580 research outputs found
Distributed coordination in unstructured intelligent agent societies
Current research on multi-agent coordination and distributed problem
solving is still not robust or scalable enough to build large real-world
collaborative agent societies because it relies on either centralised components
with full knowledge of the domain or pre-defined social structures.
Our approach allows overcoming these limitations by using
a generic coordination framework for distributed problem solving on
totally unstructured environments that enables each agent to decompose
problems into sub-problems, identify those which it can solve
and search for other agents to delegate the sub-problems for which it
does not have the necessary knowledge or resources. Regarding the
problem decomposition process, we have developed two distributed
versions of the Graphplan planning algorithm. To allow an agent
to discover other agents with the necessary skills for dealing with
unsolved sub-problems, we have created two peer-to-peer search algorithms
that build and maintain a semantic overlay network that
connects agents relying on dependency relationships, which improves
future searches. Our approach was evaluated using two different scenarios,
which allowed us to conclude that it is efficient, scalable and
robust, allowing the coordinated distributed solving of complex problems
in unstructured environments without the unacceptable assumptions
of alternative approaches developed thus far.As abordagens actuais de coordenação multi-agente e resolução distribuída de problemas não são suficientemente robustas ou escaláveis
para criar sociedades de agentes colaborativos uma vez que assentam
ou em componentes centralizados com total conhecimento do
domínio ou em estruturas sociais pré-definidas. A nossa abordagem
permite superar estas limitações através da utilização de um algoritmo
genérico de coordenação de resolução distribuída de problemas
em ambientes totalmente não estruturados, o qual permite a cada
agente decompor problemas em sub-problemas, identificar aqueles que
consegue resolver e procurar outros agentes a quem delegar os subproblemas
para os quais não tem conhecimento suficiente. Para a
decomposição de problemas, criámos duas versões distribuídas do algoritmo
de planeamento Graphplan. Para procurar os agentes com as
capacidades necessárias à resolução das partes não resolvidas do problema,
criámos dois algoritmos de procura que constroem e mantêm
uma camada de rede semântica que relaciona agentes dependentes
com o fim de facilitar as procuras. A nossa abordagem foi avaliada
em dois cenários diferentes, o que nos permitiu concluir que ´e uma
abordagem eficiente, escalável e robusta, possibilitando a resolução
distribuída e coordenada de problemas complexos em ambientes não
estruturados sem os pressupostos inaceitáveis em que assentava o trabalho
feito até agora
Improving Collaborative Learning Using Pervasive Embedded System-Based Multi-Agent Information and Retrieval Framework in Educational Systems
E-learning is a form of Technology SupportedEducation where the medium of instruction is throughDigital Technologies, particularly Computer Technology.An instance is the use of search engines like Google andYahoo, which aid Collaborative Learning. However, thewidespread provision of distributed, semi-structuredinformation resources such as the Web has obviouslybrought a lot of benefits; but it also has a number ofdifficulties. These difficulties include people gettingoverwhelmed by the sheer amount of information available,making it hard for them to filter out the junk andirrelevancies and focus on what is important, and also toactively search for the right information. Also, people easilyget bored or confused while browsing the Web because ofthe hypertext nature of the web, while making it easy to linkrelated documents together, it can also be disorienting. Toalleviate these problems, the Web Information Food ChainModel was introduced. How effective has this been with thedynamic nature of computing technologies? Pervasivecomputing devices enable people to gain immediate accessto information and services anywhere, anytime, withouthaving to carry around heavy and impractical computingdevices. Thus, the bulky PCs become less attractive andbeing slowly eroded with the development of a newgeneration of smart devices like wireless PDAs, smartphones, etc. These embedded devices are characterized bybeing unobtrusively embedded; completely connected;intuitively intelligent; effortlessly portable and mobile; andconstantly on and available. This paper presents the use ofembedded systems and Intelligent Agent-Based WebInformation Food Chain Model in Multi-Agent Informationand Retrieval Framework (IIFCEMAF), to realizing fullpotentials of the internet, for users’ improved system ofcollaborative e-learning in education
Overcoming barriers and increasing independence: service robots for elderly and disabled people
This paper discusses the potential for service robots to overcome barriers and increase independence of
elderly and disabled people. It includes a brief overview of the existing uses of service robots by disabled and elderly
people and advances in technology which will make new uses possible and provides suggestions for some of these new
applications. The paper also considers the design and other conditions to be met for user acceptance. It also discusses
the complementarity of assistive service robots and personal assistance and considers the types of applications and
users for which service robots are and are not suitable
Workshop on Modelling of Objects, Components, and Agents, Aarhus, Denmark, August 27-28, 2001
This booklet contains the proceedings of the workshop Modelling of Objects, Components, and Agents (MOCA'01), August 27-28, 2001. The workshop is organised by the CPN group at the Department of Computer Science, University of Aarhus, Denmark and the "Theoretical Foundations of Computer Science" Group at the University of Hamburg, Germany. The papers are also available in electronic form via the web pages: http://www.daimi.au.dk/CPnets/workshop01
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Knowledge search for new product development: a multi-agent based methodology
Manufacturers are the leaders in developing new products to drive productivity. Higher productivity means more products based on the same materials, energy, labour, and capitals. New product development plays a critical role in the success of manufacturing firms. Activities in the product development process are dependent on the knowledge of new product development team members. Increasingly, many enterprises consider effective knowledge search to be a source of competitive advantage.
