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Documents authored by Lamb, Luis C.

Found 2 Possible Name Variants:

Lamb, Luís C.

Document
DOI: 10.4230/DagRep.7.5.56
Human-Like Neural-Symbolic Computing (Dagstuhl Seminar 17192)

Authors: Tarek R. Besold, Artur d'Avila Garcez, and Luis C. Lamb

Published in: Dagstuhl Reports, Volume 7, Issue 5 (2018)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 17192 "Human-Like Neural-Symbolic Computing", held from May 7th to 12th, 2017. The underlying idea of Human-Like Computing is to incorporate into Computer Science aspects of how humans learn, reason and compute. Whilst recognising the relevant scientific trends in big data and deep learning, capable of achieving state-of-the-art performance in speech recognition and computer vision tasks, limited progress has been made towards understanding the principles underlying language and vision understanding. Under the assumption that neural-symbolic computing - the study of logic and connectionism as well statistical approaches - can offer new insight into this problem, the seminar brought together computer scientists, but also specialists on artificial intelligence, cognitive science, machine learning, knowledge representation and reasoning, computer vision, neural computation, and natural language processing. The seminar consisted of contributed and invited talks, breakout and joint group discussion sessions, and a hackathon. It was built upon previous seminars and workshops on the integration of computational learning and symbolic reasoning, such as the Neural-Symbolic Learning and Reasoning (NeSy) workshop series, and the previous Dagstuhl Seminar 14381: Neural-Symbolic Learning and Reasoning.
Cite as

Tarek R. Besold, Artur d'Avila Garcez, and Luis C. Lamb. Human-Like Neural-Symbolic Computing (Dagstuhl Seminar 17192). In Dagstuhl Reports, Volume 7, Issue 5, pp. 56-83, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)

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@Article{besold_et_al:DagRep.7.5.56,
  author =	{Besold, Tarek R. and d'Avila Garcez, Artur and Lamb, Luis C.},
  title =	{{Human-Like Neural-Symbolic Computing (Dagstuhl Seminar 17192)}},
  pages =	{56--83},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2017},
  volume =	{7},
  number =	{5},
  editor =	{Besold, Tarek R. and d'Avila Garcez, Artur and Lamb, Luis C.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.7.5.56},
  URN =		{urn:nbn:de:0030-drops-82803},
  doi =		{10.4230/DagRep.7.5.56},
  annote =	{Keywords: Deep Learning, Human-like computing, Multimodal learning, Natural language processing, Neural-symbolic integration}
}
Document
DOI: 10.4230/DagRep.4.9.50
Neural-Symbolic Learning and Reasoning (Dagstuhl Seminar 14381)

Authors: Artur d'Avila Garcez, Marco Gori, Pascal Hitzler, and Luís C. Lamb

Published in: Dagstuhl Reports, Volume 4, Issue 9 (2015)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 14381 "Neural-Symbolic Learning and Reasoning", which was held from September 14th to 19th, 2014. This seminar brought together specialist in machine learning, knowledge representation and reasoning, computer vision and image understanding, natural language processing, and cognitive science. The aim of the seminar was to explore the interface among several fields that contribute to the effective integration of cognitive abilities such as learning, reasoning, vision and language understanding in intelligent and cognitive computational systems. The seminar consisted of contributed and invited talks, breakout and joint group discussion sessions.
Cite as

Artur d'Avila Garcez, Marco Gori, Pascal Hitzler, and Luís C. Lamb. Neural-Symbolic Learning and Reasoning (Dagstuhl Seminar 14381). In Dagstuhl Reports, Volume 4, Issue 9, pp. 50-84, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2015)

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@Article{davilagarcez_et_al:DagRep.4.9.50,
  author =	{d'Avila Garcez, Artur and Gori, Marco and Hitzler, Pascal and Lamb, Lu{\'\i}s C.},
  title =	{{Neural-Symbolic Learning and Reasoning (Dagstuhl Seminar 14381)}},
  pages =	{50--84},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2015},
  volume =	{4},
  number =	{9},
  editor =	{d'Avila Garcez, Artur and Gori, Marco and Hitzler, Pascal and Lamb, Lu{\'\i}s C.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.4.9.50},
  URN =		{urn:nbn:de:0030-drops-48843},
  doi =		{10.4230/DagRep.4.9.50},
  annote =	{Keywords: Neural-symbolic computation, deep learning, image understanding, lifelong machine learning, natural language understanding, ontology learning}
}
Document
DOI: 10.4230/DagSemProc.08041.5
The Grand Challenges and Myths of Neural-Symbolic Computation

