Design an Electronic Market Framework Using JADE Environment
The daily growing use of agents in software environments, because of many reasons such as independence and intelligence is not a secret anymore. One of such environments in which there is a prominent job for the agents would be emarketplaces in which a user is able to give those agents the responsibility of buying and selling, instead of searching the emarketplace himself. Making up a framework which has sufficient attention to the required roles and their relations, is the first step of achieving such e-markets. In this paper, we suggest a framework in order to establish such e-markets and we will continue investigating the roles such as seller or buyer and the relations in JADE environment in details.
Framework, software agents, e-commerce, e-market.
Soft Real-Time Fuzzy Task Scheduling for Multiprocessor Systems
All practical real-time scheduling algorithms in multiprocessor systems present a trade-off between their computational complexity and performance. In real-time systems, tasks have to be performed correctly and timely. Finding minimal schedule in multiprocessor systems with real-time constraints is shown to be NP-hard. Although some optimal algorithms have been employed in uni-processor systems, they fail when they are applied in multiprocessor systems. The practical scheduling algorithms in real-time systems have not deterministic response time. Deterministic timing behavior is an important parameter for system robustness analysis. The intrinsic uncertainty in dynamic real-time systems increases the difficulties of scheduling problem. To alleviate these difficulties, we have proposed a fuzzy scheduling approach to arrange real-time periodic and non-periodic tasks in multiprocessor systems. Static and dynamic optimal scheduling algorithms fail with non-critical overload. In contrast, our approach balances task loads of the processors successfully while consider starvation prevention and fairness which cause higher priority tasks have higher running probability. A simulation is conducted to evaluate the performance of the proposed approach. Experimental results have shown that the proposed fuzzy scheduler creates feasible schedules for homogeneous and heterogeneous tasks. It also and considers tasks priorities which cause higher system utilization and lowers deadline miss time. According to the results, it performs very close to optimal schedule of uni-processor systems.
Computational complexity, Deadline, Feasible
scheduling, Fuzzy scheduling, Priority, Real-time multiprocessor
systems, Robustness, System utilization.
Emotional Learning based Intelligent Robust Adaptive Controller for Stable Uncertain Nonlinear Systems
In this paper a new control strategy based on Brain
Emotional Learning (BEL) model has been introduced. A modified
BEL model has been proposed to increase the degree of freedom,
controlling capability, reliability and robustness, which can be
implemented in real engineering systems.
The performance of the proposed BEL controller has been
illustrated by applying it on different nonlinear uncertain systems,
showing very good adaptability and robustness, while maintaining
Learning control systems, emotional decision
making, nonlinear systems, adaptive control.
Statistical Genetic Algorithm
Adaptive Genetic Algorithms extend the Standard Gas
to use dynamic procedures to apply evolutionary operators such as
crossover, mutation and selection. In this paper, we try to propose a
new adaptive genetic algorithm, which is based on the statistical
information of the population as a guideline to tune its crossover,
selection and mutation operators. This algorithms is called Statistical
Genetic Algorithm and is compared with traditional GA in some
Genetic Algorithms, Statistical Information ofthe Population, PAUX, SSO.
A Pattern Language for Software Debugging
In spite of all advancement in software testing,
debugging remains a labor-intensive, manual, time consuming, and
error prone process. A candidate solution to enhance debugging
process is to fuse it with testing process. To achieve this integration,
a possible solution may be categorizing common software tests and
errors followed by the effort on fixing the errors through general
solutions for each test/error pair. Our approach to address this issue is
based on Christopher Alexander-s pattern and pattern language
concepts. The patterns in this language are grouped into three major
sections and connect the three concepts of test, error, and debug.
These patterns and their hierarchical relationship shape a pattern
language that introduces a solution to solve software errors in a
known testing context.
Finally, we will introduce our developed framework ADE as a
sample implementation to support a pattern of proposed language,
which aims to automate the whole process of evolving software
design via evolutionary methods.
