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Signed Feature Constraint Solving
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| Jean-Marc Andreoli, Uwe Borghoff, Remo Pareschi |
| Proc. 3rd Int. Conf. on Practical Application of Constraint Technology (PACT '97), April 23-25, 1997, London, U.K., pp. 35-46 |
| dist.pdf.gz |
| dist.ps.gz |
Intelligent brokering of information needs algorithmically tractable knowledge representations and corresponding constraint solvers to tackle the satisfiability problems involved. Feature constraints emerged soon as a convenient and elegant choice of knowledge representation. The full fragment of feature constraints, where sorts and features are allowed to be combined by all the logical connectives (conjunction, disjunction, negation and quantifiers), although very expressive, is hardly tractable. On the other hand, the subfragment called “basic feature constraints” (BFC), where negation and disjunction are simply forbidden, unneccesarily restricts applications such as knowledge brokers in an intolerable way. The main contribution of this paper is twofold. On the one hand, it presents a fragment of feature constraints, called “signed feature constraints” (SFC), which allows limited use of negation, precisely capable of expressing the kind of operations needed during intelligent brokering, and on the other hand, discusses an efficient constraint solving method for SFC. Since early 1996, the SFC solver performs efficiently within the Constraint Based Knowledge Broker system (CBKB). The CBKB system aims at heterogeneous information retrieval, schema integration, and knowledge fusion.
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
VISION
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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