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TRENDS IN distributed artificial-intelligence

artificial intelligence REVIEW 1992年第1期6卷 35-66页

作者： CHAIBDRAA, B MANDIAU, R MILLOT, P MOULIN, B UNIV VALENCIENNES & HAINAUT CAMBRESIS AUTOMAT IND & HUMAINE LAB CNRS URA 1118 F-59326 VALENCIENNES FRANCE

distributed artificial intelligence (DAI) is a subfield of artificial intelligence that deals with interactions of intelligent agents. Precisely, DAI attempts to construct intelligent agents that make decisions that allow them to achieve their goals in a world populated by other intelligent agents with their own goals. This paper discusses major concepts used in DAI today. To do this, a taxonomy of DAI is presented. based on the social abilities of an individual agent. the organization of agents, and the dynamics of this organization through time. Social abilities are characterized by the reasoning about other agents and the assessment of a distributed situation. Organization depends on the degree of cooperation and on the paradigm of communication. Finally. the dynamics of organization is characterized by the global coherence of the group and the coordination between agents. A reasonably representative review of recent work done in DAI field is also supplied in order to provide a better appreciation of this vibrant AI field. The paper concludes with important issues in which further research in DAI is needed.

关键词： distributed artificial intelligence REASONING ABOUT OTHERS ORGANIZATION COOPERATION COMMUNICATION COHESION COORDINATION NEGOTIATION

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Intentional Forgetting in distributed artificial intelligence

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KUNSTLICHE INTELLIGENZ 2019年第1期33卷 69-77页

作者： Reuter, Lukas Berndt, Jan Ole Ulfert, Anna-Sophie Antoni, Conny H. Ellwart, Thomas Timm, Ingo J. Trier Univ Trier Res Lab Simulat TriLabS Ctr Informat Res & Technol CIRT Behringstr 21 D-54296 Trier Germany Trier Univ Business Work & Org Psychol Univ Ring 15 D-54296 Trier Germany

In times of Big Data and Industry 4.0, organizational information as well as knowledge availability and quantity are driving complex decision-making tasks. Especially for AI systems, increasing knowledge-bases for elaborate computations lead to a lower efficiency of their inference mechanisms. In contrast to AI, bounded cognitive capacity is a well-known problem in psychology. When humans receive too much information, a state of information overload emerges. In order to cope with limited capacity and prevent information overload, humans adapt their knowledge and delete, override, suppress, or sort out outdated information, i.e., they forget. By transferring theories from human cognition to multiagent systems, the AdaptPRO project adopts intentional forgetting as a strategy for coping with information overload in both human and multiagent teams. This article gives an overview of an interdisciplinary research project with a strong focus on knowledge distributions and knowledge dynamics from a distributed AI perspective. Its core contribution is a formal model for distributing and adapting (meta-) knowledge by intentional forgetting to enable efficient and resilient teamwork.

关键词： Intelligent agent Multiagent-system Intentional forgetting distributed artificial intelligence

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distributed artificial intelligence: Taxonomy, review, framework, and reference architecture

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INTELLIGENT SYSTEMS WITH APPLICATIONS 2023年 18卷

作者： Janbi, Nourah Katib, Iyad Mehmood, Rashid Univ Jeddah Coll Comp & Informat Technol Khulais Dept Informat Technol Jeddah 21959 Saudi Arabia King Abdulaziz Univ Fac Comp & Informat Technol Dept Comp Sci Jeddah 21589 Saudi Arabia King Abdulaziz Univ High Performance Comp Ctr Jeddah 21589 Saudi Arabia

artificial intelligence (AI) research and market have grown rapidly in the last few years, and this trend is expected to continue with many potential advancements and innovations in this field. One of the emerging AI research directions is distributed artificial intelligence (DAI). It has been motivated by technological advances in communication, networking, and hardware, together with the nature of data being generated from connected, distributed, and diverse objects. DAI is expected to create a fertile environment for innovative, advanced, robust, and scalable approaches for AI supporting the vision of smart societies. In this paper, we explore state of the art on DAI and identify the opportunities and challenges of provisioning distributed AI as a service (DAIaaS). We provide a taxonomy and a comprehensive review covering the literature from 2016 to 2022. It comprises various aspects of DAI, including AI workflow, distribution paradigms, supporting infrastructure, management techniques, and applications. Based on the gained insights from the conducted review, we propose Imtidad, a framework for provisioning DAIaaS over the cloud, fog, and edge layers. We refine this framework and propose the Imtidad software Reference Architecture (RA) for designing and deploying DAI services. In addition, we extended the framework and developed a future networking infrastructure transformation framework, as it is one of the main enablers for DAI. This framework and RA can be used as guidance facilitating the transition to the future DAI, where DAI is decoupled from the design and development of smart applications. This paper, including the proposed framework, RA, taxonomy, and detailed review, is expected to have an extensive impact on DAI research and accelerate innovations in this area.