Coding Errors, Software debugging, Testing,
Patterns, Pattern Language
Memetic Algorithm Based Path Planning for a Mobile Robot
In this paper, the problem of finding the optimal collision free path for a mobile robot, the path planning problem, is solved using an advanced evolutionary algorithm called memetic algorithm. What is new in this work is a novel representation of solutions for evolutionary algorithms that is efficient, simple and also compatible with memetic algorithm. The new representation makes it possible to solve the problem with a small population and in a few generations. It also makes the genetic operator simple and allows using an efficient local search operator within the evolutionary algorithm. The proposed algorithm is applied to two instances of path planning problem and the results are available.
Path planning problem, Memetic Algorithm,Representation.
Designing a Football Team of Robots from Beginning to End
The Combination of path planning and path following is the main purpose of this paper. This paper describes the developed practical approach to motion control of the MRL small size robots. An intelligent controller is applied to control omni-directional robots motion in simulation and real environment respectively. The Brain Emotional Learning Based Intelligent Controller (BELBIC), based on LQR control is adopted for the omni-directional robots. The contribution of BELBIC in improving the control system performance is shown as application of the emotional learning in a real world problem. Optimizing of the control effort can be achieved in this method too. Next the implicit communication method is used to determine the high level strategies and coordination of the robots. Some simple rules besides using the environment as a memory to improve the coordination between agents make the robots' decision making system. With this simple algorithm our team manifests a desirable cooperation.
multi-agent systems (MAS), Emotional learning, MIMO system, BELBIC, LQR, Communication via environment
A New Method for Complex Goods Selection in Electronic Markets
After the development of the Internet a suitable
discipline for trading goods electronically has been emerged.
However, this type of markets is not still mature enough in order to
become independent and get closer to seller/buyer-s needs.
Furthermore, the buyable and sellable goods in these markets still
don-t have essential standards for being well-defined. In this paper,
we will present a model for development of a market which can
contain goods with variable definitions and we will also investigate
its characteristics. Besides, by noticing the fact that people have
different discriminations, it-s figured out that the significance of each
attribute of a specific product may vary from different people-s view
points. Consequently we-ll present a model for weighting and
accordingly different people-s view points could be satisfied. These
two aspects will be discussed completely throughout this paper.
Electronic markets, selection of multi attributegoods, data infusion.
Using Emotional Learning in Rescue Simulation Environment
RoboCup Rescue simulation as a large-scale Multi
agent system (MAS) is one of the challenging environments for
keeping coordination between agents to achieve the objectives
despite sensing and communication limitations. The dynamicity of
the environment and intensive dependency between actions of
different kinds of agents make the problem more complex. This point
encouraged us to use learning-based methods to adapt our decision
making to different situations. Our approach is utilizing
reinforcement leaning. Using learning in rescue simulation is one of
the current ways which has been the subject of several researches in
recent years. In this paper we present an innovative learning method
implemented for Police Force (PF) Agent. This method can cope
with the main difficulties that exist in other learning approaches.
Different methods used in the literature have been examined. Their
drawbacks and possible improvements have led us to the method
proposed in this paper which is fast and accurate. The Brain
Emotional Learning Based Intelligent Controller (BELBIC) is our
solution for learning in this environment. BELBIC is a
physiologically motivated approach based on a computational model
of amygdale and limbic system. The paper presents the results
obtained by the proposed approach, showing the power of BELBIC
as a decision making tool in complex and dynamic situation.
Emotional learning, rescue, simulation environment,
RoboCup, multi-agent system.
