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pro vyhledávání: '"Forbus, Kenneth D."'
Autor:
Nakos, Constantine, Forbus, Kenneth D.
Hand-curated natural language systems provide an inspectable, correctable alternative to language systems based on machine learning, but maintaining them requires considerable effort and expertise. Interactive Natural Language Debugging (INLD) aims t
Externí odkaz:
http://arxiv.org/abs/2407.06400
Autor:
Nakos, Constantine, Forbus, Kenneth D.
One of the fundamental aspects of cognitive architectures is their ability to encode and manipulate knowledge. Without a consistent, well-designed, and scalable knowledge management scheme, an architecture will be unable to move past toy problems and
Externí odkaz:
http://arxiv.org/abs/2407.06401
Autor:
Hancock, Will, Forbus, Kenneth D.
Event perception refers to people's ability to carve up continuous experience into meaningful discrete events. We speak of finishing our morning coffee, mowing the lawn, leaving work, etc. as singular occurrences that are localized in time and space.
Externí odkaz:
http://arxiv.org/abs/2407.06088
When deciding how to act, we must consider other agents' norms and values. However, our norms are ever-evolving. We often add exceptions or change our minds, and thus norms can conflict over time. Therefore, to maintain an accurate mental model of ot
Externí odkaz:
http://arxiv.org/abs/2407.04869
One of the purposes of perception is to bridge between sensors and conceptual understanding. Marr's Primal Sketch combined initial edge-finding with multiple downstream processes to capture aspects of visual perception such as grouping and stereopsis
Externí odkaz:
http://arxiv.org/abs/2407.04859
Autor:
Chen, Kezhen, Huang, Qiuyuan, Palangi, Hamid, Smolensky, Paul, Forbus, Kenneth D., Gao, Jianfeng
Generating formal-language programs represented by relational tuples, such as Lisp programs or mathematical operations, to solve problems stated in natural language is a challenging task because it requires explicitly capturing discrete symbolic stru
Externí odkaz:
http://arxiv.org/abs/1910.02339
Extending the success of deep neural networks to natural language understanding and symbolic reasoning requires complex operations and external memory. Recent neural program induction approaches have attempted to address this problem, but are typical
Externí odkaz:
http://arxiv.org/abs/1612.01197
Harnessing the statistical power of neural networks to perform language understanding and symbolic reasoning is difficult, when it requires executing efficient discrete operations against a large knowledge-base. In this work, we introduce a Neural Sy
Externí odkaz:
http://arxiv.org/abs/1611.00020
Autor:
Gentner, Dedre, Brem, Sarah, Ferguson, Ronald W., Markman, Arthur B., Levidow, Björn B., Wolff, Phillip, Forbus, Kenneth D.
Publikováno v:
The Journal of the Learning Sciences, 1997 Jan 01. 6(1), 3-40.
Externí odkaz:
https://www.jstor.org/stable/1466874
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