A Framework for Multi-Agent Systems with Multi-Modal User Interfaces in Distributed Computing Environments

The framework of transformer condition assessment system is proposed in this paper through mainly using data warehouse techniques, data mining techniques, and Open Agent Architecture. In this system, a data warehouse is used to collect transformers’ testing data, a multi-agent system is used to design the framework of the software, and data mining techniques are used to evaluate transformers’ conditions. The present framework is open and flexible, so the objective system is easy to be developed and maintained. The system can support transformers’ condition-based maintenance to reduce electric utility’s cost.Because the condition of a transformer depends on its design, present and historical operating data, and relates to its installation environment, load amounts, and so on, it is necessary to integrate all above information to evaluate transformer’s condition. However, the off-line testing results, operational data, fault records and weather conditions have been stored in different systems, so finding an effective method to utilize all this information for condition assessment is difficult. Therefore, a data warehouse has been set up to integrate all of the above data, and some data mining techniques have been used to find the pattern and trend of the condition of a transformer. Then whether it is healthy can be determined. In order to make the system open and flexible, Open Agent Architecture (OAA) is employed to compose the multi-agent system. Seven application agents are designed to evaluate transformers’ conditions synthetically. The Grey correlation method, grey theory prediction model GM(1,1), Bayesian network classifier and Bayesian network are employed in the agents.

The framework of the assessment system for transformer condition is proposed using data warehouse, multi-agent system and data mining technologies synthetically. It is used to support the condition-based maintenance of transformer, improve the equipment reliability and reduce the substation operation cost. The health state of the transformer depends on the design, present and historical operation conditions of equipment. Usually, the off-line testing results, operation history and fault records of equipment, and weather conditions, etc. have been stored in different systems and they are very difficult to be utilized synthetically. Therefore, a data warehouse has been set up to integrate all of these data. We used data mining techniques to get the regulation and trend of the health condition of transformer and then make a conclusion. In order to implement the system efficiently and effectively, we employed some ideas of open agent architecture (OAA) to compose a multi-agent-based framework of the proposed system.

