Intelligent Software Agent Design Tool Using Goal Net Methodology

Author(s):  
Han Yu ◽  
Zhiqi Shen ◽  
Chunyan Miao
2015 ◽  
Vol 21 (3) ◽  
pp. 356-375 ◽  
Author(s):  
Michael Dibley ◽  
Haijiang Li ◽  
Yacine Rezgui ◽  
John Miles

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.


Author(s):  
Hussein Moselhy Sayed Ahmed

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.


2018 ◽  
Vol 1 (1) ◽  
pp. 20-30
Author(s):  
Hussein Moselhy Sayed Ahmed

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.


2011 ◽  
pp. 104-112 ◽  
Author(s):  
Mahesh S. Raisinghani ◽  
Christopher Klassen ◽  
Lawrence L. Schkade

Although there is no firm consensus on what constitutes an intelligent agent (or software agent), an intelligent agent, when a new task is delegated by the user, should determine precisely what its goal is, evaluate how the goal can be reached in an effective manner, and perform the necessary actions by learning from past experience and responding to unforeseen situations with its adaptive, self-starting, and temporal continuous reasoning strategies. It needs to be not only cooperative and mobile in order to perform its tasks by interacting with other agents but also reactive and autonomous to sense the status quo and act independently to make progress towards its goals (Baek et al., 1999; Wang, 1999). Software agents are goal-directed and possess abilities such as autonomy, collaborative behavior, and inferential capability. Intelligent agents can take different forms, but an intelligent agent can initiate and make decisions without human intervention and have the capability to infer appropriate high-level goals from user actions and requests and take actions to achieve these goals (Huang, 1999; Nardi et al., 1998; Wang, 1999). The intelligent software agent is a computational entity than can adapt to the environment, making it capable of interacting with other agents and transporting itself across different systems in a network.


Author(s):  
Shashank Sahu ◽  
Rashi Agarwal ◽  
Rajesh Kumar Tyagi

Author(s):  
Shu-Heng Chen ◽  
Shu G. Wang

Recently, the relation between neuroeconomics and agent-based computational economics (ACE) has become an issue concerning the agent-based economics community. Neuroeconomics can interest agent-based economists when they are inquiring for the foundation or the principle of the software-agent design, normally known as agent engineering. It has been shown in many studies that the design of software agents is non-trivial and can determine what will emerge from the bottom. Therefore, it has been quested for rather a period regarding whether we can sensibly design these software agents, including both the choice of software agent models, such as reinforcement learning, and the parameter setting associated with the chosen model, such as risk attitude. In this chapter, we shall start a formal inquiry by focusing on examining the models and parameters used to build software agents.


2012 ◽  
Vol 433-440 ◽  
pp. 6693-6701
Author(s):  
Steve Thatcher ◽  
Kavyaganga Kilingaru

When a flight crew has situation awareness they have a complete and accurate understanding of the physical, temporal and emotional environments in which they are situated. This allows the flight crew to interpret and evaluate elements or events in the environment in which they are situated and determine the risks associated with these events and an appropriate strategy to minimize and manage these risks. This paper describes the architecture for an intelligent software agent which assesses a flight crew’s situation awareness through the observation of a pilot’s eye movements. The agent perceives pilot behavior using a proprietary eye tracking device. This behavior is compared to a behavior database to decide whether the behavior of the pilot is appropriate or inappropriate in terms of the safety of the flight. The flight crew is alerted if the behavior is judged to be consistent with the flight crew losing situation awareness.


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