Abstract
The 2009 International Workshop on Agents and Data Mining Interaction (ADMI 2009) was a joint event with AAMAS 2009. In recent years, agents and data mining interaction (ADMI), or agent mining for short, has emerged as a very promising research field. Following the success of ADMI 2006 in Hong Kong, ADMI 2007 in San Jose, and ADMI 2008 in Sydney, the ADMI 2009 workshop in Budapest provided a premier forum for sharing research and engineering results, as well as potential challenges and prospects encountered in the synergy between agents and data mining.
As usual, the ADMI workshop encouraged and promoted theoretical and applied research and development, which aims at:
Exploiting agent-driven data mining and demonstrating how intelligent agent technology can contribute to critical data mining problems in theory and practice
Improving data mining-driven agents and showing how data mining can strengthen agent intelligence in research and practical applications.
Exploring the integration of agents and data mining toward super-intelligent information processing and systems.
Identifying challenges and directions for future research on the synergy between agents and data mining.
ADMI 2009 featured two invited talks and twelve selected papers. The first invited talk was on “Agents and Data Mining in Bioinformatics,” with the second focusing on “Knowledge-Based Reinforcement Learning.” The ten accepted papers are from seven countries. A majority of submissions came from European countries, indicating the boom of ADMI research in Europe. In addition, the two invited papers addressed fundamental issues related to agent-driven data mining, data mining-driven agents, and agent mining applications.
The proceedings of the ADMI workshops will be published as part of the LNAI series by Springer. We appreciate the support of Springer, and in particular Alfred Hofmann.
As usual, the ADMI workshop encouraged and promoted theoretical and applied research and development, which aims at:
Exploiting agent-driven data mining and demonstrating how intelligent agent technology can contribute to critical data mining problems in theory and practice
Improving data mining-driven agents and showing how data mining can strengthen agent intelligence in research and practical applications.
Exploring the integration of agents and data mining toward super-intelligent information processing and systems.
Identifying challenges and directions for future research on the synergy between agents and data mining.
ADMI 2009 featured two invited talks and twelve selected papers. The first invited talk was on “Agents and Data Mining in Bioinformatics,” with the second focusing on “Knowledge-Based Reinforcement Learning.” The ten accepted papers are from seven countries. A majority of submissions came from European countries, indicating the boom of ADMI research in Europe. In addition, the two invited papers addressed fundamental issues related to agent-driven data mining, data mining-driven agents, and agent mining applications.
The proceedings of the ADMI workshops will be published as part of the LNAI series by Springer. We appreciate the support of Springer, and in particular Alfred Hofmann.
Original language | English |
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Publisher | Springer Berlin Heidelberg |
Number of pages | 211 |
Edition | 1st |
ISBN (Electronic) | 9783642036033 |
ISBN (Print) | 9783642036026 |
DOIs | |
Publication status | Published - 30 Jul 2009 |
Event | 4th International Workshop on Agents and Data Mining Interaction, ADMI 2009 - Budapest, Hungary Duration: 10 May 2009 → 15 May 2009 https://link.springer.com/book/10.1007/978-3-642-03603-3 |
Publication series
Name | Lecture Notes in Computer Science |
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Volume | 5680 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Name | Lecture Notes in Artificial Intelligence |
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ISSN (Print) | 2945-9133 |
ISSN (Electronic) | 2945-9141 |
Name | ADMI: International Workshop on Agents and Data Mining Interaction |
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User-Defined Keywords
- agent architectures
- agent assignment
- agent interaction
- agent systems implementation
- agent technology
- agent-based simulation
- agents
- autonomous agent
- autonomous agents
- business benefit
- classifier generation
- collaborative filtering
- community detection
- complex systems
- data mining