This research presents an exploratory case study conducted at an aircraft manufacturer. This investigation uncovered six, empirically derived and theoretically informed, problems to enterprise knowledge search. They have been articulated as (i) the effectual web bandwidth limits search speed; (ii) less relevant search results based on word-frequency recognition models of search engine; (iii) un-useable techniques for enterprise search; (iv) rigour security, reliability, and company policy; (v) poor search performance about unstructured enterprise knowledge; (vi) the lack of tacit knowledge sharing. Existing search methodologies have focused on the internet search, rather than providing effective search for enterprise.
This research aim is developed to assist the manufacturing enterprise in meeting the industrial requirements in the following way: a methodology and system that can improve the information and knowledge search performance in new product development process. Based on the exploratory case findings, a knowledge search methodology and system has been developed. Agent technology is used to fulfil the requirements of enterprise search. Some initial tests were conducted to better understand implementation issues and future deployment of the methodology and system in practice
Fostering Distributed Business Logic in Open Collaborative Networks: an integrated approach based on semantic and swarm coordination
Given the great opportunities provided by Open Collaborative Networks (OCNs), their success depends on the effective integration of composite business logic at all stages. However, a dilemma between cooperation and competition is often found in environments where the access to business knowledge can provide absolute advantages over the competition. Indeed, although it is apparent that business logic should be automated for an effective integration, chain participants at all segments are often highly protective of their own knowledge. In this paper, we propose a solution to this problem by outlining a novel approach with a supporting architectural view. In our approach, business rules are modeled via semantic web and their execution is coordinated by a workflow model. Each company’s rule can be kept as private, and the business rules can be combined together to achieve goals with defined interdependencies and responsibilities in the workflow. The use of a workflow model allows assembling business facts together while protecting data source. We propose a privacy-preserving perturbation technique which is based on digital stigmergy. Stigmergy is a processing schema based on the principle of self-aggregation of marks produced by data. Stigmergy allows protecting data privacy, because only marks are involved in aggregation, in place of actual data values, without explicit data modeling. This paper discusses the proposed approach and examines its characteristics through actual scenarios
Reframing superintelligence: comprehensive AI services as general intelligence
Studies of superintelligent-level systems have typically posited AI functionality that plays the role of a mind in a rational utility-directed agent, and hence employ an abstraction initially developed as an idealized model of human decision makers. Today, developments in AI technology highlight intelligent systems that are quite unlike minds, and provide a basis for a different approach to understanding them: Today, we can consider how AI systems are produced (through the work of research and development), what they do (broadly, provide services by performing tasks), and what they will enable (including incremental yet potentially thorough automation of human tasks).
Because tasks subject to automation include the tasks that comprise AI research and development, current trends in the field promise accelerating AI-enabled advances in AI technology itself, potentially lead- ing to asymptotically recursive improvement of AI technologies in distributed systems, a prospect that contrasts sharply with the vision of self-improvement internal to opaque, unitary agents.
The trajectory of AI development thus points to the emergence of asymptotically comprehensive, superintelligent-level AI services that— crucially—can include the service of developing new services, both narrow and broad, guided by concrete human goals and informed by strong models of human (dis)approval. The concept of comprehensive AI services (CAIS) provides a model of flexible, general intelligence in which agents are a class of service-providing products, rather than a natural or necessary engine of progress in themselves.
Ramifications of the CAIS model reframe not only prospects for an intelligence explosion and the nature of advanced machine intelligence, but also the relationship between goals and intelligence, the problem of harnessing advanced AI to broad, challenging problems, and fundamental considerations in AI safety and strategy. Perhaps surprisingly, strongly self-modifying agents lose their instrumental value even as their implementation becomes more accessible, while the likely context for the emergence of such agents becomes a world already in possession of general superintelligent-level capabilities. These prospective capabilities, in turn, engender novel risks and opportunities of their own.
Further topics addressed in this work include the general architecture of systems with broad capabilities, the intersection between symbolic and neural systems, learning vs. competence in definitions of intelligence, tactical vs. strategic tasks in the context of human control, and estimates of the relative capacities of human brains vs. current digital systems
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Knowledge based approach to flexible workflow management systems
This thesis was submitted for the degree of Doctor of Philosophy and awarded the Korea Advanced Institute of Science and Technology (KAIST).Today's business environments are characterized by dynamic and uncertain environments. In order to effectively support business processes in such contexts, workflow management systems must be able to adapt themselves effectively. In this dissertation, the workflow is redefined in
concept and represented with a set of business rules. Business rules play a central role in
organizational workflows in context of cooperation among actors. To achieve business goals, they constrain the flow of works, use of resources, and responsibility mapping between tasks and actors using role concept. Business rules are explicitly modeled in the Knowledge-based Workflow Model (KWM) using frames.
To increase the adaptability of workflow management system, KWM has several distinctive
features. First, it increases expressiveness of workflow model so that exception handling rules
and responsibility mapping rules between tasks and actors as well as task scheduling rules are
explicitly modeled. Secondly, formal definition of KWM enables one to define and to analyze correctness of workflow schema. Knowledge-based approach enables more powerful analysis on workflow schema including checking consistency and compactness of routing rules as well as terminality of a workflow. Thirdly, providing change propagation mechanism which assures
correctness of workflow after the modification of workflow schema increases adaptability.
Change propagation rules for the modification primitives are provided to manage workflow
evolution. On the other hand, metarules that control rules in KWM are used to handle exceptions that occur in a running workflow instance. Workflow participants can easily change workflow schema of a workflow instance with the support of extra rules and a metarule.
Based on KWM, K-WFMS (Knowledge-based WorkFlow Management System) has been implemented in client/server architecture. Inference shell of knowledge-based systems is employed for enactment of business rules and integrated with database systems. From a real application based on the KWM architecture, it has been shown that system performance can increase notably by reducing the number of rules and facts that are used in the course of workflow enactment
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