A Multi-intelligent Agent Architecture for Knowledge Extraction: Novel Approaches for Automatic Production Rules Extraction

Kadhim, Mohammed Abbas; Alam, M. Afshar; Kaur, Harleen

doi:10.14257/ijmue.2014.9.2.10

Cited by 17 publications

(8 citation statements)

References 31 publications

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“…[34] Another excellent benefit of a MAS for our purposes is the capability of interaction with humans, providing for expert, real-time feedback, such as interpretation or interpolation of findings to set the course for ongoing activities. [35] SPADE (Smart Python multi-Agent Development Environment) [12], an open source and FIPA [36] compliant multi-agent platform was used to construct the MAS for this research. SPADE employs the XMPP/Jabber [37] messaging protocol for inter-agent communication, facilitating the exchange of requests and responses between software agents and humans in the same Jabber domain.…”

Section: E Putting It Together In a Multi-agent Systemmentioning

confidence: 99%

Mining information assurance data with a hybrid intelligence/multi-agent system

Fowler

Hammell

2015

2015 IEEE/ACIS 14th International Conference on Computer and Information Science (ICIS)

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In today's world an unprecedented and overwhelming volume of information is available to organizations of all sizes; the resultant "information overload" problem is well documented. This problem is especially challenging in the world of network intrusion detection. In this research we demonstrate that in the domain of offline network data mining, several different data mining algorithms (hybrid intelligence) operating concurrently on the same data in a multiagent system, and reporting their results for interpretation and interpolation, will yield more accurate characterizations and extract more knowledge than a single data mining algorithm acting on its own. We also briefly outline the design and functions of our SPADE-based multi-agent system, demonstrating its effectiveness at automating multi-path data mining tasks. This code ingests a PCAP, semi-autonomously preprocesses it and feeds it into Weka for processing/mining using several different algorithms. It then presents results to a human operator providing a feedback insertion point.

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Section: E Putting It Together In a Multi-agent Systemmentioning

confidence: 99%

Mining information assurance data with a hybrid intelligence/multi-agent system

Fowler

Hammell

2015

2015 IEEE/ACIS 14th International Conference on Computer and Information Science (ICIS)

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“…The inconsistent nature of data can generate results which can provide null benefit to end users. In order to improve the quality of results and dis-cover knowledge from big data, preprocessing technique should be involved for am-putation of inconsistent values from datasets [22,27,28,29,30,31]. However preprocessing tends to be critical technique among the data mining process which involves data cleaning, data integration, data transformation and data reduction.…”

Section: Data Preprocessingmentioning

confidence: 99%

A Spectrum of Big Data Applications for Data Analytics

Chauhan¹,

Kaur

2015

Adaptation, Learning, and Optimization

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Abstract. As technology is gaining its insights, vast amount of data is getting collected from various resources. Foremost complex nature of data is providing challenging task among the researchers to store, process and analyze big data. At present, big data analytics tends to be an emerging domain which potentially has limitless opportunities for possible future outcomes. However, big data mining provides application capabilities to extract hidden information from large volumes of data for knowledge discovery process. In fact big data mining is demonstration varied challenges and vast opportunity among researchers and scientist for another upcoming decade. This chapter provides broad view of big data in medical application domain. In addition, a framework which can handle big data by using several preprocessing and data mining technique to discover hidden knowledge from large scale databases is designed and implemented. The proposed chapter also discuss the challenges in big data to gain insight knowledge for future outcomes.

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“…The main ones include first-order predicate logic, production systems, artificial neural networks, frame representation, ontologies such as semantic web and semantic networks, multiattributes and multi-values structures, and multi valued logic [12,17,32,33,39,40,42,43,44,46]. Some of these methods are closely related to fuzzy logic.…”

Section: Fuzzy Logic As Agents' Knowledge Representationmentioning

confidence: 99%

Fuzzy logic in the multi-agent financial decision support system

Korczak

Hernes

Bac

2015

Annals of Computer Science and Information Systems

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Abstract-The article presents the application of a fuzzy logic in building the trading agents of the a-Trader system. The system supports investment decisions on the FOREX market. The first part of the article contains a discussion related to the use of fuzzy logic as an agents' knowledge representation. Next, the algorithms of the selected fuzzy logic buy-sell decision agents are presented. In the last part of the article the agent performance is evaluated on real FOREX data.

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A Multi-intelligent Agent Architecture for Knowledge Extraction: Novel Approaches for Automatic Production Rules Extraction

Abstract: In this paper, multi-intelligent agent architecture has been proposed for automatic knowledge extraction from its resources (domain experts and text documents

Cited by 17 publications

References 31 publications

Mining information assurance data with a hybrid intelligence/multi-agent system

Mining information assurance data with a hybrid intelligence/multi-agent system

A Spectrum of Big Data Applications for Data Analytics

Fuzzy logic in the multi-agent financial decision support system

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