关键词： artificial intelligence distributed artificial intelligence distributed pre-processing distributed training distributed inference distributed decision making Data parallelism Model parallelism Pipelined parallelism Hybrid parallelism Federated learning Multi-layer architecture 6th generation (6 g) networks Future networking Fog computing edge computing cloud computing

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Innovative Semantic Web Services for Next Generation Academic Electronic Library via Web 3.0 via distributed artificial intelligence

Innovative Semantic Web Services for Next Generation Academi...

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4th International Scientific Asian Conference on Intelligent Information and Database Systems (ACIIDS)

作者： Chu, Hai-Cheng Yang, Szu-Wei Natl Taichung Univ Educ Dept Educ 140 Min Shen Rd Taichung 40306 Taiwan Natl Taichung Univ Educ Dept Int Business Taichung 40306 Taiwan

ISBN: (纸本)9783642284878

The purpose of this article is to incubate the establishment of semantic web services for next generation academic electronic library via Web 3.0. The e-library users will be extremely beneficial from the forthcoming semantic social web mechanism. Web 3.0 will be the third generation of WWW and integrate semantics web, intelligent agent, and distributed artificial intelligence into the ubiquitous networks. On top of current library 2.0 structures, we would be able to fulfill the Web 3.0 electronic library. We design the deployment of intelligent agents to form the semantic social web in order to interpret linguistic expressions of e-library users without ambiguity. This research is conducting the pioneering research to introduce the future and direction for the associate academic electronic library to follow the proposed guidelines to initiate the construction of future library system in terms of service-oriented architecture. This research article is the pioneering practice of future academic digital libraries under Web 3.0 structures.

关键词： Web 3.0 Multi-Agent System (MAS) Digital Library Semantic Social Web distributed artificial intelligence

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Bridging the gap:: On the collaboration between symbolic interactionism and distributed artificial intelligence in the field of multi-agent systems research

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SYMBOLIC INTERACTION 1998年第4期21卷 441-463页

作者： Strübing, J Free Univ Berlin Inst Sociol D-10715 Berlin Germany

This article discusses the relationship in the U.S. between current symbolic interactionism and computer sciences-specifically, distributed artificial intelligence (DAI). The general thesis is twofold First, current interactionist approaches to organization, science, and technology show a special affinity to goals and problems of DAI research, and in research style, methods, and theoretical concepts, symbolic interactionism can provide useful suggestions in the design of DAI systems. Second, a good way to analyze the relationship between computer sciences and symbolic interactionism is reflexive of theoretical concepts provided by interactionist approaches. In this sense, DAI is a "going concern" which extends across various fields and intersecting social worlds connected through a set of conceptual "boundary objects." It is concluded that the interaction between technology and sociological thought must go beyond a mere exchange of ideas. What is required is continual, hands-on, transdisciplinary collaboration.

关键词： distributed artificial intelligence SYMBOLIC interactionism SOCIAL interaction artificial intelligence COMPUTER science

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FUZZY distributed artificial intelligence SYSTEMS

FUZZY DISTRIBUTED ARTIFICIAL INTELLIGENCE SYSTEMS

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3rd IEEE Conference on Fuzzy Systems/IEEE World Congress on Computational intelligence

作者： NAKAMITI, G FREITAS, R PRADO, J GOMIDE, F UNESP IGCEDEMACRIO CLAROSPBRAZIL

ISBN: (纸本)0780318978

In this paper, we introduce a DAI approach called hereinafter Fuzzy distributed artificial intelligence (FDAI). Through the use of fuzzy logic, we have been able to develop mechanisms that we feel may effectively improve current DAI systems, giving much more flexibility and providing the subsidies which a formal theory can bring. The appropriateness of the FDAI approach is explored in an important application, a fuzzy distributed traffic-light control system, where we have been able to aggregate and study several issues concerned with fuzzy and distributed artificial intelligence. We also present a number of current research directions necessary to develop the FDAI approach more fully.

关键词： distributed artificial intelligence FUZZY SYSTEMS TRAFFIC-LIGHT CONTROL

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Decentralized network management using distributed artificial intelligence

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Journal of Network and Systems Management 2001年第4期9卷 375-388页

作者： Koch, Fernando Luiz Federal University of Santa Catarina Post-Graduate Program in Computer Science Technological Center 88040-970 Florianopolis SC P.O. Box 476 Brazil

Centralized approaches to Network Management have demonstrated a clear inadequacy for efficient management of large and heterogeneous computer networks. Considerable research is being carried out on decentralized approaches for network management. This paper presents the work on a practical application of distributed artificial intelligence for computer network management. The objective is to implement a software platform using only Intelligent Autonomous Agents, integrated with the SNMP environment. © 2001 Plenum Publishing Corporation.

