A Proposed Model of an Intelligent Software Agent for Marketing Education (ISAME)

2021 ◽  
pp. 529-537
Author(s):  
Hussein Moselhy Sayed Ahmed
Keyword(s):  
Software Agent ◽  
Interactive Software ◽  
Initial Test ◽  
Training And Education ◽  
Agent Based ◽  
Marketing Education ◽  
Education Resources ◽  
Intelligent Software ◽  
Proposed Model ◽  
Digital Environments

The purpose of this article is to illustrate the advantages of intelligent software agent technologies in order to facilitate the location and customization of appropriate marketing education resources, as well as to foster collaboration between individuals within digital environments. In order to do this, this article discusses how such intelligent and interactive software can translate into a better educational environment for marketing curriculum, particularly e-marketing courses. The authors present a conceptual model for managing marketing training and education using intelligent software agent, based on extant literature. So, this article presents some initial test of the proposed model of ISAME usage in marketing education in e-marketing class.

A Proposed Model of an Intelligent Software Agent for Marketing Education (ISAME)

2018 ◽  
Vol 1 (1) ◽  
pp. 20-30
Author(s):  
Hussein Moselhy Sayed Ahmed
Keyword(s):  
Educational Environment ◽  
Software Agent ◽  
Interactive Software ◽  
Training And Education ◽  
Agent Based ◽  
Marketing Education ◽  
Education Resources ◽  
Intelligent Software ◽  
Proposed Model ◽  
Digital Environments

The purpose of this article is to illustrate the advantages of intelligent software agent technologies in order to facilitate the location and customization of appropriate marketing education resources, as well as to foster collaboration between individuals within digital environments. In order to do this, this article discusses how such intelligent and interactive software can translate into a better educational environment for marketing curriculum, particularly e-marketing courses. The authors present a conceptual model for managing marketing training and education using intelligent software agent, based on extant literature. So, this article presents some initial test of the proposed model of ISAME usage in marketing education in e-marketing class.

Functional Safety of Distributed Embedded Control Systems

2012 ◽  
pp. 132-170 ◽  
Author(s):  
Atef Gharbi ◽  
Hamza Gharsellaoui ◽  
Mohamed Khalgui ◽  
Samir Ben Ahmed
Keyword(s):  
Control Systems ◽  
Distributed Control ◽  
Software Agent ◽  
Software Components ◽  
Functional Safety ◽  
Embedded Control ◽  
Agent Based ◽  
Embedded Control Systems ◽  
Intelligent Software ◽  
Execution Environment

This chapter deals with the functional safety of distributed embedded control systems following the component-based approach. The authors define a new concept of components called “Control Component” (CC) to cover all of the used technologies in industry. To guarantee the functional safety of distributed control software components, the authors define an agent-based architecture where an intelligent software agent is deployed in a device of the execution environment in order to apply local reconfiguration scenarios, and a coordination agent is used for inter-devices coordination in order to allow coherent reconfigurations.

scholarly journals A Novel Crowdsourcing Model for Micro-Mobility Ride-Sharing Systems

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4636
Author(s):  
Mohammed Elhenawy ◽  
Mostafizur R. Komol ◽  
Mahmoud Masoud ◽  
Shiqiang Liu ◽  
Huthaifa I. Ashqar ◽  
...  
Keyword(s):  
User Needs ◽  
Computational Results ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Ride Sharing ◽  
Proposed Model ◽  
Micro Mobility

Substantial research is required to ensure that micro-mobility ride sharing provides a better fulfilment of user needs. This study proposes a novel crowdsourcing model for the ride-sharing system where light vehicles such as scooters and bikes are crowdsourced. The proposed model is expected to solve the problem of charging and maintaining a large number of light vehicles where these efforts will be the responsibility of the crowd of suppliers. The proposed model consists of three entities: suppliers, customers, and a management party responsible for receiving, renting, booking, and demand matching with offered resources. It can allow suppliers to define the location of their private e-scooters/e-bikes and the period of time they are available for rent. Using a dataset of over 9 million e-scooter trips in Austin, Texas, we ran an agent-based simulation six times using three maximum battery ranges (i.e., 35, 45, and 60 km) and different numbers of e-scooters (e.g., 50 and 100) at each origin. Computational results show that the proposed model is promising and might be advantageous to shift the charging and maintenance efforts to a crowd of suppliers.

