Imagine distributed knowledge processing with autonomous activities and decentralized control where the handling of partial knowledge will not result in unclear semantics or failure-prone behavior. In this paper, a modular approach is taken where autonomous activities, called constraint-based knowledge brokers, process and generate new knowledge in presence of partial information. We present a mathematical model where the semantics of the knowledge system is described using the traditional fixed-point procedure. The computational model is given by LO, our model for multi-agent coordination. The problems of inter-argument dependencies and knowledge reuse inside the knowledge generators are addressed as well as the difficulty of recursion control.
Constraint-Based Knowledge Brokers
| Jean-Marc Andreoli, Uwe Borghoff, Remo Pareschi |
| Hong, H. (ed.): Proc. of the 1st Intl. Symp. on Parallel Symbolic Computation (PASCO'94), September 1994, Hagenberg/Linz (Austria). Singapore, New Jersey, London, Hong Kong: World Scientific, Lecture Notes Series in Computing 5, pp 1-11. |
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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