关键词： Autonomous agents Computational cooperation distributed artificial intelligence distributed computing Knowledge management Neural networks

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Stormcast — A distributed artificial intelligence Application for Severe Storm Forecasting

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IFAC Proceedings Volumes 1988年第12期21卷 99-102页

作者： G. Hartvigsen D. Johansen Bodø Graduate School of Business N-8016 Mørkved Norway Dept. of Computer Science Unversity of Tromsø N-9001 Tromsø Norway

This paper describes the design and implementation of a prototype distributed artificial intelligence application for severe storm forecasting. The distributed artificial intelligence application consists of a set of ... 详细信息

关键词： distributed application distributed artificial intelligence expert system weather forecasting

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MYRTO: An efficient pervasive method for hybrid ML-based data filtered allocations

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JOURNAL OF INTELLIGENT INFORMATION SYSTEMS 2025年第2期63卷 681-704页

作者： Papathanasiou, Dimitrios Tziouvaras, Athanasios Kolomvatsos, Kostas Univ Thessaly Dept Informat & Telecommun Lamia 35100 Thessaly Greece Univ Thessaly Dept Elect & Comp Engn Volos 38334 Thessaly Greece

In a Pervasive Edge Computing (PEC) ecosystem, numerous Internet of Things (IoT) devices collect data from their environment and forward them to a network of Edge Computing (EC) nodes. Each EC node maintains a local dataset of the collected data and trains Machine Learning (ML) models to produce knowledge upon data, or to serve end user requests. Since data can be stored either on Cloud or on an EC node, a question arises regarding the optimal placement of such information with the ultimate goal of minimizing the latency when processing activities upon those data are requested. In this research, we tackle this issue by proposing MYRTO, which is novel methodology that decides where data should be allocated within a network of distributed nodes. MYRTO is a hybrid approach that utilises both data and statistical overlapping estimation techniques, to efficiently move data within the PEC network. Naturally, the collected data together with the data processing requests may alter the ML filters of the EC nodes. To account for this, MYRTO adopts a quantile regression technique that estimates the best data-ML filter overlapping and then, selects the appropriate nodes. If no nodes present adequate overlapping, the data are then uploaded to the Cloud. This way, our technique finds the optimal candidate for storing the IoT data between a large number of EC nodes, by selecting the one(s) that will exploit them more efficiently. We accompany our theoretical approach be a detailed evaluation methodology that clearly demonstrates the applicability of our work in PEC ecosystems.

关键词： Ubiquitous computing edge computing data filtering data management machine learning distributed artificial intelligence quantile regression statistical paradigms

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Intelligent Agent Based Clustering and Optimal Multipath Routing for Energy-Efficient Wireless Sensor Networks in Smart City Applications: A distributed AI-Driven Approach 5th

Intelligent Agent Based Clustering and Optimal Multipath Rou...

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5th International Conference on Computing Science, Communication and Security

作者： Patra, Binaya Kumar Mishra, Sarojananda Patra, Sanjay Kumar Mallik, Ashutosh Roy, Souveek Indira Gandhi Inst Technol Dept Comp Sci Engn & Applicat Sarang 759146 Odisha India GIET Univ Dept Comp Sci Engn Gunupur Odisha India

ISBN: (纸本)9783031751691;9783031751707

The study proposes an innovative approach to enhance energy efficiency in Wireless Sensor Networks (WSNs) for smart city applications. The primary focus is on leveraging distributed artificial intelligence (AI) and multipath routing techniques to address challenges such as unequal clustering, poor cluster head selection, and excessive power consumption within WSNs. The approach uses agent-based clustering, where autonomous AI agents dynamically form clusters of sensor nodes based on real-time data characteristics. These clusters are then used for multipath routing, optimizing energy consumption, reliability, and congestion reduction. The distributed nature of AI agents allows for adaptive cluster formations. This algorithm aims to address issues related to uneven clustering, inefficient cluster head selection, and excessive power consumption. Additionally, the integration of agent-based clustering is proposed, involving the deployment of autonomous AI agents that dynamically cluster sensor nodes based on real-time data properties. These AI agents facilitate self-organization and adaptability, ensuring that clusters accurately reflect the evolving data landscape in urban environments. The approach also employs sophisticated energy management strategies at the sensor node level, such as duty cycling, adaptive transmission power control, and sleep-wake scheduling. Simulations in a smart city environment show significant improvements in energy efficiency, prolonging the network's operational lifespan and improving service quality by mitigating data loss and latency issues. This approach contributes to the sustainable development and performance optimization of smart city infrastructure.

关键词： Wireless Sensor Networks Smart Cities Clustering distributed artificial intelligence Multipath Routing Energy Efficiency

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