Software agent-based directed diffusion in wireless sensor network

2008 ◽  
Vol 38 (3-4) ◽  
pp. 161-174 ◽  
Author(s):  
Elhadi Shakshuki ◽  
Haroon Malik ◽  
Mieso K. Denko
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Software Agent ◽  
Wireless Sensor ◽  
Directed Diffusion ◽  
Agent Based

scholarly journals A strategy learning model for autonomous agents based on classification

2015 ◽  
Vol 25 (3) ◽  
pp. 471-482 ◽  
Author(s):  
Bartłomiej Śnieżyński
Keyword(s):  
Reinforcement Learning ◽  
Supervised Learning ◽  
Learning Process ◽  
Autonomous Agents ◽  
Good Alternative ◽  
Learning Model ◽  
Learning Method ◽  
Complex Environments ◽  
Agent Based ◽  
Proposed Model

AbstractIn this paper we propose a strategy learning model for autonomous agents based on classification. In the literature, the most commonly used learning method in agent-based systems is reinforcement learning. In our opinion, classification can be considered a good alternative. This type of supervised learning can be used to generate a classifier that allows the agent to choose an appropriate action for execution. Experimental results show that this model can be successfully applied for strategy generation even if rewards are delayed. We compare the efficiency of the proposed model and reinforcement learning using the farmer-pest domain and configurations of various complexity. In complex environments, supervised learning can improve the performance of agents much faster that reinforcement learning. If an appropriate knowledge representation is used, the learned knowledge may be analyzed by humans, which allows tracking the learning process

scholarly journals Agent Based Load Balancing in Grid Computing

2020 ◽  
Author(s):  
Wided Ali ◽  
Fatima Bouakkaz
Keyword(s):  
Load Balancing ◽  
Heterogeneous Systems ◽  
Agent Based ◽  
Selection Policy ◽  
Development Framework ◽  
Policy Selection ◽  
Proposed Model ◽  
Multi Agent ◽  
Location Policy ◽  
System Characteristics

Load-Balancing is an important problem in distributed heterogeneous systems. In this paper, an Agent-based load-balancing model is developed for implementation in a grid environment. Load balancing is realized via migration of worker agents from overloaded resources to underloaded ones. The proposed model purposes to take benefit of the multi-agent system characteristics to create an autonomous system. The Agent-based load balancing model is implemented using JADE (Java Agent Development Framework) and Alea 2 as a grid simulator. The use of MAS is discussed, concerning the solutions adopted for gathering information policy, location policy, selection policy, worker agents migration, and load balancing.

scholarly journals AN INTEGRATED FRAMEWORK UTILISING SOFTWARE AGENT REASONING AND ONTOLOGY MODELS FOR SENSOR BASED BUILDING MONITORING

2015 ◽  
Vol 21 (3) ◽  
pp. 356-375 ◽  
Author(s):  
Michael Dibley ◽  
Haijiang Li ◽  
Yacine Rezgui ◽  
John Miles
Keyword(s):  
Decision Making ◽  
Sensor Network ◽  
Explicit Knowledge ◽  
Knowledge Bases ◽  
Software Agent ◽  
Facility Management ◽  
Good Opportunity ◽  
Integrated Framework ◽  
Intelligent Software ◽  
Building Monitoring

Smart building monitoring demands a new software infrastructure that can elaborate building domain knowledge in order to provide advanced and intelligent functionalities. Conventional facility management (FM) software tools lack semantically rich components, and that limits the capability of supporting software for automatic information sharing, resource negotiation and to assist in timely decision making. Recent hardware innovation on compact ZigBee sensor devices, software developments on ontology and intelligent software agent paradigms provide a good opportunity to develop tools that can further improve current FM practices. This paper introduces an integrated framework which includes a ZigBee based sensor network and underlying multi-agent software (MAS) components. Several different types of sensors were integrated with the ZigBee host devices to produce compact multi-functional sensor units. The MAS framework incorporates the belief-desire-intention (BDI) abstraction with ontology support (provided via explicit knowledge bases). The different software agent types have been developed to work with sensor hardware to conduct resource negotiation, to optimize battery utilization, to monitor building space in a non-intrusive way and to reason about its usage through real time ontology model queries. The deployed sensor network shows promising intelligent characteristics, and it has been applied in several on-going research projects as an underlying decision making service. More applications and larger deployments have been planned for future work.

Adaptive Ensemble Multi-Agent Based Intrusion Detection Model

2010 ◽  
pp. 36-48 ◽  
Author(s):  
Tarek Helmy
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Agent Based ◽  
Detection Model ◽  
Detection Analysis ◽  
Proposed Model ◽  
Multi Agent

The system that monitors the events occurring in a computer system or a network and analyzes the events for sign of intrusions is known as intrusion detection system. The performance of the intrusion detection system can be improved by combing anomaly and misuse analysis. This chapter proposes an ensemble multi-agent-based intrusion detection model. The proposed model combines anomaly, misuse, and host-based detection analysis. The agents in the proposed model use rules to check for intrusions, and adopt machine learning algorithms to recognize unknown actions, to update or create new rules automatically. Each agent in the proposed model encapsulates a specific classification technique, and gives its belief about any packet event in the network. These agents collaborate to determine the decision about any event, have the ability to generalize, and to detect novel attacks. Empirical results indicate that the proposed model is efficient, and outperforms other intrusion detection models.

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