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International Conference on Autonomous Agents and Multiagent Systems (AAMAS)

作者： Nunes, Davide Univ Lisbon Lisbon Portugal

ISBN: (纸本)9781450327381

The Semantic Web movement promotes the embedding of semantic content into Web resources. Unfortunately, the Web entities do not find enough motivation nor reward to do their part in the construction of a semantically richer Web environment. To enable a true semantic Web, each human readable document should provide a formal knowledge representation for its content. As hand-crafted knowledge acquisition seems infeasible when applied to an environment as vast and dynamic as the Web, efforts have been focused on automatic approaches to extract semantic content from textual documents. Advances in Natural Language Processing, Information Extraction and Ontology Learning provided tools that allow for the extraction and analysis of structured semantic knowledge given existing textual corpus. While these tools seem promising to enable a scenario where Semantic Web can be achieved, they need to be adapted to the scale and complexity of the Web. In this article, I propose that a possible solution lies in harvesting the power of emergent multi-agent societies to create an infrastructure capable of bootstrapping the adoption of Semantic Web technologies. I focus on how to create and adapt distributed on-line evolutionary algorithms to continuously design and improve social agents capable of semantic knowledge retrieval. I argue that currently existing automatic and semi-automatic structured knowledge acquisition techniques can be adapted to serve not only as building blocks for emerging and evolving knowledge extracting processes but also as one of the driving forces behind its adaptation. I then claim that learning extracting procedures can be done not only using annotated resources but also existing techniques creating dynamic fitness models that can be continuously updated to improve the system.

关键词： Multi-agent Systems distributed artificial intelligence Evlutionary Computation On-line learning Ontology Learning Ontology Emergence Semantic Web

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Incorporating fuzzy sets in intelligent distributed systems

Incorporating fuzzy sets in intelligent distributed systems

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1996 Biennial Conference of the North-American-Fuzzy-Information-Processing-Society

作者： Nakamiti, G Gomide, F UNESP IGCEDEMACRIO CLAROSPBRAZIL

ISBN: (纸本)0780332253

The incorporation of fuzzy sets concepts into intelligent distributed systems increases flexibility and adaptability when uncertain information must be handled. Therefore, a wider class of complex, real-world problems can be tackled. The emphasis of this paper is on a distributed traffic-light control system built upon a fuzzy distributed architecture. Simulation results considering a section of Campinas downtown area are included. The performance of the proposed system, when compared to conventional strategies, is also shown to demonstrate its effectiveness.

关键词： distributed artificial intelligence fuzzy sets traffic control systems

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A Web Pages Automatic Filtering System

A Web Pages Automatic Filtering System

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Conference of the World-Academy-of-Science-Engineering-and-Technology

作者： Nouali, O. Saidi, A. Chahrat, H. Krinah, A. Toursel, B. CERIST Lab Intelligence Artificielle Ben Aknoun Alger Algeria

ISBN: (纸本)9759845849

This article describes a Web pages automatic filtering system. It is an open and dynamic system based on multi agents' architecture. This system is built up by a set of agents having each a quite precise filtering task of to carry out (filtering process broken up into several elementary treatments working each one a partial solution). New criteria can be added to the system without stopping its execution or modifying its environment. We want to show applicability and adaptability of the multi-agents approach to the networks information automatic filtering. In practice, most of existing filtering systems are based on modular conception approaches which are limited to centralized applications which role is to resolve static data flow problems. Web pages filtering systems are characterized by a data flow which varies dynamically.

关键词： Agent distributed artificial intelligence Multi agents System Web pages filtering

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Computers, productive agency, and the effort after shared meaning

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Journal of Computing in Higher Education 2001年第2期12卷 3-33页

作者： Schwartz, Daniel L. Lin, Xiaodong School of Education Stanford University Stanford CA 94305-3096 485 Lasuen Mall United States Department of Teaching and Learning Vanderbilt University United States George Peabody College Vanderbilt University 30203 Nashville TN United States

COMPUTERS PROVIDE FRESH OPPORTUNITIES for enhancing and understanding collaborative learning. They permit new research methodologies such as simulations of cooperating agents, and they present new design challenges including computerized peers and supporting collaborations across cultural boundaries. This article has two goals. One goal is to offer recent examples and findings that may help design computer-supported collaboration. The other is to begin a theoretical discussion that focuses on the individuals who collaborate. This differs from much of the collaborative and cooperative learning literature that emphasizes the rules and structures for enforcing collaboration (e. g., group roles and joint accountability). We start with individuals, because we believe that much of the learning that occurs during collaboration develops out of individuals' efforts to share meaning and understand one another. We consider prerequisites to people's effort to share meaning, and we particularly focus on the important role of the productive agency that leads people to contribute rather than just borrow knowledge. We consider the type of knowledge likely to develop during collaboration, and we suggest ways to prepare and help people learn from the language that permeates collaborations as well as formal classroom lectures and texts. © 2001 Springer.

