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For example, many agents-based implementations of many different aspects of e-commerce systems have been reported in the literature 15 , 16 , 17 , 18 , 19 , In other words, an agent is a set of computers environment that is responsible for one or more specific kind of tasks.

Many definitions of the properties and functionality of an agent exist. Wooldridge and Jennings 22 describe agents as problem solving entities that have autonomy, social ability, pro-activeness and responsiveness. Autonomy is the ability of an agent to act on its own without any interference from users or other agents on its prescribed tasks. Social ability allows the agent to communicate with other agents or users.

Pro-activeness refers to the ability of an agent to initiate some action when appropriate. Finally, responsiveness implies that agents understand their environment and can react accordingly to changes in it. Christoffel et al. Similar properties of agents are available 19 , 24 , 25, 26 in the literature. According to Guttman and Maes 27 and Maes et al. The flexibility and enormous capabilities of agents make them suitable technology for e-commerce transactions negotiations We do not examine the features of agents in this paper beyond that necessary to model e-commerce transactions.

We draw on available agents communication theory in the literature. It suffices, however, to note that multi-agents systems focus on cooperation and collaboration, joint goals and plans, and information sharing. This is synonymous to the phenomenon in the termite colony 15 model. A multi-agent system consists of multiple agents. Although agents are autonomous, in order to accomplish complex tasks, they need to communicate with one another and cooperate together.

The advantage of using multi-agents is that it offers a bouquet of multiple solutions and multiple services. For every communication, there is one agent sending request called requestAgent and multiple agents serving the request called serviceAgent. The requestAgent may broadcast its request with the service it is expecting, then serviceAgents receive the request, based on the parameter of the request, they check their available service, if their service meets the request, they respond to the requestAgent.

Improving Multi-Agent Architectural Design

The requestAgent may receive multiple replies from the serviceAgents; however, it chooses the most appropriate serviceAgent to perform its task. Our multi-agents system architecture draws from the knowledge and theories of service oriented architecture see Fig. Our architecture uses the simple object access protocol SOAP standard a message exchange standard that supports service communication and web service definition language WSDL standard that allows a service interface and its binding definitions.

The binding maps the abstract interface to a set of protocols that specifies how or rules to communicate with a web service, a fundamental ingredient of web-based applications, including e-commerce systems. In most cases, this request-and-reply communication is not done directly between requestAgent and serviceAgent. A broker is introduced to deal with the multiple requests and multiple services.

At the beginning, every serviceAgent registers its available services including name, OS, reliability, QoS, available time, etc. When requestAgent has a task with some parameters intended OS, reliability, etc. Thereafter, the requestAgent could communicate with the selected serviceAgent directly. The introduction of the broker simplifies the communication between requestAgent and serviceAgent, and reduces communication time and overhead. For a detailed examination of multi-agents in e-commerce environments, interested readers should see the following resources 15 , 28, As we mentioned in Subsections 3.

Communication among multiple agents requires a framework and protocol that governs how agents interact with one another. The agent communication language provides the framework and protocol for interactions among agents. Communication between any two agents is akin to communication between two networks at the same network level, both ends should follow the same standard protocol. Knowledge Query and Manipulation Language KQML is a popular agent communication language and protocol for exchanging information and knowledge.

KQML is both a message format and a message-handling protocol to support run-time knowledge sharing among agents Examples of such primitives are: Advertise: service agents register services with broker agent Ask request agent requests service agents to perform a task Tell: provide other agents with its service information Monitor: watch another agent for a particular condition ….

For example, a message representing a query about price of Air Canada tickets might be encoded 33 as: ask. In this paper, we focus on the application layer, thus we do not examine the details of how the agent communication language is implemented and linked to lower layers middleware and operating system. To simplify our discourse, we introduce Notation Agent Language NAL to describe the communication between multiple agents.

The NAL is flexible and amenable to use for formal modeling of agents-based systems see Fig.

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We extended its basic primitives to support real-time features to suite the modeling and specification of the real-time multi-agents systems. See Section 3. A real-time system is a system where the correct functioning of the system depends on the results produced by the system and the time at which these results are produced Hard real-time systems , where operation is incorrect if results are not produced according to the timing constraint specification.

In this paper, we focus on soft real-time systems. When the server is unable to finish its tasks in the specified time period, the operation is degraded; that is, the QoS is degraded. As discussed in Section 1 , many e-commerce applications rely on real-time features, so they require service provisioning in real-time; otherwise the QoS will degrade significantly.

These specifications are called timing constraints. By adding timing constraints to the request, the broker could find the appropriate serviceAgent to perform the job efficiently. The timing constraints could be expressed in different languages. DiPippo et al 33 extended KQML performatives with expressions of time for both specification of timing capabilities and constraints. An example of such extension, following the style provided 33 , is given below: ask. The broker compares the two parameters of the serviceAgent and requestAgent to determine if the following condition is supported: RTAsk.


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That means the serviceAgent could perform the service asked by the requestAgent. In this way, the broker would find the best service for the requester and ensures the guarantee of real-time service provisioning. In this section, we describe the methodology and design of our model of real-time multi-agent architecture for e-commerce servers. We adopt a very simple working example to illustrate the problems in the current architectures; however, it is expressive enough to provide the solution directions and motivation for our design.

When the request management module and resource management module receive requests, they pass those requests to reasoning module by utilizing the information obtained from the knowledge base and the confidentiality policy rule Talib et al. Main responsibility of this agent is to perform various block-level operations and generate a correctness assurance when the cloud user performs update operation, delete operation, append to modify operation or insert operation.

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A Reference Architecture for Situated Multiagent Systems

Cloud Coordination Module provides the agent with the following mechanisms. If the data is updated then the data encryption is performed. If the data is deleted then the data encryption is performed. If the data is Append then the data encryption is performed. If the data is inserted then the data encryption is performed. Cloud Reasoning Module calculates the necessary amount of cloud resources to complete the service based on the required Service Level Agreements SLA by utilizing the information obtained from the knowledge base and the correctness assurance policy rule.

Therefore, it is crucial to consider the dynamic case, where a cloud user may wish to perform various block-level operations of update, delete and append to modify the data. Our proposed correctness assurance protocol is not going to be genuine if there is absent of SecureFormula.

Sainbayar Sukhbaatar: "Deep Architecture for Sets and Its Application to Multi-agent Communication"

CDAA facilitate two new techniques of file distribution preparation and file retrieval Talib et al. Cloud data availability is to ensure that the cloud data processing resources are not made unavailable by malicious action.

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Our MAS architecture is able to tolerate multiple failures in cloud distributed storage systems. To ensure the availability, we explain the notions of global and local cloud attack blueprints.

To detect intrusions, the CDAA receives a set of goals representing the global cloud attack blueprints. To recognize this global cloud attack blueprint, it must be decomposed in local cloud sub-blueprints used locally by the different agents distributed in the CDS. In general agents can detect only local cloud attacks because they have a restricted view of the CDS. So, we make a distinction between a global cloud attack blueprint and local cloud sub-blueprints.