Report from Dagstuhl Seminar 14381 Neural-Symbolic Learning and Reasoning

This report documents the program and the outcomes of Dagstuhl Seminar 14381 “NeuralSymbolic 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. Seminar September 14–19, 2014 – http://www.dagstuhl.de/14381 1998 ACM Subject Classification I.2 Artificial Intelligence, I.2.4 Knowledge Representation Formalisms and Methods, I.2.6 Learning, I.2.10 Vision and Scene Understanding, I.2.11 Distributed Artificial Intelligence

[1]  Raymond Reiter,et al.  A Logical Framework for Depiction and Image Interpretation , 1989, Artif. Intell..

[2]  Ramanathan V. Guha,et al.  Building Large Knowledge-Based Systems: Representation and Inference in the Cyc Project , 1990 .

[3]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[4]  Moshe Y. Vardi.  Why is Modal Logic So Robustly Decidable? , 1996, Descriptive Complexity and Finite Models.

[5]  Dan Roth,et al.  Learning to reason , 1994, JACM.

[6]  Avrim Blum,et al.  The Bottleneck , 2021, Monopsony Capitalism.

[7]  Leslie G. Valiant,et al.  A neuroidal architecture for cognitive computation , 1998, ICALP.

[8]  Umberto Straccia,et al.  Reasoning within Fuzzy Description Logics , 2011, J. Artif. Intell. Res..

[9]  Bart Goethals,et al.  Relational Association Rules: Getting WARMeR , 2002, Pattern Detection and Discovery.

[10]  H. Lan,et al.  SWRL : A semantic Web rule language combining OWL and ruleML , 2004 .

[11]  P. Hitzler,et al.  The Integration of Connectionism and First-Order Knowledge Representation and Reasoning as a Challenge for Artificial Intelligence , 2004, ArXiv.

[12]  Hannu Toivonen,et al.  Discovery of frequent DATALOG patterns , 1999, Data Mining and Knowledge Discovery.

[13]  Dan Roth,et al.  Learning to Reason with a Restricted View , 1995, COLT '95.

[14]  Boris Motik,et al.  Query Answering for OWL-DL with Rules , 2004, SEMWEB.

[15]  Pascal Hitzler,et al.  Ontology learning as a use-case for neural-symbolic integration , 2005, IJCAI 2005.

[16]  Dov M. Gabbay,et al.  Value-based Argumentation Frameworks as Neural-symbolic Learning Systems , 2005, J. Log. Comput..

[17]  Bozena Staruch,et al.  First Order Theories for Partial Models , 2005, Stud Logica.

[18]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[19]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[20]  Bernhard Schölkopf,et al.  Introduction to Semi-Supervised Learning , 2006, Semi-Supervised Learning.

[21]  Jerome A. Feldman,et al.  From Molecule to Metaphor - A Neural Theory of Language , 2006 .

[22]  Artur S. d'Avila Garcez,et al.  A Connectionist Cognitive Model for Temporal Synchronisation and Learning , 2007, AAAI.

[23]  Dov M. Gabbay,et al.  Connectionist modal logic: Representing modalities in neural networks , 2007, Theor. Comput. Sci..

[24]  Rajat Raina,et al.  Self-taught learning: transfer learning from unlabeled data , 2007, ICML '07.

[25]  A. Torralba,et al.  The role of context in object recognition , 2007, Trends in Cognitive Sciences.

[26]  Pascal Hitzler,et al.  Connectionist model generation: A first-order approach , 2008, Neurocomputing.

[27]  Leslie G. Valiant.  Knowledge Infusion: In Pursuit of Robustness in Artificial Intelligence , 2008, FSTTCS.

[28]  Daniel L. Silver,et al.  Inductive transfer with context-sensitive neural networks , 2008, Machine Learning.

[29]  Bernd Neumann,et al.  On scene interpretation with description logics , 2006, Image Vis. Comput..

[30]  Luís C. Lamb,et al.  The Grand Challenges and Myths of Neural-Symbolic Computation , 2008, Recurrent Neural Networks.

[31]  Sebastian Rudolph,et al.  Foundations of Semantic Web Technologies , 2009 .

[32]  Robert E. Mercer,et al.  Life-long Learning Through Task Rehearsal and Selective Knowledge Transfer , 2009 .

[33]  Rajat Raina,et al.  Large-scale deep unsupervised learning using graphics processors , 2009, ICML '09.

[34]  Frank van Harmelen,et al.  A reasonable Semantic Web , 2010, Semantic Web.

[35]  Francesco M. Donini,et al.  A Unified Framework for Non-standard Reasoning Services in Description Logics , 2010, ECAI.

[36]  Amit P. Sheth,et al.  Ontology Alignment for Linked Open Data , 2010, SEMWEB.

[37]  Agnieszka Lawrynowicz,et al.  The role of semantics in mining frequent patterns from knowledge bases in description logics with rules , 2010, Theory and Practice of Logic Programming.

[38]  Luc De Raedt,et al.  Constraint Programming for Data Mining and Machine Learning , 2010, AAAI.

[39]  Amit P. Sheth,et al.  Linked Data Is Merely More Data , 2010, AAAI Spring Symposium: Linked Data Meets Artificial Intelligence.

