Behrouz H. Far

Model based detection of implied scenarios in multi agent systems

Multi-agent systems (MAS) are efficient solutions for commercial applications such as information retrieval and search. In a MAS, agents are usually designed with distribution of functionality and control. Lack of central control implies that the quality of service of MAS may be degraded because of possible unwanted behavior at the runtime, commonly known as emergent behavior. Detecting and removing emergent behavior during the design phase of MAS will lead to huge savings in deployment costs of such systems. An effective approach for the MAS design is to describe system requirements using scenarios. A scenario, commonly known as a message sequence chart or a sequence diagram, is a temporal sequence of messages sent between agents. In this paper a method for detecting emergent behavior of MAS by detecting incompleteness and partial description of scenarios is proposed. The method is explained along with a prototype MAS for semantic search that blends the search and ontological concept learning.

Detecting emergent behavior in distributed systems using an ontology based methodology

Lack of central control makes the design of distributed software systems a challenging task because of possible unwanted behavior at runtime, commonly known as emergent behavior. Developing methodologies to detect emergent behavior prior to the implementation stage of the system can lead to huge savings in time and cost. However manual review of requirements and design documents for real-life systems is inefficient and error prone; thus automation of analysis methodologies is considered greatly beneficial. This paper proposes the utilization of an ontology-based approach to analyze system requirements expressed by a set of message sequence charts (MSC). This methodology involves building a domain-specific ontology of the system, and examines the requirements based on this ontology. The advantages of this approach in comparison with other methodologies are its consistency and increased level of automation. The effectiveness of this approach is explained using a case study of an IntelliDrive system.

Automated ontology construction from scenario based software requirements using clustering techniques

Ontologies have been utilized in many different areas of software engineering. As software systems grow in size and complexity, the need to devise methodologies to manage the amount of information and knowledge becomes more apparent. Utilizing ontologies in requirement elicitation and analysis is very practical as they help to establish the scope of the system and facilitate information reuse. Moreover ontologies can serve as a natural bridge to transition from the requirements gathering stage to designing the architecture for the system. However manual construction of ontologies is time consuming, error prone and subjective. Therefore it is greatly beneficial to devise automated methodologies which allow knowledge extraction from system requirements using an automated and systematic approach. This paper introduces an approach to systematically extract knowledge from system requirements to construct different views of ontologies for the system as a part of a comprehensive framework to analyze and validate software requirements and design.

Multi-Agent system for semantic web service composition

Agent-oriented analysis and design is a prosperous approach to model and build software systems. On the other hand, semantic web services are software components that have been emerging to enable dynamic service discovery, composition, invocation, and provide services, which can be considered as the main function of an agent. Semantic web service composition is a feature that improves the flexibility of the system. In this paper we propose a multi-agent system for web service composition. We modeled it by MaSE (Multi-agent System Engineering) methodology, which is a top-down approach. Also an implementation of our system is illustrated for semantic search engines. The case study shows that our system is feasible and effective for composition of semantic web services in a distributed network.

A method to detect and remove emergent behavior caused by overgeneralization

Emergent behavior in distributed systems is a central problem that may lead to unexpected behaviors and major faults. Emergent behaviors are usually categorized into three groups: emergent behaviors occur due to scenarios incompleteness; emergent behaviors as a result of violation of a system wide policy; and emergent behaviors as a result of synthesis of behavior models. In this paper, a technique for addressing the latter group is proposed. The technique prevents from overgeneralization in the behavior model synthesis. Overgeneralization happens as the result of behavior model synthesis and depends on the assumptions of the process. In addition, the proposed technique addresses the issue of the existing ad-hoc methodologies by providing an automated algorithm. This algorithm can be used by a syntax checker to automatically detect and correct the emergent behaviors in the scenarios. The proposed algorithm is validated using a case study of a fleet management system.

Model based analysis of internet filtering systems

Network filters are designed and deployed to enforce restrictions for a variety of political, social, economical or merely security reasons. However, network filters can potentially be violated by attackers and thus may not be completely adequate to fulfill their tasks. This paper approaches the network filtering from a software engineering perspective by utilizing an automated method to analyze the correctness of the requirements of the filtering mechanisms, and to reduce their vulnerability. The filtering requirements are expressed using scenario-based specifications. The set of scenarios are automatically analyzed for illegal behaviors. To demonstrate the effectiveness of this approach, a real-life internet filtering system is used as a case study.