Exploiting Domain Knowledge in Making Delegation Decisions

@inproceedings{conf/admi/EmeleNSP11, added-at = {2011-12-19T00:00:00.000+0100}, author = {Emele, Chukwuemeka David and Norman, Timothy J. and Sensoy, Murat and Parsons, Simon}, biburl = {http://www.bibsonomy.org/bibtex/20a08b683088443f1fd36d6ef28bf6615/dblp}, booktitle = {ADMI}, crossref = {conf/admi/2011}, editor = {Cao, Longbing and Bazzan, Ana L. C. and Symeonidis, Andreas L. and Gorodetsky, Vladimir and Weiss, Gerhard and Yu, Philip S.}, ee = {http://dx.doi.org/10.1007/978-3-642-27609-5_9}, interhash = {1d7e7f8554e8bdb3d43c32e02aeabcec}, intrahash = {0a08b683088443f1fd36d6ef28bf6615}, isbn = {978-3-642-27608-8}, keywords = {dblp}, pages = {117-131}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, timestamp = {2011-12-19T00:00:00.000+0100}, title = {Exploiting Domain Knowledge in Making Delegation Decisions.}, url = {http://dblp.uni-trier.de/db/conf/admi/admi2011.html#EmeleNSP11}, volume = 7103, year = 2011 }Postprin

Calibrating the atomic balance by carbon nanoclusters

Carbon atoms are counted at near atomic-level precision using a scanning transmission electron microscope calibrated by carbon nanocluster mass standards. A linear calibration curve governs the working zone from a few carbon atoms up to 34,000 atoms. This linearity enables adequate averaging of the scattering cross sections, imparting the experiment with near atomic-level precision despite the use of a coarse mass reference. An example of this approach is provided for thin layers of stacked graphene sheets. Suspended sheets with a thickness below 100 nm are visualized, providing quantitative measurement in a regime inaccessible to optical and scanning probe methods

Towards the development of agent-based organizations through MDD

Electronic version of an article published as International Journal on Artificial Intelligence Tools, 22, 2, 2013, DOI 10.1142/S0218213013500024 © World Scientific Publishing Company http://www.worldscientific.com/worldscinet/ijaitVirtual Organizations are a mechanism where agents can demonstrate their social skills since they can work in a cooperative and collaborative way. Nonetheless, the development of organizations using Multi-Agent Systems (MAS) requires extensive experience in different methodologies and platforms. Model-Driven Development (MDD) is a technique for generating application code that is developed from basic models and meta-models using a variety of automatic transformations. This paper presents an approach to develop and deploy organization-oriented Multi-Agent Systems using a model-driven approach. Based on this idea, we introduce a relatively generic agent-based meta-model for a Virtual Organization, which was created by a comprehensive analysis of the organization-oriented methodologies used in MAS. Following the MDD approach, the concepts and relationships obtained were mapped into two different platforms available for MAS development, allowing the validation of our proposal. In this way, the resultant approach can generate Virtual Organization deployments from unified meta-models, facilitating the development process of agent-based software from the user point of view.This work was partially supported by TIN2009-13839-C03-01 and PROMETEO/2008/051 projects of the Spanish government and CONSOLIDER-INGENIO 2010 under grant CSD2007-00022.Agüero, J.; Carrascosa Casamayor, C.; Rebollo Pedruelo, M.; Julian Inglada, VJ. (2013). Towards the development of agent-based organizations through MDD. International Journal on Artificial Intelligence Tools. 22(2):1-34. https://doi.org/10.1142/S0218213013500024S134222Argente, E., Julian, V., & Botti, V. (2006). Multi-Agent System Development Based on Organizations. Electronic Notes in Theoretical Computer Science, 150(3), 55-71. doi:10.1016/j.entcs.2006.03.005Bézivin, J. (2005). On the unification power of models. Software & Systems Modeling, 4(2), 171-188. doi:10.1007/s10270-005-0079-0Bresciani, P., Perini, A., Giorgini, P., Giunchiglia, F., & Mylopoulos, J. (2004). Tropos: An Agent-Oriented Software Development Methodology. Autonomous Agents and Multi-Agent Systems, 8(3), 203-236. doi:10.1023/b:agnt.0000018806.20944.efFoster, I., Kesselman, C., & Tuecke, S. (2001). The Anatomy of the Grid: Enabling Scalable Virtual Organizations. The International Journal of High Performance Computing Applications, 15(3), 200-222. doi:10.1177/109434200101500302Hahn, C., Madrigal-Mora, C., & Fischer, K. (2008). A platform-independent metamodel for multiagent systems. Autonomous Agents and Multi-Agent Systems, 18(2), 239-266. doi:10.1007/s10458-008-9042-0HORLING, B., & LESSER, V. (2004). A survey of multi-agent organizational paradigms. The Knowledge Engineering Review, 19(4), 281-316. doi:10.1017/s0269888905000317Huhns, M. N., & Singh, M. P. (2005). Service-oriented computing: key concepts and principles. IEEE Internet Computing, 9(1), 75-81. doi:10.1109/mic.2005.21Huhns, M. N., Singh, M. P., Burstein, M., Decker, K., Durfee, E., Finin, T., … Zavala, L. (2005). Research Directions for Service-Oriented Multiagent Systems. IEEE Internet Computing, 9(6), 65-70. doi:10.1109/mic.2005.132Kolp, M., Giorgini, P., & Mylopoulos, J. (2006). Multi-Agent Architectures as Organizational Structures. Autonomous Agents and Multi-Agent Systems, 13(1), 3-25. doi:10.1007/s10458-006-5717-6OHTANI, T., CASE, S., AZARMI, N., & THINT, M. (2002). AN INTELLIGENT SYSTEM FOR MANAGING AND UTILIZING INFORMATION RESOURCES OVER THE INTERNET. International Journal on Artificial Intelligence Tools, 11(01), 117-138. doi:10.1142/s0218213002000800Omicini, A., Ricci, A., & Viroli, M. (2005). RBAC for Organisation and Security in an Agent Coordination Infrastructure. Electronic Notes in Theoretical Computer Science, 128(5), 65-85. doi:10.1016/j.entcs.2004.11.045Papazoglou, M. P., & Georgakopoulos, D. (2003). Introduction. Communications of the ACM, 46(10), 24. doi:10.1145/944217.944233Papazoglou, M. P., Traverso, P., Dustdar, S., & Leymann, F. (2007). Service-Oriented Computing: State of the Art and Research Challenges. Computer, 40(11), 38-45. doi:10.1109/mc.2007.400Selic, B. (2003). The pragmatics of model-driven development. IEEE Software, 20(5), 19-25. doi:10.1109/ms.2003.1231146SKARMEAS, N. P., & CLARK, K. L. (2002). COMPONENT BASED AGENT CONSTRUCTION. International Journal on Artificial Intelligence Tools, 11(01), 139-163. doi:10.1142/s0218213002000812Zambonelli, F., Jennings, N. R., & Wooldridge, M. (2003). Developing multiagent systems. ACM Transactions on Software Engineering and Methodology, 12(3), 317-370. doi:10.1145/958961.95896