关键词： agency collaborative learning cultural exchange distributed artificial intelligence small group research

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Enhancing Cooperation in distributed Information Systems Using Conviviality and Multi-Context Systems

Enhancing Cooperation in Distributed Information Systems Usi...

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5th Multi-Disciplinary International Workshop on artificial intelligence, MIWAI-2011

作者： Caire, Patrice Bikakis, Antonis Univ Namur PReCISE Res Ctr Dept Comp Sci Namur Belgium UCL Dept Informat Studies London England

ISBN: (纸本)9783642257247;9783642257254

Modern information systems are characterized by the distribution of information and services among several autonomous heterogeneous entities. A major requirement for the success of such systems is that participating entities cooperate by sharing parts of their local knowledge. This paper presents a novel approach for modeling and enhancing cooperation in distributed information systems, which combines two formal models from the field of Knowledge Representation and Reasoning: a conviviality model and Multi-Context Systems. Our aim is two-fold. First, we develop a combined model for context-based representation and cooperation. Second, we provide the means for measuring cooperation leading to the design and evaluation of more convivial systems.

关键词： distributed artificial intelligence Knowledge Representation and Reasoning Conviviality Multi-Context Systems Social Dependence Networks

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Swarm intelligence 1st

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丛书名： The Morgan Kaufmann series in evolutionary computation

2001年

作者： James Kennedy Russell C. Eberhart Yuhui Shi

ISBN: (数字)9780080518268

ISBN: (纸本)9781558605954

Traditional methods for creating intelligent computational systems have privileged private "internal" cognitive and computational processes. In contrast, Swarm intelligence argues that human intelligence derives from the interactions of individuals in a social world and further, that this model of intelligence can be effectively applied to artificially intelligent systems. The authors first present the foundations of this new approach through an extensive review of the critical literature in social psychology, cognitive science, and evolutionary computation. They then show in detail how these theories and models apply to a new computational intelligence methodology—particle swarms—which focuses on adaptation as the key behavior of intelligent systems. Drilling down still further, the authors describe the practical benefits of applying particle swarm optimization to a range of engineering problems. Developed by the authors, this algorithm is an extension of cellular automata and provides a powerful optimization, learning, and problem solving method. This important book presents valuable new insights by exploring the boundaries shared by cognitive science, social psychology, artificial life, artificial intelligence, and evolutionary computation and by applying these insights to the solving of difficult engineering problems. Researchers and graduate students in any of these disciplines will find the material intriguing, provocative, and revealing as will the curious and savvy computing professional.

关键词： Swarm intelligence Systems engineering distributed artificial intelligence

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Using Cognitive Radio to Deliver 'Green' Communications

Using Cognitive Radio to Deliver 'Green' Communications

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4th International Conference on Cognitive Radio Oriented Wireless Networks and Communications

作者： Grace, David Chen, Jingxin Jiang, Tao Mitchell, Paul D. Univ York Dept Elect Commun Res Grp York YO10 5DD N Yorkshire England

ISBN: (纸本)9781424434237

This paper discusses different ways of delivering 'green' cognitive radio based systems. Fundamental is the need to exploit the free spectrum available and power efficient modulation where possible. The paper describes a variable power/bandwidth efficient modulation strategy where the modulation level is adjusted by cognitively determining the assignment and use of the available spectrum, taking into account the channel occupancy probability. Battery life for different techniques is also considered. Secondly, the paper discusses ways to reduce the complexity overall of cognitive radio systems, particularly in the need for spectrum sensing by exploiting distributed artificial intelligence. Techniques presented show how it is possible to largely eliminate the need for spectrum sensing, along with the associated energy consumption, by using reinforcement learning to develop a preferred channel set in each device. Finally, the paper discusses the potential benefits of the use of antenna directionality to improve energy efficiency, and the associated problems that still must be solved before this technique can deliver 'green' radio.

