Multi-agent System Engineering for Emphatic Human-Robot Interaction

Costantini, Stefania; Gasperis, Giovanni De; Migliarini, Patrizio

doi:10.1109/aike.2019.00015

Cited by 7 publications

(4 citation statements)

References 24 publications

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“…Moreover, also connected with our work, at least from the point of view of the logic programming technologies exploited and the final goal to generate virtual environments is the recent work by S. Costantini et al [47]. In that work, the authors explore the possibility of building a constraint-procedural logic generator for 2D environments and develop a deep Q-learning model based neural network agent able to address the NP search problem in the virtual space; the agent has the goal of exploring the generated virtual environment to seek for a target, improving its performance through a reinforced learning process.…”

Section: Related Work Virtual Reality and Multiagent Systems (Mas)mentioning

confidence: 63%

VEsNA, a Framework for Virtual Environments via Natural Language Agents and Its Application to Factory Automation

Gatti

Mascardi

2023

Robotics

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Automating a factory where robots are involved is neither trivial nor cheap. Engineering the factory automation process in such a way that return of interest is maximized and risk for workers and equipment is minimized is hence, of paramount importance. Simulation can be a game changer in this scenario but requires advanced programming skills that domain experts and industrial designers might not have. In this paper, we present the preliminary design and implementation of a general-purpose framework for creating and exploiting Virtual Environments via Natural language Agents (VEsNA). VEsNA takes advantage of agent-based technologies and natural language processing to enhance the design of virtual environments. The natural language input provided to VEsNA is understood by a chatbot and passed to an intelligent cognitive agent that implements the logic behind displacing objects in the virtual environment. In the complete VEsNA vision, for which this paper provides the building blocks, the intelligent agent will be able to reason on this displacement and on its compliance with legal and normative constraints. It will also be able to implement what-if analysis and case-based reasoning. Objects populating the virtual environment will include active objects and will populate a dynamic simulation whose outcomes will be interpreted by the cognitive agent; further autonomous agents, representing workers in the factory, will be added to make the virtual environment even more realistic; explanations and suggestions will be passed back to the user by the chatbot.

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Section: Related Work Virtual Reality and Multiagent Systems (Mas)mentioning

confidence: 63%

VEsNA, a Framework for Virtual Environments via Natural Language Agents and Its Application to Factory Automation

Gatti

Mascardi

2023

Robotics

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“…Actually, Redis is not typically associated with being an MAS or a platform for building MASs. But its messaging infrastructure assists users to act like an MAS [14].…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent Systems and Machine Learning for Wind Turbine Power Prediction from an Educational Perspective

Soygazi

2023

Sustainability

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Artificial intelligence (AI) is an umbrella term that encompasses different fields of study, and topics related to these fields are addressed separately or within the scope of AI. Multi-agent systems (MASs) and machine learning (ML) are the core concepts of AI that are taught during AI courses. The separate explanation of these core research areas is common, but the emergence of federated learning has triggered their combined usage. This paper describes a practical scenario in the energy domain where these technologies can be used together to provide a sustainable energy solution for predicting wind turbine active power production. The projects in the AI course were assigned prior to the step-by-step learning of MASs and ML. These concepts were applied using a wind turbine energy dataset collected in Turkey to predict the power production of wind turbines. The observed performance improvements, achieved by applying various agent architectures and data partitioning scenarios, indicate that boosting methods such as LightGBM yield better results even when the settings are modified. Additionally, a questionnaire about the assignments was filled out by the student groups to assess the impact of learning MASs and ML through project-based education. The application of MASs and ML in a hybrid way proves valuable for learning core concepts related to AI education, as evidenced by feedback from students.

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“…Similarly to what the Trusted Platform Module [11] has been for the evolution of cryptography, our system could be integrated as middleware in modern computing platforms [9]. Considering the variety of input data required to tailor a personalized User Model, including the emotional and biometric ones [17], the mobile domain is particularly suited for our vision, due to its sensors-rich nature. Modern mobile devices are in fact equipped with audiovisual (e.g., high resolution camera, microphone), biometric (e.g., heart beat monitor, galvanic skin response [21]) and motion sensors (e.g., gyroscope, accelerometer) which may be of use.…”

Section: Introductionmentioning

confidence: 99%

On the elicitation of privacy and ethics preferences of mobile users

Migliarini

Scoccia

Autili

et al. 2020

Proceedings of the IEEE/ACM 7th International Conference on Mobile Software Engineering and Systems

Self Cite

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Nowadays, mobile users are constantly being connected and increasingly asked to express their personal preferences in the digital world. User preferences deal with simple device settings options, like notification alarms, as well as relevant ethical choices relating to the user behavior, privacy ones included (e.g., concerning the unauthorized disclosure and mining of personal data, as well as the access to restricted resources). All these preferences define the user, they are the building blocks of her digital identity and will be increasingly important given the growing rise of autonomous technologies and their ethical implications. The settings that enable these preferences are often hard to locate and hard to understand, even in popular apps and operating systems. Moreover, they can expose privacy, be employed for profiling or exploited for malicious activities. In this landscape, we devise the introduction of a Personal Preferences Automation Module (PPAM) capable of automatically inferring, applying and enforcing user choices in multiple scenarios ranging from speeding up simple time consuming tasks, to managing sensitive ethical choices. The wide range of sensors and devices that can be found in the mobile domain makes it a privileged context in which to employ the proposed module. In this paper, we present two application scenarios and describe the proposed approach at work on them.

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Multi-agent System Engineering for Emphatic Human-Robot Interaction

Cited by 7 publications

References 24 publications

VEsNA, a Framework for Virtual Environments via Natural Language Agents and Its Application to Factory Automation

VEsNA, a Framework for Virtual Environments via Natural Language Agents and Its Application to Factory Automation

Multi-Agent Systems and Machine Learning for Wind Turbine Power Prediction from an Educational Perspective

On the elicitation of privacy and ethics preferences of mobile users

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