Introduction to domain driven data mining

The mainstream data mining faces critical challenges and lacks of soft power in solving real-world complex problems when deployed. Following the paradigm shift from 'data mining' to 'knowledge discovery', we believe much more thorough efforts are essential for promoting the wide acceptance and employment of knowledge discovery in real-world smart decision making. To this end, we expect a new paradigm shift from 'data-centered knowledge discovery' to 'domain-driven actionable knowledge discovery'. In the domain-driven actionable knowledge discovery, ubiquitous intelligence must be involved and meta-synthesized into the mining process, and an actionable knowledge discovery-based problem-solving system is formed as the space for data mining. This is the motivation and aim of developing Domain Driven Data Mining (D 3 M for short). This chapter briefs the main reasons, ideas and open issues in D 3 M. © 2009 Springer US

Testing adaptive local hyperplane for multi-class classification by double cross-validation

Adaptive Local Hyperplane (ALH) is a recently proposed classifier for the multi-class classification problems and it has shown encouraging performance in many pattern recognition problems. However, ALH's performance over many general classification datasets has only been tested by using a single loop of cross-validation procedure, where the whole datasets are used for both hyper-parameter determination and accuracy estimation. This procedure is appropriate for classifier performance comparison, but the produced results are likely to be optimistic for classifier accuracy estimation on new datasets. In this paper, we test the performance of ALH as well as several other benchmark classifiers by using two loops of cross-validation (a.k.a. double resampling) procedure, where the inner loop is used for hyper-parameter determination and the outer loop is used for accuracy estimation. With such a testing scheme, the classification accuracy of a tested classifier can be evaluated in a more strict way. The experimental results indicate the superior performance of the ALH classifier with respect to the traditional classifiers including Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA), Classification Tree (Tree) and K-local Hyperplane distance Nearest Neighbor (HKNN). These results imply that the ALH classifier might become a useful tool for the pattern recognition tasks. © 2010 IEEE

Class association rule mining with multiple imbalanced attributes

In this paper, we propose a novel framework to deal with data imbalance in class association rule mining. In each class association rule, the right-hand is a target class while the left-hand may contain one or more attributes. This framework is focused on the multiple imbalanced attributes on the left-hand. In the proposed framework, the rules with and without imbalanced attributes are processed in parallel. The rules without imbalanced attributes are mined through standard algorithm while the rules with imbalanced attributes are mined based on new defined measurements. Through simple transformation, these measurements can be in a uniform space so that only a few parameters need to be specified by user. In the case study, the proposed algorithm is applied into social security field. Although some attributes are severely imbalanced, the rules with minority of the imbalanced attributes have been mined efficiently. © Springer-Verlag Berlin Heidelberg 2007