Welcome to the 24th ACM International Conference on User Modeling, Adaptation, and Personalization (UMAP 2016) in Halifax, Canada, July 13-16, 2016. UMAP is the premier international conference for researchers and practitioners working on systems that adapt to individual users or to groups of users. UMAP is the successor of the biennial User Modeling (UM) and Adaptive Hypermedia and Adaptive Web-based Systems (AH) conferences that were merged in 2009. It has traditionally been organized under the auspices of User Modeling Inc. This year (2016) UMAP became an ACM conference, sponsored by ACM SIG CHI and SIG WEB. The conference spans a wide scope of topics related to user modeling, adaptation, and personalization. UMAP 2016 is focused on bringing together cutting-edge research from user interaction and modeling, adaptive technologies, and delivery platforms. It includes high-quality peer-reviewed papers featuring substantive new research in one of five research areas, each chaired by leaders in the field: User Modeling for Recommender Systems (chairs: Alexander Felfernig & Pasquale Lops)Adaptive & Personalized Educational Systems (chairs: Antonija Mitrovic & Kalina Yacef)Personalization in the Social Web & Crowdsourcing Era (chairs: Alessandro Bozzon & Harith Alani)Adaptive, Intelligent, & Multimodal User Interfaces (chairs: Julien Epps & Hatice Gunes)Architectures, Techniques, & Methodologies for UMAP (chairs: Stephan Weibelzahl & Mihaela Cocea) This year we received 123 submissions. In keeping with UMAPs rigorous standards, each paper was carefully reviewed by members of the Program Committee (PC) while the Area Chairs (ACs) coordinated the reviews and provided recommendations to the Program Chairs. The international Program Committee (PC) consisted of 132 members who were assisted by 49 subreviewers. These were leading researchers as well as highly promising young researchers. Papers were assigned to at least 4 members of the PC and to 1 AC member based on their expertise, interests, and other factors. Each paper received at least 3 reviews, and 95% received 4 reviews. After the initial reviews were submitted, the designated AC facilitated discussion amongst reviewers in order to resolve differences and correct misunderstandings. The AC then provided a summative meta-review and a recommendation to the Program Chairs. The final decisions were based on these recommendations, the meta-reviews, and reviewer scores. We accepted 21 long papers (23.9% acceptance rate) and 13 short papers (27.6% acceptance rate) for oral presentation and an additional 17 extended abstracts for poster presentation and inclusion in the proceedings. The program also features posters, demos, and late breaking results, which collectively showcase the wide spectrum of novel ideas and latest results in user modeling, adaptation and personalization. We also invited three distinguished keynote speakers, each illustrating significant issues and prospective directions for the field. Hossein Derakhshan (shared keynote speaker with Hypertext 2016) is an Iranian-Canadian blogger who was imprisoned in Tehran from November 2008 to November 2014. He has been called the "father of Persian blogging" and has helped promote podcasting in Iran. His talk, "Killing the Hyperlink, Killing the Web: the Shift from Library-Internet to Television-Internet," reflects his views on the Internet today.Lada Adamic leads the Product Science group within Facebook's Data Science Team. She is also an adjunct associate professor at the University of Michigan's School of Information and Center for the Study of Complex Systems. Her talk "The Life and Times of Information in Networks" focuses on cascades of information-sharing and resharing within social media.Sandra Carberry was one of the founders of the User Modeling research area at the first workshop in Maria Laach in 1986. As appropriate for the 30th anniversary, her talk "User Modeling: The Past, The Present and The Future" discusses how the field evolved, insights into where the field is headed, and the hottest topics for exploration. The conference includes a doctoral consortium that provides an opportunity for doctoral students to explore and develop their research interests under the guidance of distinguished scholars. This track received 18 submissions of which nine were accepted as full papers and eight as posters. A set of seven workshops and two tutorials round out the program. (Workshop) IFUP: Workshop on Multi-dimension Information Fusion for Modeling and Personalisation (half-day) organized by Robin Burke (DePaul University, USA), Feida Zhu (Singapore Management University, Singapore), Neil Yorke-Smith (American University of Beirut, Lebanon), and Guibing Guo (Northeastern University, China)(Workshop) INRA: News Recommendation and Analytics (half-day) organized by Jon Atle Gulla (Norwegian University of Science and Technology Trondheim, Norway), Luc Martens (Minds-UGent-WiCa Ghent, Belgium), Özlem Özgöbek (Norwegian University of Science and Technology Trondheim, Norway), and Nafiseh Shabib (TNS Gallup, Oslo, Norway)(Workshop) SOAP: Workshop on Surprise, Opposition, and Obstruction in Adaptive and Personalized Systems (half-day) organized by Peter Knees (Johannes Kepler University Linz, Austria), Kristina Andersen (Studio for Electro Instrumental Music, Amsterdam, the Netherlands), Alan Said (Recorded Future, Gothenburg, Sweden), and Marko Tkalcic (Free University of Bozen-Bolzano, Italy)(Workshop) HAAPIE: Human Aspects in Adaptive and Personalised Interactive Environments (half-day) organized by Panagiotis Germanakos (SAP SE, Germany), Marios Belk (Department of Computer Science, University of Cyprus), George Samaras (Department of Computer Science, University of Cyprus), and Vania Dimitrova (University of Leeds, UK)(Workshop) EvalUMAP: Towards comparative evaluation in the user modelling, adaptation and personalization space (full-day) organized by Owen Conlan (Trinity College Dublin, Ireland), Liadh Kelly (Trinity College Dublin, Ireland), Kevin Koidl (Trinity College Dublin, Ireland), Séamus Lawless (Trinity College Dublin, Ireland), Killian Levacher (Trinity College Dublin, Ireland), and Athanasios Staikopoulos (Trinity College Dublin, Ireland)(Workshop) FuturePD: The future of personal data: envisioning new personalized services enabled by Quantified Self technologies (half-day) organized by Amon Rapp (University of Torino, Italy), Federica Cena (University of Torino, Italy), Judy Kay (University of Sidney, Australia), Bob Kummerfeld (University of Sydney, Australia), Frank Hopfgartner (University Gardens Glasgow, UK), Jakob Eg Larsen (Technical University of Denmark, Denmark), and Elise van den Hoven (University of Technology Sydney, Australia). (Workshop) PALE: Personalization Approaches in Learning Environments (full-day) organized by Milos Kravcik (RWTH Aachen University, Germany), Olga C. Santos (UNED, Spain), Jesus G. Boticario (UNED, Spain), and Maria Bielikova (FIIT STUBA, Slovakia)(Tutorial) Semantics-Aware Techniques for Social Media Analysis, User Modeling, and Recommender Systems (half-day) by Pasquale Lops and Cataldo Musto (University of Bari Aldo Moro, Italy)(Tutorial) Games, Gamification and Personalization (half-day) by Amon Rapp (University of Torino, Italy).