关键词： cognitive radio green radio power-efficient modulation distributed artificial intelligence

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Agent-based reasoning for distributed multi-INT analysis

Agent-based reasoning for distributed multi-INT analysis

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Conference on Intelligent Computing - Theory and Applications IV

作者： Inchiosa, Mario E. Parker, Miles T. Perline, Richard NuTech Solut Inc 121 West Trade StInterstate TowerSuite 1900 Charlotte NC 28202 USA

ISBN: (纸本)0819462853

Fully exploiting the intelligence community's exponentially growing data resources will require computational approaches differing radically from those currently available. intelligence data is massive, distributed, and heterogeneous. Conventional approaches requiring highly structured and centralized data will not meet this challenge. We report on a new approach, Agent-Based Reasoning (ABR). In NIST evaluations, the use of ABR software tripled analysts' solution speed, doubled accuracy, and halved perceived difficulty. ABR makes use of populations of fine-grained, locally interacting agents that collectively reason about intelligence scenarios in a self-organizing, "bottom-up" process akin to those found in biological and other complex systems. Reproduction rules allow agents to make inferences from multi-INT data, while movement rules organize information and optimize reasoning. Complementary deterministic and stochastic agent behaviors enhance reasoning power and flexibility. Agent interaction via small-world networks-such as are found in nervous systems, social networks, and power distribution grids-dramatically increases the rate of discovering intelligence fragments that usefully connect to yield new inferences. Small-world networks also support the distributed processing necessary to address intelligence community data challenges. In addition, we have found that ABR pre-processing can boost the performance of commercial text clustering software. Finally, we have demonstrated interoperability with Knowledge Engineering systems and seen that reasoning across diverse data sources can be a rich source of inferences.

关键词： automated reasoning distributed artificial intelligence intelligence analysis text clustering GIS

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Self-Adaptive Swarm System

Self-Adaptive Swarm System

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作者： Yang, Qin University of Georgia

学位级别：Ph.D., Doctor of Philosophy

Multi-agent systems (MAS) could play a pivotal role in realizing future intelligent workspaces, especially in building so-called artificial social systems, such as self-driving cars and multi-robot systems (MRS). For example, MAS/MRS cooperates to increase mission performance in many applications, including exploration, surveillance, defense, humanitarian, and emergency missions like urban search and rescue. In such missions, complex environments such as hazardous, dynamic changing, and adversarial surroundings create a significant challenge to the agents in realizing their full potential. Therefore, this thesis addresses some pressing gaps in the literature in realizing an adaptive MAS by proposing a principled MAS cooperation framework, termed the Self-Adaptive Swarm System (SASS), which bridges communication, planning, decision-making and learning in the distributed MAS. In particular, the core scientific contributions of this thesis are as follows: 1) we define a novel human-inspired agent (robot) needs hierarchy model to consider an agent's motivation and requirements based on the current status and assigned tasks; 2) we present a priority-based distributed negotiation-agreement algorithm for realizing multi-agent tasks assignment problems, effectively avoiding plan conflicts – Here, we decompose the tasks into atomic operations and achieve MAS cooperation through a series of simple sub-tasks; 3) we introduce a new needs-based agent trust and cooperation mechanism to create needs-driven relationships among multiple agents in challenging environments; 4) we build a new hierarchical utility tree to realize game-theoretic solutions for the cooperating MAS in the presence of adversarial opponent agents; 5) we propose a novel Bayesian strategy networks (BSN) applied to deep reinforcement learning by decomposing tasks into multiple sub-level actions and obtaining the optimal agent policies in unknown and challenging environments.

关键词： distributed artificial intelligence Multi-agent systems Multi-robot systems Robotics Swarm intelligence

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Research on Smart Grid Simulation Framework Based on distributed Intelligent System

Research on Smart Grid Simulation Framework Based on Distrib...

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International Conference on Power System Technology (PowerCon)

作者： Dong, Lei Li, Ye Liu, Kewen Pu, Tianjiao Liu, Guangyi North China Elect Power Univ Beijing Peoples R China CEPRI Beijing Peoples R China

ISBN: (纸本)9781479950324

Based on distributed intelligent system smart grid simulation is a new generation simulation considering with distributed generation feed-in and demand response for the future grid development, which has the characteristics of distributed artificial intelligence. This paper builds structures, models and synergy mechanisms of agents in smart grid simulation and achieves the real-time perception of whole network through scenario decision-making. On these bases, this paper presents a complete smart grid simulation framework based on multi-agent system and scenario decision-making. Finally, an example verifies the effectiveness of this smart grid distributed simulation framework.

关键词： Smart Grid Simulation Multi-Agent System Scenario Decision-Making distributed artificial intelligence

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