[40]  Uta Priss,et al.  An application of formal concept analysis to semantic neural decoding , 2009, Annals of Mathematics and Artificial Intelligence.

[41]  Estevam R. Hruschka,et al.  Toward an Architecture for Never-Ending Language Learning , 2010, AAAI.

[42]  Francesca A. Lisi.  AL-QuIn: An Onto-Relational Learning System for Semantic Web Mining , 2011, Int. J. Semantic Web Inf. Syst..

[43]  Geoffrey E. Hinton,et al.  Transforming Auto-Encoders , 2011, ICANN.

[44]  Artur S. d'Avila Garcez,et al.  Learning and Representing Temporal Knowledge in Recurrent Networks , 2011, IEEE Transactions on Neural Networks.

[45]  Céline Hudelot,et al.  Towards ontologies for image interpretation and annotation , 2011, 2011 9th International Workshop on Content-Based Multimedia Indexing (CBMI).

[46]  Daniel L. Silver,et al.  Consolidation Using Context-Sensitive Multiple Task Learning , 2011, Canadian Conference on AI.

[47]  Bashar Nuseibeh,et al.  Learning to adapt requirements specifications of evolving systems: (NIER track) , 2011, 2011 33rd International Conference on Software Engineering (ICSE).

[48]  Johanna Völker,et al.  Statistical Schema Induction , 2011, ESWC.

[49]  Artur S. d'Avila Garcez,et al.  A Neural-Symbolic Cognitive Agent for Online Learning and Reasoning , 2011, IJCAI.

[50]  Luciano Serafini,et al.  Data-Driven Logical Reasoning , 2012, URSW.

[51]  Francesca A. Lisi.  A Declarative Modeling Language for Concept Learning in Description Logics , 2012, ILP.

[52]  Yoshua Bengio,et al.  Deep Learning of Representations for Unsupervised and Transfer Learning , 2011, ICML Unsupervised and Transfer Learning.

[53]  Amit P. Sheth,et al.  Alignment-Based Querying of Linked Open Data , 2012, OTM Conferences.

[54]  Luciano Serafini,et al.  Semantic Knowledge Discovery from Heterogeneous Data Sources , 2012, EKAW.

[55]  Krzysztof Janowicz,et al.  The Digital Earth as knowledge engine , 2012, Semantic Web.

[56]  Guido Boella,et al.  Learning and reasoning about norms using neural-symbolic systems , 2012, AAMAS.

[57]  Andrew Y. Ng,et al.  Emergence of Object-Selective Features in Unsupervised Feature Learning , 2012, NIPS.

[58]  Brendan Juba,et al.  Learning implicitly in reasoning in PAC-Semantics , 2012, ArXiv.

[59]  Pascal Vincent,et al.  Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[60]  Diedrich Wolter,et al.  A Probabilistic Framework for Object Descriptions in Indoor Route Instructions , 2013, COSIT.

[61]  Qiang Yang,et al.  Lifelong Machine Learning Systems: Beyond Learning Algorithms , 2013, AAAI Spring Symposium: Lifelong Machine Learning.

[62]  Yoshua Bengio,et al.  Deep Learning of Representations: Looking Forward , 2013, SLSP.

[63]  Marc'Aurelio Ranzato,et al.  Building high-level features using large scale unsupervised learning , 2011, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[64]  Daniel L. Silver.  The Consolidation of Task Knowledge for Lifelong Machine Learning , 2013, AAAI Spring Symposium: Lifelong Machine Learning.

[65]  Krzysztof Janowicz,et al.  Linked Data, Big Data, and the 4th Paradigm , 2013, Semantic Web.

[66]  Fabian M. Suchanek,et al.  AMIE: association rule mining under incomplete evidence in ontological knowledge bases , 2013, WWW.

[67]  S. Tran,et al.  Knowledge Extraction from Deep Belief Networks for Images , 2013 .

[68]  Gadi Pinkas,et al.  Representing, binding, retrieving and unifying relational knowledge using pools of neural binders , 2013, BICA 2013.

[69]  Artur S. d'Avila Garcez,et al.  Dreaming Machines: On multimodal fusion and information retrieval using neural-symbolic cognitive agents , 2013, ICCSW.

[70]  Luciano Serafini,et al.  Mixing Low-Level and Semantic Features for Image Interpretation - A Framework and a Simple Case Study , 2014, ECCV Workshops.

[71]  Artur S. d'Avila Garcez,et al.  Applying Neural-Symbolic Cognitive Agents in Intelligent Transport Systems to reduce CO2 emissions , 2014, 2014 International Joint Conference on Neural Networks (IJCNN).

[72]  Guido Boella,et al.  Neural Networks for Runtime Verification , 2014, 2014 International Joint Conference on Neural Networks (IJCNN).

[73]  Luciano Serafini,et al.  Semantic Knowledge Discovery and Data-Driven Logical Reasoning from Heterogeneous Data Sources , 2014, URSW.

[74]  Dov M. Gabbay,et al.  A neural cognitive model of argumentation with application to legal inference and decision making , 2014, J. Appl. Log..

[75]  Luc De Raedt,et al.  Neural-Symbolic Learning and Reasoning: Contributions and Challenges , 2015, AAAI Spring Symposia.