This paper investigates a distributed time-varying output formation control framework for heterogeneous multi-agent systems (MASs) based on reinforcement learning (RL) method with multiple leaders and various disturbances. The outputs of followers are designed to accomplish robust tracking along with the movement of leaders’ convex combination while achieving a predefined configuration of time-varying formation. A three-layer framework, composed of a distributed adaptive finite-time observer, an off-policy RL-based optimal tracking controller and a robust formation controller, is proposed in a model-free manner while neither the global information of graph nor followers’ dynamics is utilized. The stability of this integrated observer-based controller is analyzed using Lyapunov theory which indicates that the formation tracking error asymptotically converges to zero under both external and internal unknown disturbances. Simulation results provide a detailed validation of the proposed control architecture.

This paper gives aspects related to a cooperation scenario in the framework of multiagent systems. The presentation is focused on a multiagent system that consists of two agents, the Master and the Apprentice. The theoretical basis of the cooperation scenario is the definition of the most probable process, and two algorithms are used with this regard. The formulation of the cooperation scenario is exemplified for a case study that builds an architecture of successively placed bricks in the workspace.

Reinforcement Learning has long been employed to solve sequential decision-making problems with minimal input data. However, the classical approach requires a long time to learn a suitable policy, especially in Multiagent Systems. The teacher-student framework proposes to mitigate this problem by integrating an advising procedure in the learning process, in which an experienced agent (human or not) can advise a student to guide her exploration. However, the teacher is assumed to be an expert in the learning task. We here propose an advising framework where multiple agents advise each other while learning in a shared environment, and the advisor is not expected to necessarily act optimally. Our experiments in a simulated Robot Soccer environment show that the learning process is improved by incorporating this kind of advice.

We present a machine learning framework for multi-agent systems to learn both the optimal policy for maximizing the rewards and the encoding of the high dimensional visual observation. The encoding is useful for sharing local visual observations with other agents under communication resource constraints. The actor-encoder encodes the raw images and chooses an action based on local observations and messages sent by the other agents. The machine learning agent generates not only an actuator command to the physical device, but also a communication message to the other agents. We formulate a reinforcement learning problem, which extends the action space to consider the communication action as well. The feasibility of the reinforcement learning framework is demonstrated using a 3D simulation environment with two collaborating agents. The environment provides realistic visual observations to be used and shared between the two agents.

Emergence of game strategy in multiagent systems is studied. Symbolic and subsymbolic (neural network) approaches are compared. Symbolic approach is represented by a backtrack algorithm with specified search depth, whereas the subsymbolic approach is represented by feedforward neural networks that are adapted by reinforcement temporal difference TD(λ) technique. As a test game, we use simplified checkers. The problem is studied in the framework of multiagent system, where each agent is endowed with a neural network used for a classification of checkers positions. Three different strategies are used. The first strategy corresponds to a single agent that repeatedly plays games against MinMax version of a backtrack search method. The second strategy corresponds to single agents that are repeatedly playing a megatournament, where each agent plays two different games with all other agents, one game with white pieces and the other game with black pieces. After finishing each game, both agents modify their neural networks by reinforcement learning. The third strategy is an evolutionary modification of the second one. When a megatournament is finished, each agent is evaluated by a fitness, which reflects its success in the given megatournament (more successful agents have greater fitness). It is demonstrated that all these approaches lead to a population of agents very successfully playing checkers against a backtrack algorithm with the search depth 3.

This paper describes the control of computations in a distributed computing environment (DCE) on the basis of its meta-monitoring and simulation modeling. Computations are controlled by a multiagent system with a given organizational structure. Resource allocation is carried out by agents with the use of economic mechanisms for controlling their supply and demand. Controlling actions for agents are formed on the basis of the simulation modeling of functional processes of the DCE. Data about the DCE resources and processes are collected and emergency situations in the DCE nodes are detected and prevented by the meta-monitoring system of this environment. The research results are the techniques for selecting control actions and the methods for intellectual processing and effective storage of data.

