Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Infraworld is an experimental framework for Computer Aided Engineering (CAE) systems which is designed for distributed design. The framework is based on Multi-agent systems that allow engineers to synchronize their work by keeping track of their changes and facilitating the detection and management of semantic conflicts that arise when different actors are working in parallel. Conflicts are detected according of each engineers semantics which are defined by using OWL ontologies and SWRL rules. When they are detected, the framework allows solving them by negotiating possible alternatives. Then the alternatives are evaluated by expressing preferences and the picked alternative, being the one that maximizes the global lfare, is applied in all the models in the distributed environment. The system is completed with a machine learning module that allows the agents to suggest similar solutions to future conflicts with similar semantic context.
['Jaume Domínguez Faus', 'Francisco Grimaldo']
I am writing a research paper on a idea Infraworld, a multi-agent based framework to assist in civil infrastructure collaborative design (demonstration)
[{"from": "human", "value": "I am writing a research paper on a idea Infraworld, a multi-agent based framework to assist in civil infrastructure collaborative design (demonstration)"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Infraworld is an experimental framework for Computer Aided Engineering (CAE) systems which is designed for distributed design. The framework is based on Multi-agent systems that allow engineers to synchronize their work by keeping track of their changes and facilitating the detection and management of semantic conflicts that arise when different actors are working in parallel. Conflicts are detected according of each engineers semantics which are defined by using OWL ontologies and SWRL rules. When they are detected, the framework allows solving them by negotiating possible alternatives. Then the alternatives are evaluated by expressing preferences and the picked alternative, being the one that maximizes the global lfare, is applied in all the models in the distributed environment. The system is completed with a machine learning module that allows the agents to suggest similar solutions to future conflicts with similar semantic context."}]