Adaptation to changes in organizational procedures and business rules is a sine qua non for workflow management systems, if they have to be useful to organizations. This paper describes an approach based on workflow agents capable of managing dynamic changes in business policies. The two key aspects of our approach are (i) the agentification of the process engine, in the sense that this is viewed as controlled and executed by autonomous workflow agents capable of reacting and adapting to external changes, and (ii) the fact that these agents are reflective, in the sense that they can observe and thus modify their own behavior. The model is described in the context of an agent-based framework for coordination with reflective capabilities, and is evaluated against a well-known case of dynamic change.
Reflective Agents for Adaptive Workflows
| Uwe Borghoff, Paolo Bottoni, Pierre Mussio, Remo Pareschi |
| Proc. 2nd Int. Conf. on the Practical Application of Intelligent Agents and Multi-Agent Technology (PAAM '97), April 21-23, 1997, London, U.K., pp. 405-420 |
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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