| 1. |
- Besold, Tarek R., et al.
(author)
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A narrative in three acts : Using combinations of image schemas to model events
- 2017
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In: Biologically Inspired Cognitive Architectures. - : Elsevier. - 2212-683X .- 2212-6848. ; 19, s. 10-20
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Journal article (peer-reviewed)abstract
- Image schemas have been proposed as conceptual building blocks corresponding to the hypothesised most fundamental embodied experiences. We formally investigate how combinations of image schemas (or 'image schematic profiles') can model essential aspects of events, and discuss benefits for artificial intelligence and cognitive systems research, in particular concerning the role of such basic events in concept formation. More specifically, as exemplary illustrations and proof of concept the image schemas Object, Contact, and Path are combined to form the events Blockage, Bouncing, and Caused-Movement. Additionally, an outline of a proposed conceptual hierarchy of levels of modelling for image schemas and similar cognitive theories is given.
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| 2. |
- 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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| 3. |
- 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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