By Radovan Cervenka
Multi-agent platforms are already a spotlight of reviews for greater than 25 years. regardless of immense attempt of an lively study neighborhood, modeling of multi-agent platforms nonetheless lacks whole and correct definition, basic reputation, and useful program. as a result of big strength of those structures e.g., to enhance the perform in software program and to volume the functions which may feasibly be tackled, this ebook attempts to supply a accomplished modeling language - the Agent Modeling Language (AML) - as an extension of UML 2.0, focusing on multi-agent platforms and purposes.
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Extra resources for The Agent Modeling Language - AML: A Comprehensive Approach to Modeling Multi-Agent Systems
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G. g. DAI [152], BDI [119], CDL [155], SMART [35] Tab. 0 Superstructure [104], augmenting it with base several new modeling concepts appropriate for capturing typical features of multi-agent systems. The main advantages of this approach are: Reuse of well-defined, well-founded, and commonly used concepts of UML. Use of existing mechanisms for specifying and extending UMLbased languages (metamodel extensions and UML profiles). Ease of incorporation into existing UML-based CASE tools. Structure of AML is defined at two distinct levels—AML Metamodel and Notation AML and AML Profiles.
Structure of AML is defined at two distinct levels—AML Metamodel and Notation AML and AML Profiles. Fig. 0. 0 Superstructure AML Notation AML Metamodel and Notation UML Fig. 4-1 Levels of AML specification With reference to Fig. 0 Superstructure which defines the abstract syntax, semantics and notation of UML. AML uses this level as the foundation upon which to define MAS-specific modeling constructs. The AML Metamodel and Notation level defines the AML abstract syntax, semantics and notation. The AML Metamodel is further structured into two main packages: AML Kernel and UML Extension for AML.
3. AORML does not include the concept of goals which is fundamental in several other approaches, such as [39,166]. 4. AORML does not allow modeling of the proactive behavior of agents. This type of behavior, which is the focus of Artificial Intelligence approaches to agents, is based on action planning and plan execution for achieving goals. 7 Summary of Today’s MAS Modeling Languages Achieved In the last few years we were witnesses to rapid progress in the area of results MAS modeling. A considerably large number of agent-oriented modeling languages came into existence as reaction to the urgency of application of an organized way of development agent-based systems.