Abstract
This paper describes a new approach for the creation of an adaptive system able to selectively combine dynamic multidimensional information sources to perform state estimation. The system proposed is based on an intelligent agent paradigm. Each information source is implemented as an agent that is able to adapt its behavior according to the relevant task and environment constraints. The adaptation is provided by a local self-evaluation function on each agent. Cooperation among the agents is given by a probabilistic scheme that integrates the evidential information provided by them. The proposed system aims to achieve two highly desirable attributes of an engineering system: robustness and efficiency. By combining the outputs of multiple vision modules the assumptions and constrains of each module can be factored out to result in a more robust system overall. Efficiency is still kept through the on-line selection and specialization of the agents. An initial implementation for the case of visual information demonstrates the advantages of the approach for two frequent problems faced by a mobile robot: dynamic target tracking and obstacle detection.
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Soto, A., Khosla, P. (2003). Adaptive Agent Based System for State Estimation Using Dynamic Multidimensional Information Sources. In: Laddaga, R., Shrobe, H., Robertson, P. (eds) Self-Adaptive Software: Applications. IWSAS 2001. Lecture Notes in Computer Science, vol 2614. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36554-0_6
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DOI: https://doi.org/10.1007/3-540-36554-0_6
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