to coordinate to achieve their delegated tasks.