Anomaly Detection using Neuro Fuzzy system
As the network based technologies become
omnipresent, demands to secure networks/systems against threat
increase. One of the effective ways to achieve higher security is
through the use of intrusion detection systems (IDS), which are a
software tool to detect anomalous in the computer or network. In this
paper, an IDS has been developed using an improved machine
learning based algorithm, Locally Linear Neuro Fuzzy Model
(LLNF) for classification whereas this model is originally used for
system identification. A key technical challenge in IDS and LLNF
learning is the curse of high dimensionality. Therefore a feature
selection phase is proposed which is applicable to any IDS. While
investigating the use of three feature selection algorithms, in this
model, it is shown that adding feature selection phase reduces
computational complexity of our model. Feature selection algorithms
require the use of a feature goodness measure. The use of both a
linear and a non-linear measure - linear correlation coefficient and
mutual information- is investigated respectively
anomaly Detection, feature selection, Locally Linear
Neuro Fuzzy (LLNF), Mutual Information (MI), liner correlation
Optimized Data Fusion in an Intelligent Integrated GPS/INS System Using Genetic Algorithm
Most integrated inertial navigation systems (INS) and
global positioning systems (GPS) have been implemented using the
Kalman filtering technique with its drawbacks related to the need for
predefined INS error model and observability of at least four
satellites. Most recently, a method using a hybrid-adaptive network
based fuzzy inference system (ANFIS) has been proposed which is
trained during the availability of GPS signal to map the error
between the GPS and the INS. Then it will be used to predict the
error of the INS position components during GPS signal blockage.
This paper introduces a genetic optimization algorithm that is used to
update the ANFIS parameters with respect to the INS/GPS error
function used as the objective function to be minimized. The results
demonstrate the advantages of the genetically optimized ANFIS for
INS/GPS integration in comparison with conventional ANFIS
specially in the cases of satellites- outages. Coping with this problem
plays an important role in assessment of the fusion approach in land
Adaptive Network based Fuzzy Inference System
(ANFIS), Genetic optimization, Global Positioning System (GPS),
Inertial Navigation System (INS).
How Social Network Structure Affects the Dynamics of Evolution of Cooperation?
The existence of many biological systems,
especially human societies, is based on cooperative behavior
[1, 2]. If natural selection favors selfish individuals, then what
mechanism is at work that we see so many cooperative
behaviors? One answer is the effect of network structure. On a
graph, cooperators can evolve by forming network bunches
[2, 3, 4]. In a research, Ohtsuki et al used the idea of iterated
prisoners- dilemma on a graph to model an evolutionary
game. They showed that the average number of neighbors
plays an important role in determining whether cooperation is
the ESS of the system or not . In this paper, we are going to
study the dynamics of evolution of cooperation in a social
network. We show that during evolution, the ratio of
cooperators among individuals with fewer neighbors to
cooperators among other individuals is greater than unity. The
extent to which the fitness function depends on the payoff of
the game determines this ratio.
Evolution of cooperation, Iterated prisoner's
dilemma, Model dynamics, Social network structure, Intensity
An Innovative Fuzzy Decision Making Based Genetic Algorithm
Several researchers have proposed methods about
combination of Genetic Algorithm (GA) and Fuzzy Logic (the use of
GA to obtain fuzzy rules and application of fuzzy logic in
optimization of GA). In this paper, we suggest a new method in
which fuzzy decision making is used to improve the performance of
genetic algorithm. In the suggested method, we determine the alleles
that enhance the fitness of chromosomes and try to insert them to the
In this algorithm we try to present an innovative vaccination in the
process of reproduction in genetic algorithm, with considering the
trade off between exploration and exploitation.
Genetic Algorithm, Fuzzy Decision Making.
Relational Representation in XCSF
Generalization is one of the most challenging issues
of Learning Classifier Systems. This feature depends on the
representation method which the system used. Considering the
proposed representation schemes for Learning Classifier System, it
can be concluded that many of them are designed to describe the
shape of the region which the environmental states belong and the
other relations of the environmental state with that region was
ignored. In this paper, we propose a new representation scheme
which is designed to show various relationships between the
environmental state and the region that is specified with a particular
Classifier Systems, Reinforcement Learning,Relational Representation, XCSF.