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MESO: Supporting online decision making in autonomic computing systems
- IEEE Transactions on Knowledge and Data Engineering (TKDE
, 2007
"... Abstract—Autonomic computing systems must be able to detect and respond to errant behavior or changing conditions with little or no human intervention. Clearly, decision making is a critical issue in such systems, which must learn how and when to invoke corrective actions based on past experience. T ..."
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Cited by 6 (4 self)
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Abstract—Autonomic computing systems must be able to detect and respond to errant behavior or changing conditions with little or no human intervention. Clearly, decision making is a critical issue in such systems, which must learn how and when to invoke corrective actions based on past experience. This paper describes the design, implementation, and evaluation of MESO, a pattern classifier designed to support online, incremental learning and decision making in autonomic systems. A novel feature of MESO is its use of small agglomerative clusters, called sensitivity spheres, that aggregate similar training samples. Sensitivity spheres are partitioned into sets during the construction of a memory-efficient hierarchical data structure. This structure facilitates data compression, which is important to many autonomic systems. Results are presented demonstrating that MESO achieves high accuracy while enabling rapid incremental training and classification. A case study is described in which MESO enables a mobile computing application to learn, by imitation, user preferences for balancing wireless network packet loss and bandwidth consumption. Once trained, the application can autonomously adjust error control parameters as needed while the user roams about a wireless cell. Index Terms—Autonomic computing, adaptive software, pattern classification, decision making, imitative learning, machine learning, mobile computing, perceptual memory, reinforcement learning. Ç
Meso: Perceptual memory to support online learning in adaptive software
- in Proceedings of the 3rd International Conference on Development and Learning (ICDL’04
, 2004
"... Adaptive and autonomic systems often must be able to detect and respond to errant behavior or changing conditions with little or no human intervention. Decision making is a critical issue in such systems, which must learn how and when to invoke corrective actions based on past experience. This paper ..."
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Adaptive and autonomic systems often must be able to detect and respond to errant behavior or changing conditions with little or no human intervention. Decision making is a critical issue in such systems, which must learn how and when to invoke corrective actions based on past experience. This paper describes the design, implementation and evaluation of a perceptual memory system, called MESO, that supports online decision-making in adaptive systems. MESO uses clustering and pattern classification methods while addressing the needs of online, incremental learning.
J.A.: Virtual Friend: Tracking and Generating Natural Interactive Behaviours in Real Video
- In: Proceedings of the 8th Internation Conference on Signal Processing (ICSP
, 2006
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Visual Perception and Reproduction for Imitative Learning of A Partner Robot
"... Abstract:- This paper proposes visual perception and model reproduction based on imitation of a partner robot interacting with a human. First of all, we discuss the role of imitation, and propose the method for imitative behavior generation. After the robot searches for a human by using a CCD camera ..."
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Abstract:- This paper proposes visual perception and model reproduction based on imitation of a partner robot interacting with a human. First of all, we discuss the role of imitation, and propose the method for imitative behavior generation. After the robot searches for a human by using a CCD camera, human hand positions are extracted from a series of images taken from the CCD camera. Next, the position sequence of the extracted human hand is used as inputs to a fuzzy spiking neural network to recognize the position sequence as a motion pattern. The trajectory for the robot behavior is generated and updated by a steady-state genetic algorithm based on the human motions pattern. Furthermore, a self-organizing map is used for clustering human hand motion patterns. Finally, we show experimental results of imitative behavior generation through interaction with a human.
Relational Learning by Imitation
"... Abstract. Imitative learning can be considered an essential task of humans development. People use instructions and demonstrations provided by other human experts to acquire knowledge. In order to make an agent capable of learning through demonstrations, we propose a relational framework for learnin ..."
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Abstract. Imitative learning can be considered an essential task of humans development. People use instructions and demonstrations provided by other human experts to acquire knowledge. In order to make an agent capable of learning through demonstrations, we propose a relational framework for learning by imitation. Demonstrations and domain specific knowledge are compactly represented by a logical language able to express complex relational processes. The agent interacts in a stochastic environment and incrementally receives demonstrations. It actively interacts with the human by deciding the next action to execute and requesting demonstration from the expert based on the current learned policy. The framework has been implemented and validated with experiments in simulated agent domains.
A LOGIC PROGRAMMING FRAMEWORK FOR LEARNING BY IMITATION
"... Humans use imitation as a mechanism for acquiring knowledge, i.e. they use instructions and/or demonstrations provided by other humans. In this paper we propose a logic programming framework for learning from imitation in order to make an agent able to learn from relational demonstrations. In partic ..."
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Humans use imitation as a mechanism for acquiring knowledge, i.e. they use instructions and/or demonstrations provided by other humans. In this paper we propose a logic programming framework for learning from imitation in order to make an agent able to learn from relational demonstrations. In particular, demonstrations are received in incremental way and used as training examples while the agent interacts in a stochastic environment. This logical framework allows to represent domain specific knowledge as well as to compactly and declaratively represent complex relational processes. The framework has been implemented and validated with experiments in simulated agent domains. 1
Distribution- Confidentiality: Public Code: REVERIE_D3_1_QMUL_V01_20120124.docx FP7-ICT-287723- REVERIE D3.1-Report On Available Cutting-Edge Tools
, 2012
"... Copyright by the REVERIE Consortium Disclaimer This document contains material, which is the copyright of certain REVERIE contractors, and may not be reproduced or copied without permission. All REVERIE consortium partners have agreed to the full publication of this document. The commercial use of ..."
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Copyright by the REVERIE Consortium Disclaimer This document contains material, which is the copyright of certain REVERIE contractors, and may not be reproduced or copied without permission. All REVERIE consortium partners have agreed to the full publication of this document. The commercial use of any information contained in this document may require a license from the proprietor of that information. The REVERIE Consortium consists of the following companies: No Participant name Short name Roll Country
Real-Time Generation of Interactive Virtual Human
"... Abstract. In this paper, we propose a new approach for generating interactive behaviours for virtual characters, namely the windowed Viterbi algorithm, capa-ble of doing so in real-time. Consequently, we compare the performance of the standard Viterbi algorithm and the windowed Viterbi algorithm wit ..."
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Abstract. In this paper, we propose a new approach for generating interactive behaviours for virtual characters, namely the windowed Viterbi algorithm, capa-ble of doing so in real-time. Consequently, we compare the performance of the standard Viterbi algorithm and the windowed Viterbi algorithm within our sys-tem. Our system tracks and analyses the behaviour of a real person in video input and produces a fully articulated three dimensional (3D) character interacting with the person in the video input. Our system is model-based. Prior to tracking, we train a collection of dual-input Hidden Markov Model (HMM) on 3D motion capture (MoCap) data representing a number of interactions between two people. Then using the dual-input HMM, we generate a moving virtual character reacting to (the motion of) a real person. In this article, we present the detailed evaluation of using the windowed Viterbi algorithms within our system, and show that our approach is suitable for generating interactive behaviours in real-time. Further-more, in order to enhance the tracking capabilities of the algorithm, we develop a novel technique that splits the complex motion data in an automated way. This results in improved tracking of the human motion from our model.
