| 1. |
- Besold, Tarek R., et al.
(author)
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Anchoring Knowledge in Interaction : Towards a Harmonic Subsymbolic/Symbolic Framework and Architecture of Computational Cognition
- 2015
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In: Artificial General Intelligence (AGI 2015). - Cham : Springer. - 9783319213651 - 9783319213644 ; , s. 35-45
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Conference paper (peer-reviewed)abstract
- We outline a proposal for a research program leading to a new paradigm, architectural framework, and prototypical implementation, for the cognitively inspired anchoring of an agent's learning, knowledge formation, and higher reasoning abilities in real-world interactions: Learning through interaction in real-time in a real environment triggers the incremental accumulation and repair of knowledge that leads to the formation of theories at a higher level of abstraction. The transformations at this higher level filter down and inform the learning process as part of a permanent cycle of learning through experience, higher-order deliberation, theory formation and revision.The envisioned framework will provide a precise computational theory, algorithmic descriptions, and an implementation in cyber-physical systems, addressing the lifting of action patterns from the subsymbolic to the symbolic knowledge level, effective methods for theory formation, adaptation, and evolution, the anchoring of knowledge-level objects, realworld interactions and manipulations, and the realization and evaluation of such a system in different scenarios. The expected results can provide new foundations for future agent architectures, multi-agent systems, robotics, and cognitive systems, and can facilitate a deeper understanding of the development and interaction in human-technological settings.
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| 2. |
- d'Avila Garcez, Artur, et al.
(author)
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Neural-Symbolic Learning and Reasoning : Contributions and Challenges
- 2015
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In: Knowledge Representation and Reasoning. - : AAAI Press. - 9781577357070 ; , s. 18-21
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Conference paper (peer-reviewed)abstract
- Neural-symbolic computation aims at integrating robust connectionist learning algorithms with sound symbolic rea-soning. The recent impact of neural learning, in particular of deep networks, has led to the creation of new representa-tions that have, so far, not really been used for reasoning. Results on neural-symbolic computation have shown to of-fer powerful alternatives for knowledge representation, learning and inference in neural computation. This paper presents key challenges and contributions of neural-symbolic computation to this area.
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