Centering ontologies in agent oriented software engineering processes

Beydoun, Ghassan; Low, Graham

doi:10.1007/s40747-016-0025-5

Cited by 2 publications

(2 citation statements)

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“…The benefits of bringing agents and ontologies together for boosting the engineering of a complex and distributed system were clear since the dawning of the AOSE research field as well. A huge body of work on ontology‐based AOSE exists starting with early proposals for ‘Integrating Ontologies into Multi‐Agent Systems Engineering’ and reaching mature and recent works on ‘Centering Ontologies in Agent‐Oriented Software Engineering Processes’ , and for ‘Supporting Agent‐Oriented Requirement Analysis with Ontologies’ . These works, together with many others produced between 2002 and today such as , advocate the use of ontologies as central constructs to drive the Software Development Life Cycle of agent‐based systems, in order to enhance reuse and promote modularity and interoperability.…”

Section: Related Workmentioning

confidence: 99%

Agent‐oriented and ontology‐driven digital libraries: the IndianaMAS experience

et al. 2017

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This paper describes IndianaMAS, a multiagent system able to automatically classify and manage images, sketches, and multilingual documents in a cultural heritage domain. The latter has been formalized by means of an ontology, which enables the semantic integration of heterogeneous data from different sources, drives the agent communication with the internal and external environment, and provides an abstract and human- readable interface between the system and the user. IndianaMAS is able to expose to the world the classified data via a digital library. Modularity and reusability are the key engineering principles followed in the system design and implementation. We present the details of the IndianaMAS system and discuss how its archi- tecture can be generalized to create – with the minimal effort – systems addressing similar classification, storage, and management problems, but operating in different domains and driven by different ontologies. The concrete problems we faced and their solutions are described to share our lesson learned and, at the same time, to show the applicability and reusability of our modular approach based on ontologies, agents, and digital libraries

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Section: Related Workmentioning

confidence: 99%

Agent‐oriented and ontology‐driven digital libraries: the IndianaMAS experience

et al. 2017

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“…Moreover, they provide agents with intelligence, reasoning, and support the main four characteristics of agents which are autonomy, social ability, reactivity, and proactiveness [24].…”

Section: Introductionmentioning

confidence: 99%

BOEM: A Model for Automating Detection and Evolution of Distributed Ontologies in Multi-Agent Environment

Soliman¹,

Salah²,

Hefny³

2017

IJIES

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Knowledge gives a strong support to autonomous agents in multi-agent systems and thus the evolution of agent's knowledge needs a great attention since it has a control on agents' behaviors and has effect on their decisions making. The problem is to allow agents to detect and decide whether they need more domain knowledge and allow their knowledge to evolve consistently and automatically. This paper utilizes ontologies to represent the internal knowledge of agents instead of utilizing them only as a shared conceptualization. Consequently, the paper proposes a model of bottom-up instance-driven ontology evolution that allows the internal ontologies of agents to evolve automatically and consistently in run time based on agents' interactions. Experiments are designed and implemented to evaluate our model in different situations. One of its results shows that an empty internal ontology of one agent could evolve automatically in runtime by 88.3% through its interactions with other agents. Moreover, a comparison between the proposed approach and literature review approaches is presented to compare between their different features and techniques. This paper is considered a step forward to automate ontology evolution for agents in multiagent environment.

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Centering ontologies in agent oriented software engineering processes

Cited by 2 publications

References 23 publications

Agent‐oriented and ontology‐driven digital libraries: the IndianaMAS experience

Agent‐oriented and ontology‐driven digital libraries: the IndianaMAS experience

BOEM: A Model for Automating Detection and Evolution of Distributed Ontologies in Multi-Agent Environment

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