Market-Inspired Approach to Collaborative Learning

Tožička, Jan; Jakob, Michal; Pěchouček, Michal

doi:10.1007/11839354_16

Cited by 7 publications

(6 citation statements)

References 11 publications

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“…This model is used for diagnostic of the manufacturing alarm situations [11,68]. The Office for Naval Research supported a research in distributed learning in the civilian naval domain, where different learning, semi-collaborative agents monitor different parts of Mediterranean Sea and sharing selected observations and selected hypotheses [86]. Loosely related is a use of agent technologies for aircraft maintenance.…”

Section: Distributed Diagnosticsmentioning

confidence: 99%

Industrial deployment of multi-agent technologies: review and selected case studies

Pěchouček

Mařı́k

2008

Auton Agent Multi-Agent Syst

Self Cite

202

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This paper reports on industrial deployment of multi-agent systems and agent technology. It provides an overview of several application domains and an in-depth presentation of four specific case studies. The presented applications and deployment domains have been analyzed. The analysis indicates that despite strong industrial involvement in this field, the full potential of the agent technology has not been fully utilized yet and that not all of the developed agent concepts and agent techniques have been completely exploited in industrial practice. In the paper, the key obstacles for wider deployments are listed and potential future challenges are discussed.

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Section: Distributed Diagnosticsmentioning

confidence: 99%

Industrial deployment of multi-agent technologies: review and selected case studies

Pěchouček

Mařı́k

2008

Auton Agent Multi-Agent Syst

Self Cite

202

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“…An agentbased method for integrating distributed cluster analysis processes using density estimation is presented by Klusch et al [13] which is also specifically designed for a particular learning algorithm. The same is true of [22,23] which both present market-based mechanisms for aggregating the output of multiple learning agents, even though these approaches consider more interesting interaction mechanisms among learners.…”

Section: Related Workmentioning

confidence: 73%

“…In section 4 we analyse the performance of each of these two classes for different choices of m. It is noteworthy that this agent-based distributed data mining system is one of the simplest conceivable instances of our abstract architecture. While we have previously applied it also to a more complex market-based architecture using Inductive Logic Programming learners in a transport logistics domain [22], we believe that the system described here is complex enough to illustrate the key design decisions involved in using our framework and provides simple example solutions for these design issues. Figure 3 shows results obtained from simulations with three learning agents in the above system using the k-means and k-medoids clustering methods respectively.…”

Section: Agent-based Distributed Learning System Designmentioning

confidence: 98%

A framework for agent-based distributed machine learning and data mining

Tožička

Rovatsos

Pěchouček

2007

Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems

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This paper proposes a framework for agent-based distributed machine learning and data mining based on (i) the exchange of meta-level descriptions of individual learning processes among agents and (ii) online reasoning about learning success and learning progress by learning agents. We present an abstract architecture that enables agents to exchange models of their local learning processes and introduces a number of different methods for integrating these processes. This allows us to apply existing agent interaction mechanisms to distributed machine learning tasks, thus leveraging the powerful coordination methods available in agent-based computing, and enables agents to engage in meta-reasoning about their own learning decisions. We apply this architecture to a real-world distributed clustering application to illustrate how the conceptual framework can be used in practical systems in which different learners may be using different datasets, hypotheses and learning algorithms. We report on experimental results obtained using this system, review related work on the subject, and discuss potential future extensions to the framework. General TermsTheory

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“…Mobile agents are able to adjust to changing conditions such as network nodes, devices, media services or other agents which may be available or unavailable dynamically. Multi-agent systems can be created that use agent communication to collaborate and work together to provide services, reach a desired outcome or achieve a common execution goal [5,6].…”

Section: Mobile Agentsmentioning

confidence: 99%

A Distributed Agent Media Access Framework

Gelowitz¹,

Benedicenti²,

Paranjape³

2014

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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This paper presents a distributed software mobile agent framework for media access. The access and delivery of media is enhanced by an agent architecture framework that utilizesmobile agent characteristics to provide access and delivery of media. Migrating agents to the media source and destination devices enables the framework to discover the properties of devices and media within networks dynamically. The mobile agents in this framework make decisions and work together to enable access and delivery of media through the transcoding of media based on the properties of the data path and device constraints.

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Market-Inspired Approach to Collaborative Learning

Cited by 7 publications

References 11 publications

Industrial deployment of multi-agent technologies: review and selected case studies

Industrial deployment of multi-agent technologies: review and selected case studies

A framework for agent-based distributed machine learning and data mining

A Distributed Agent Media Access Framework

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