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Conference

Autonomous and Intelligent Systems 

About: Autonomous and Intelligent Systems is an academic conference. The conference publishes majorly in the area(s): Mobile robot & Autonomous agent. Over the lifetime, 174 publication(s) have been published by the conference receiving 1314 citation(s).
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Book ChapterDOI
03 Jun 2007
TL;DR: This paper draws a high-level overview of the agent-mining interaction from the perspective of an emerging area in the scientific family, and summarizes key driving forces, originality, major research directions and respective topics, and the progression of research groups, publications and activities of agent- mining interaction.
Abstract: In the past twenty years, agents (we mean autonomous agent and multi-agent systems) and data mining (also knowledge discovery) have emerged separately as two of most prominent, dynamic and exciting research areas. In recent years, an increasingly remarkable trend in both areas is the agent-mining interaction and integration. This is driven by not only researcher's interests, but intrinsic challenges and requirements from both sides, as well as benefits and complementarity to both communities through agent-mining interaction. In this paper, we draw a high-level overview of the agent-mining interaction from the perspective of an emerging area in the scientific family. To promote it as a newly emergent scientific field, we summarize key driving forces, originality, major research directions and respective topics, and the progression of research groups, publications and activities of agent-mining interaction. Both theoretical and application-oriented aspects are addressed. The above investigation shows that the agent-mining interaction is attracting everincreasing attention from both agent and data mining communities. Some complicated challenges in either community may be effectively and efficiently tackled through agent-mining interaction. However, as a new open area, there are many issues waiting for research and development from theoretical, technological and practical perspectives.

53 citations


Proceedings ArticleDOI
21 Jun 2010
TL;DR: A novel hierarchical clustering approach for wireless sensor networks to maintain energy depletion of the network in minimum using Artificial Bee Colony Algorithm which is a new swarm based heuristic algorithm.
Abstract: In this paper, we propose a novel hierarchical clustering approach for wireless sensor networks to maintain energy depletion of the network in minimum using Artificial Bee Colony Algorithm which is a new swarm based heuristic algorithm. We present a protocol using Artificial Bee Colony Algorithm, which tries to provide optimum cluster organization in order to minimize energy consumption. In cluster based networks, the selection of cluster heads and its members is an essential process which affects energy consumption. Simulation results demonstrate that the proposed approach provides promising solutions for the wireless sensor networks.

43 citations


Proceedings ArticleDOI
21 Jun 2010
TL;DR: A classification of team tracking systems applied to sports is proposed by distinguishing them into two main categories: intrusive and nonintrusive, which are further refined into outdoor and indoor sports applications.
Abstract: Recent years have brought an increasing interest on analyzing efficiently the performance of sports players during training sessions and games. The information collected from such analysis is very valuable to educators and coaches since it allows them to better understand the difficulties of a trainee, a player or even an entire team and formulate adequate training and strategic plans accordingly. In order to perform this analysis in a consistent and systematic way, sophisticated sensory systems and data processing techniques are needed. This paper presents a survey on relevant work, current techniques and trends on the area of team tracking systems applied to sports. We propose a classification of these systems by distinguishing them into two main categories: intrusive and nonintrusive. Nonintrusive systems are further refined into outdoor and indoor sports applications. The specific characteristics of each system are itemized, including the identification of the strong points and limitations. Finally, the paper highlights some open issues and research opportunities on this area.

41 citations


Book ChapterDOI
06 Jun 2005
TL;DR: This paper tells a story of synergism of two cutting edge technologies — agents and data mining, and a new way to integrate these two techniques –ontology-based integration is discussed.
Abstract: This paper tells a story of synergism of two cutting edge technologies — agents and data mining. By integrating these two technologies, the power for each of them is enhanced. Integrating agents into data mining systems, or constructing data mining systems from agent perspectives, the flexibility of data mining systems can be greatly improved. New data mining techniques can add to the systems dynamically in the form of agents, while the out-of-date ones can also be deleted from systems at run-time. Equipping agents with data mining capabilities, the agents are much smarter and more adaptable. In this way, the performance of these agent systems can be improved. A new way to integrate these two techniques –ontology-based integration is also discussed. Case studies will be given to demonstrate such mutual enhancement.

37 citations


Book ChapterDOI
22 Jun 2011
TL;DR: Simulation results show that the kernelized ELM outperforms LS-SVM in terms of both recognition prediction accuracy and training speed.
Abstract: The original extreme learning machine (ELM), based on least square solutions, is an efficient learning algorithm used in "generalized" single-hidden layer feedforward networks (SLFNs) which need not be neuron alike. Latest development[1] shows that ELM can be implemented with kernels. Kernlized ELM can be seen as a variant of the conventional LS-SVM without the output bias b. In this paper, the performance comparison of LS-SVM and kernelized ELM is conducted over a benchmarking face recognition dataset. Simulation results show that the kernelized ELM outperforms LS-SVM in terms of both recognition prediction accuracy and training speed.

36 citations


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Performance
Metrics
No. of papers from the Conference in previous years
YearPapers
202111
201232
201140
201039
200727
200523