Authors: Luis C. Lamb

Published in: Dagstuhl Seminar Proceedings, Volume 8041, Recurrent Neural Networks- Models, Capacities, and Applications (2008)

Abstract
The construction of computational cognitive models integrating the connectionist and symbolic paradigms of artificial intelligence is a standing research issue in the field. The combination of logic-based inference and connectionist learning systems may lead to the construction of semantically sound computational cognitive models in artificial intelligence, computer and cognitive sciences. Over the last decades, results regarding the computation and learning of classical reasoning within neural networks have been promising. Nonetheless, there still remains much do be done. Artificial intelligence, cognitive and computer science are strongly based on several non-classical reasoning formalisms, methodologies and logics. In knowledge representation, distributed systems, hardware design, theorem proving, systems specification and verification classical and non-classical logics have had a great impact on theory and real-world applications. Several challenges for neural-symbolic computation are pointed out, in particular for classical and non-classical computation in connectionist systems. We also analyse myths about neural-symbolic computation and shed new light on them considering recent research advances.
Cite as

Luis C. Lamb. The Grand Challenges and Myths of Neural-Symbolic Computation. In Recurrent Neural Networks- Models, Capacities, and Applications. Dagstuhl Seminar Proceedings, Volume 8041, pp. 1-16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

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@InProceedings{lamb:DagSemProc.08041.5,
  author =	{Lamb, Luis C.},
  title =	{{The Grand Challenges  and Myths of Neural-Symbolic Computation}},
  booktitle =	{Recurrent Neural Networks- Models, Capacities, and Applications},
  pages =	{1--16},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{8041},
  editor =	{Luc De Raedt and Barbara Hammer and Pascal Hitzler and Wolfgang Maass},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08041.5},
  URN =		{urn:nbn:de:0030-drops-14233},
  doi =		{10.4230/DagSemProc.08041.5},
  annote =	{Keywords: Connectionist non-classical logics, neural-symbolic computation, non-classical reasoning, computational cognitive models}
}

Lamb, Luis C.

Document
DOI: 10.4230/DagRep.7.5.56
Human-Like Neural-Symbolic Computing (Dagstuhl Seminar 17192)

Authors: Tarek R. Besold, Artur d'Avila Garcez, and Luis C. Lamb

Published in: Dagstuhl Reports, Volume 7, Issue 5 (2018)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 17192 "Human-Like Neural-Symbolic Computing", held from May 7th to 12th, 2017. The underlying idea of Human-Like Computing is to incorporate into Computer Science aspects of how humans learn, reason and compute. Whilst recognising the relevant scientific trends in big data and deep learning, capable of achieving state-of-the-art performance in speech recognition and computer vision tasks, limited progress has been made towards understanding the principles underlying language and vision understanding. Under the assumption that neural-symbolic computing - the study of logic and connectionism as well statistical approaches - can offer new insight into this problem, the seminar brought together computer scientists, but also specialists on artificial intelligence, cognitive science, machine learning, knowledge representation and reasoning, computer vision, neural computation, and natural language processing. The seminar consisted of contributed and invited talks, breakout and joint group discussion sessions, and a hackathon. It was built upon previous seminars and workshops on the integration of computational learning and symbolic reasoning, such as the Neural-Symbolic Learning and Reasoning (NeSy) workshop series, and the previous Dagstuhl Seminar 14381: Neural-Symbolic Learning and Reasoning.
Cite as

Tarek R. Besold, Artur d'Avila Garcez, and Luis C. Lamb. Human-Like Neural-Symbolic Computing (Dagstuhl Seminar 17192). In Dagstuhl Reports, Volume 7, Issue 5, pp. 56-83, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)

Copy BibTex To Clipboard

@Article{besold_et_al:DagRep.7.5.56,
  author =	{Besold, Tarek R. and d'Avila Garcez, Artur and Lamb, Luis C.},
  title =	{{Human-Like Neural-Symbolic Computing (Dagstuhl Seminar 17192)}},
  pages =	{56--83},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2017},
  volume =	{7},
  number =	{5},
  editor =	{Besold, Tarek R. and d'Avila Garcez, Artur and Lamb, Luis C.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.7.5.56},
  URN =		{urn:nbn:de:0030-drops-82803},
  doi =		{10.4230/DagRep.7.5.56},
  annote =	{Keywords: Deep Learning, Human-like computing, Multimodal learning, Natural language processing, Neural-symbolic integration}
}
Document
DOI: 10.4230/DagRep.4.9.50
Neural-Symbolic Learning and Reasoning (Dagstuhl Seminar 14381)

