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In recent years, the growing use of Intelligent Personal Agents in different human activities and in various domains led the corresponding research to focus on the design and development of agents that are not limited to interaction with humans and execution of simple tasks. The latest research efforts have introduced Intelligent Personal Agents that utilize Natural Language Understanding (NLU) modules and Machine Learning (ML) techniques in order to have complex dialogues with humans, execute complex plans of actions and effectively control smart devices. To this aim, this article introduces the second generation of the CERTH Intelligent Personal Agent (CIPA) which is based on the RASA framework and utilizes two machine learning models for NLU and dialogue flow classification. CIPA-Generation B provides a dialogue-story generator that is based on the idea of adjacency pairs and multiple intents, that are classifying complex sentences consisting of two users' intents into two automatic operations. More importantly, the agent can form a plan of actions for implicit Demand-Response and execute it, based on the user's request and by utilizing AI Planning methods. The introduced CIPA-Generation B has been deployed and tested in a real-world scenario at Centre's of Research & Technology Hellas (CERTH) nZEB SmartHome in two different domains, energy and health, for multiple intent recognition and dialogue handling. Furthermore, in the energy domain, a scenario that demonstrates how the agent solves an implicit Demand-Response problem has been applied and evaluated. An experimental study with 36 participants further illustrates the usefulness and acceptance of the developed conversational agent-based system. [ABSTRACT FROM AUTHOR]

This paper presents a fault‐tolerant secondary and adaptive primary microgrid control scheme using a hybrid multi‐agent system (MAS), capable of operating either in a semi‐centralised or distributed manner. The proposed scheme includes a droop‐based primary level that considers the microgrid energy reserves in production and storage. The secondary level is responsible for: a) the microgrid units' coordination, b) voltage and frequency restoration and c) calculation of the droop/ reversed‐droop coefficients. The suggested architecture is arranged upon a group of dedicated asset agents that collect local measurements, take decisions independently and, collaborate in order to achieve more complex control objectives. Additionally, a supervising agent is added to fulfill secondary level objectives. The hybrid MAS can operate either with or without the supervising agent operational, manifesting fast redistribution of the supervising agent tasks. The proposed hybrid scheme is tested in simulation upon two separate physical microgrids using three scenarios. Additionally, a comparison with conventional control methodologies is performed in order to illustrate further the operation of a hybrid approach. Overall, results show that the proposed control framework exhibits unique characteristics regarding reconfigurability and fault‐tolerance, while power quality and improved load sharing are ensured even in case of critical component failure. [ABSTRACT FROM AUTHOR]