In multi-agent and multi-robot systems (MRS), communication and memory play a crucial role in ensuring reliable knowledge and information exchange among agents. However, as these systems become more complex and distributed, challenges arise in managing memory and communication capabilities. Existing ontological frameworks have helped represent higher-level knowledge and facilitate various knowledge operations, but they still face two major challenges: the knowledge-control gap and the knowledge-control delay.
The knowledge-control gap refers to the mismatch between the knowledge represented in an ontology and the actual complexity of the robot’s environment. This can lead to unexpected behaviors in robots and limit their ability to make accurate decisions. To bridge this gap, it is essential to continuously refine the ontologies used by robots by incorporating information about their environment and other agents. However, these ontologies may be limited by their expressiveness at the control level, making it challenging to capture the complexity and nuances that directly relate to their functional knowledge, such as collision avoidance.
Creating ontologies requires a clear definition of concepts, which demands domain expertise and knowledge of tools used in ontology creation. Moreover, studying various types of studies conducted can provide valuable insights into the effect of opportunities on knowledge spread and updates, as well as the comparison of target collection rate over time for different modalities.
In conclusion, addressing memory and communication challenges is crucial to ensure reliable knowledge and information exchange in MRS. Continuously refining ontologies used by robots can help bridge the knowledge-control gap, but it also requires careful consideration of the expressiveness at the control level. By demystifying complex concepts and using everyday language, this summary aims to provide a comprehensive understanding of the article’s key points without oversimplifying the information.
Designing Behavior Trees for Collective Intelligence in Multi-Robot Systems