Authors: Artur d'Avila Garcez, Marco Gori, Pascal Hitzler, and Luís C. Lamb

Published in: Dagstuhl Reports, Volume 4, Issue 9 (2015)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 14381 "Neural-Symbolic Learning and Reasoning", which was held from September 14th to 19th, 2014. This seminar brought together specialist in machine learning, knowledge representation and reasoning, computer vision and image understanding, natural language processing, and cognitive science. The aim of the seminar was to explore the interface among several fields that contribute to the effective integration of cognitive abilities such as learning, reasoning, vision and language understanding in intelligent and cognitive computational systems. The seminar consisted of contributed and invited talks, breakout and joint group discussion sessions.
Cite as

Artur d'Avila Garcez, Marco Gori, Pascal Hitzler, and Luís C. Lamb. Neural-Symbolic Learning and Reasoning (Dagstuhl Seminar 14381). In Dagstuhl Reports, Volume 4, Issue 9, pp. 50-84, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2015)

Copy BibTex To Clipboard

@Article{davilagarcez_et_al:DagRep.4.9.50,
  author =	{d'Avila Garcez, Artur and Gori, Marco and Hitzler, Pascal and Lamb, Lu{\'\i}s C.},
  title =	{{Neural-Symbolic Learning and Reasoning (Dagstuhl Seminar 14381)}},
  pages =	{50--84},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2015},
  volume =	{4},
  number =	{9},
  editor =	{d'Avila Garcez, Artur and Gori, Marco and Hitzler, Pascal and Lamb, Lu{\'\i}s C.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.4.9.50},
  URN =		{urn:nbn:de:0030-drops-48843},
  doi =		{10.4230/DagRep.4.9.50},
  annote =	{Keywords: Neural-symbolic computation, deep learning, image understanding, lifelong machine learning, natural language understanding, ontology learning}
}
Document
DOI: 10.4230/DagSemProc.08041.5
The Grand Challenges and Myths of Neural-Symbolic Computation

Authors: Luis C. Lamb

Published in: Dagstuhl Seminar Proceedings, Volume 8041, Recurrent Neural Networks- Models, Capacities, and Applications (2008)

Abstract
The construction of computational cognitive models integrating the connectionist and symbolic paradigms of artificial intelligence is a standing research issue in the field. The combination of logic-based inference and connectionist learning systems may lead to the construction of semantically sound computational cognitive models in artificial intelligence, computer and cognitive sciences. Over the last decades, results regarding the computation and learning of classical reasoning within neural networks have been promising. Nonetheless, there still remains much do be done. Artificial intelligence, cognitive and computer science are strongly based on several non-classical reasoning formalisms, methodologies and logics. In knowledge representation, distributed systems, hardware design, theorem proving, systems specification and verification classical and non-classical logics have had a great impact on theory and real-world applications. Several challenges for neural-symbolic computation are pointed out, in particular for classical and non-classical computation in connectionist systems. We also analyse myths about neural-symbolic computation and shed new light on them considering recent research advances.
Cite as

Luis C. Lamb. The Grand Challenges and Myths of Neural-Symbolic Computation. In Recurrent Neural Networks- Models, Capacities, and Applications. Dagstuhl Seminar Proceedings, Volume 8041, pp. 1-16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

Copy BibTex To Clipboard

@InProceedings{lamb:DagSemProc.08041.5,
  author =	{Lamb, Luis C.},
  title =	{{The Grand Challenges  and Myths of Neural-Symbolic Computation}},
  booktitle =	{Recurrent Neural Networks- Models, Capacities, and Applications},
  pages =	{1--16},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{8041},
  editor =	{Luc De Raedt and Barbara Hammer and Pascal Hitzler and Wolfgang Maass},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08041.5},
  URN =		{urn:nbn:de:0030-drops-14233},
  doi =		{10.4230/DagSemProc.08041.5},
  annote =	{Keywords: Connectionist non-classical logics, neural-symbolic computation, non-classical reasoning, computational cognitive models}
}
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