A key requirement to realize modern distributed systems is the ability to adapt the system’s behavior autonomously at runtime towards changing environmental conditions, in order to preserve their operation even in the presence of uncertain changes. The different parts of such a distributed self-organizing system have to be coordinated in order to achieve meaningful adaptations. To avoid single point of failures, decentralized coordination is a key element for the realization of robust and scalable self-adaptation. Due to their inherently decentralized system architecture Multi-Agent Systems (MAS) are well suited to realize such self-organizing systems relying on decentralized coordination. This paper proposes a decentralized coordination framework which focuses on equipping different types of MAS with self-organizing capabilities. Thereby, it shall support various types of MAS so that developers are not limited to a specific platform, while also supporting the coordination of applications consisting out of different, heterogeneous (Multi-Agent) technologies, e.g. required in the area of co-simulations.

Our overall aim is to provide a verification framework for practical multi-agent systems. To achieve practicality, we must be able to describe and implement heterogeneous multi-agent systems. To achieve verifiability, we must define semantics appropriately for use in formal verification. In this paper, we tackle the problem of implementing heterogeneous multi-agent systems in a semantically clear, and appropriate, way.

In this paper, we propose an approach for estimating multi-objective operational efficiency of microgrid. The proposed approach is based on analyzing various microgrid components in the form of distributed intelligent agents in a framework of multi-agent system (MAS), which allows evaluating the operational efficiency of agents individually, as well as the efficiency of the microgrid as a combined system. Moreover, we introduce a novel unified agent representation, appropriate for a variety of dynamic systems containing a combination of both continuous and discrete event processes. The elaborated scheme gives the possibility to formalize the process of integrating heterogeneous objective functions into certain combined criteria. Different efficiency criteria are considered in this work: technical, economic and ecological. The algorithm of microgrid efficiency estimation is carried out based on an application of designing an eco-friendly hybrid power generating system, producing electricity and heat in Arctic enclave.

Due date (DD) quotation for the customer enquiry is strategically important in the mould industry, having a large influence on the delivery reliability performance. This paper develops a multi-agent system framework (MASF), which uses a multi-agent system to provide a reliable and competitive DD for the new arriving orders. Firstly, a decision-making structure for mould DD quotation is proposed, which can determine reasonable delivery times or reject the unsuitable orders. Compared with previous research, a distinguishing feature of the structure is to take into consideration the uncertainties of resources for processing task via a stochastic state evolution model (SSEM). Based on the presented structure, the MASF is then developed and described using six different agents. The algorithm agent (AA) is the key to the framework and contains two models: capacity check model and project progress evolution model. The former is used for verifying the feasibility of the fixed DD, and that the latter can compute the probability distribution and mathematical expectation of the negotiable DD. Other agents are responsible for providing necessary information to the AA. Finally, a numerical example of a real-life mould production system is studied and shown to be more suitable for a stochastic environment than related research.

Within the multiagent systems framework, the relative states between neighbors can be acquired by some on-board sensors, and then the relative state saturations inevitably occur due to the limited sensing capabilities. This paper investigates the consensus problem of nonlinear multiagent systems subject to the relative state saturations. Utilizing the incidence matrix and the edge Laplacian, the consensus problem of nonlinear multiagent systems with the relative state saturations can be cast into the stabilization problem of edge dynamics operating on the constrained set. Three types of protocols, namely continuous, intermittent, and adaptive state feedback protocols are, respectively, proposed for achieving the constrained consensus, and meanwhile yielding consensus values. A consensus analysis is provided by virtue of state saturation theory, switched system theory, adaptive theory, and Lyapunov stability theory. Output feedback protocol is also designed. Finally, the obtained results are applied to connectivity preservation for first-order nonlinear multiagent systems, despite the presence of limited communication range and input constraints. The theoretical results are validated by two simulation examples.

This paper presents the development of a robust and intelligent multi-agent system (MAS) framework integrated with a microgrid (MG) in a co-simulation environment. The work begins with an overview of recent advances in MAS applications for MG control, cybersecurity, and transactive energy management, highlighting the rapid growth and significance of this research domain. The proposed MAS platform is designed to enable seamless MG operational mode transitions and includes detailed descriptions of agent platform initialisation, agent class modelling, logic definition, algorithm design, and communication mechanisms, alongside the comprehensive mathematical modelling of the MG. Multiple case studies are conducted to assess the system's performance and to verify seamless data exchange among distributed agents and between two distinct simulation platforms. The simulation results confirm that the proposed MAS-based solution effectively supports intelligent MG control across multiple operational scenarios, including grid-connected operation, islanding, load shedding, and grid reconnection. The findings demonstrate the framework's capability to enhance operational efficiency, resilience, and coordination within MG environments.