Agent mining and agent learning [1,4] refer to the methodologies, technology, tools and systems that synthesize multiagent technology, data mining and knowledge discovery, machine learning and other relevant artificial intelligence, intelligent systems, and data science theories and techniques such as statistics and semantic web for better addressing issues that cannot be tackled by any single techniques with the same quality and performance.
With the recent advancement in deep reinforcement learning, behavior informatics, deep learning, and interaction networks, agent mining and learning face new development opportunities to tackle complex intelligent systems and problems that require or are likely to be more effectively handled by integrating individual technologies and methods. It also fosters unique symbiosis and symbionts, e.g., autonomous hard and soft robots, drones, and driverless cars, that combine the advantages from the corresponding constituent systems.
With the new-generation AI, deep neural network, deep reinforcement learning, game theory, and data science, agent mining faces enormous opportunities that it was proposed over a decade ago, including but being not limited to:
- Agent mining for federated learning: multiagent distributed learning systems in heterogenous networks and cloud;
- Agent-based knowledge discovery: building multiagent data mining and learning systems for distributed knowledge discovery and machine learning;
- Multiagent learning: learning from multiagent data, behaviors, interactions, and environment to improve multiagent system design, architecture, mechanism, planning, performance, operation
- Multiagent behavior analytics: analyzing agent behaviors and interactions, communications to improve their action-taking and performance;
- Multiagent risk analytics: estimating and predicting risk in multiagent systems and individual agent decision-making;
- Cybersecurity learning: learning adversary intent, learning adversary communications and interactions, optimize psychological and mental models, and data-driven defender communication, planning and action recommendations;
- Active multiagent systems: making multiagent systems to actively learn from the environment and dynamics to reinforce action-taking,
- Deep reinforcement multiagent learning: learning to reinforce value and policy in complex system settings, environment, and big data.
More research topics and opportunities in agent mining can be found in [1-3].
The following includes proceedings for the relevant workshops:
|Longbing Cao (Ed.). Data Mining and Multi-agent Integration, ISBN: 978-1-4419-0522-2, Springer, 2009. BibTeX|
|Longbing Cao; Yifeng Zeng, Bo An, Andreas L. Symeonidis, Vladimir Gorodetsky, Frans Coenen, Philip S. Yu (Eds.). Agents and Data Mining Interaction, 10th International Workshop, ADMI 2014, Paris, France, May 5-9, 2014, Revised Selected Papers. Lecture Notes in Computer Science 9145, Springer 2015, ISBN 978-3-319-20229-7, 2013.|
|Cao, L.; Zeng, Y.; Symeonidis, A.L.; Gorodetsky, V.; Yu, P.S.; Singh, M.P. (Eds.). Agents and Data Mining Interaction, ADMI2012, LNAI 7607, Springer, 2013|
|Cao, L.; Bazzan, A.L.C.; Symeonidis, A.L.; Gorodetsky, V.; Weiss, G.; Yu, P.S. (Eds.).
Agents and Data Mining Interaction, ADMI2011, LNAI 7103, Springer, 2011
|Longbing Cao, Bazzan, A.L.C.; Gorodetsky, V.; Mitkas, P.A.; Weiss, G.; Yu, P.S. (Eds.).
Agents and Data Mining Interaction (edited), ADMI10, LNAI 5980, Springer, 2010
|Longbing Cao, Gorodetsky, A.E.; Liu, Jiming; Weiss, Gerhard; Yu, Philipp S. Yu.
Agents and Data Mining Interaction (edited), ADMI09, LNAI5680, Springer, 2009
|Vladimir Gorodetsky, Chengqi Zhang, Victor Skormin, Longbing Cao.
Autonomous Intelligent Systems: Multi-Agents and Data Mining (AIS-ADM2007), LNAI4476, Springer, 2007
Other references on agent mining and learning:
 Longbing Cao, Gerhard Weiss, Philip S Yu. A Brief Introduction to Agent Mining. Journal of Autonomous Agents and Multi-Agent Systems, 25:419-424, 2012. BibTeX
 Longbing Cao. Introduction to Agent Mining Interaction and Integration, in Data Mining and Multi-agent Integration, 3-36, Springer. BibTeX
 Longbing Cao, Vladimir Gorodetsky, Pericles A. Mitkas. Guest Editors’ Introduction: Agents and Data Mining. Special Issue with IEEE Intelligent Systems 24(3): 14-15 (2009).
 Longbing Cao, Vladimir Gorodetsky, Pericles A. Mitkas. Agent Mining: The Synergy of Agents and Data Mining, IEEE Intelligent Systems, vol. 24, no. 3, 64-72, May/June 2009. BibTeX
 Longbing Cao. Integrating Agent, Service and Organizational Computing, Int. J. on Software Engineering and Knowledge Engineering, 18(5):573-596, 2008. BibTeX
 Longbing Cao, Dan Luo, Chengqi Zhang. Ubiquitous Intelligence in Agent Mining. ADMI 2009: 23-35. BibTeX
Refer to the agent mining website for more information about its relevant activities and publications.