Perspectives of Neural-Symbolic Integration PDF
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BarbaraHammerandPascalHitzler(Eds.) PerspectivesofNeural-SymbolicIntegration StudiesinComputationalIntelligence,Volume77 Editor-in-chief Prof.JanuszKacprzyk SystemsResearchInstitute PolishAcademyofSciences ul.Newelska6 01-447Warsaw Poland E-mail:[email protected] Furthervolumesofthisseries Vol.67.VassilisG.KaburlasosandGerhardX.Ritter canbefoundonourhomepage: (Eds.) springer.com ComputationalIntelligenceBasedonLattice Theory,2007 ISBN978-3-540-72686-9 Vol.57.NadiaNedjah,AjithAbrahamandLuizade MacedoMourella(Eds.) Vol.68.CiprianoGalindo,Juan-Antonio ComputationalIntelligenceinInformationAssurance Ferna´ndez-MadrigalandJavierGonzalez andSecurity,2007 AMulti-HierarchicalSymbolicModel ISBN978-3-540-71077-6 oftheEnvironmentforImprovingMobileRobot Vol.58.Jeng-ShyangPan,Hsiang-ChehHuang,Lakhmi Operation,2007 C.JainandWai-ChiFang(Eds.) ISBN978-3-540-72688-3 IntelligentMultimediaDataHiding,2007 Vol.69.FalkoDresslerandIacopoCarreras(Eds.) 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Design,2007 PerspectivesofNeural-SymbolicIntegration,2007 ISBN978-3-540-72376-9 ISBN978-3-540-73953-1 Barbara Hammer Pascal Hitzler (Eds.) Perspectives of Neural-Symbolic Integration With81Figuresand26Tables ABC Prof.Dr.BarbaraHammer PDDr.PascalHitzler InstituteofComputerScience InstituteAIFB ClausthalUniversityofTechnology UniversityofKarlsruhe JuliusAlbertStraβe4 76128Karlsruhe 38678Clausthal-Zellerfeld Germany Germany E-mail:[email protected] E-mail:[email protected] LibraryofCongressControlNumber:2007932795 ISSNprintedition:1860-949X ISSNelectronicedition:1860-9503 ISBN978-3-540-73953-1SpringerBerlinHeidelbergNewYork Thisworkissubjecttocopyright.Allrightsarereserved,whetherthewholeorpartofthematerial isconcerned,specificallytherightsoftranslation,reprinting,reuseofillustrations,recitation,broad- casting,reproductiononmicrofilmorinanyotherway,andstorageindatabanks.Duplicationof thispublicationorpartsthereofispermittedonlyundertheprovisionsoftheGermanCopyrightLaw ofSeptember9,1965,initscurrentversion,andpermissionforusemustalwaysbeobtainedfrom Springer-Verlag.ViolationsareliabletoprosecutionundertheGermanCopyrightLaw. SpringerisapartofSpringerScience+BusinessMedia springer.com (cid:176)c Springer-VerlagBerlinHeidelberg2007 Theuseofgeneraldescriptivenames,registerednames,trademarks,etc.inthispublicationdoesnot imply, even in the absence of a specific statement, that such names are exempt from the relevant protectivelawsandregulationsandthereforefreeforgeneraluse. Coverdesign:deblik,Berlin TypesettingbytheSPiusingaSpringerLATEXmacropackage Printedonacid-freepaper SPIN:11744269 89/SPi 543210 ... fu¨r Anne, Manfred und Michel ... Contents Preface ........................................................ IX Part I Structured Data and Neural Networks Introduction: Structured Data and Neural Networks........... 3 1 Kernels for Strings and Graphs Craig Saunders and Anthony Demco................................ 7 2 Comparing Sequence Classification Algorithms for Protein Subcellular Localization Fabrizio Costa, Sauro Menchetti, and Paolo Frasconi ................. 23 3 Mining Structure-Activity Relations in Biological Neural Networks using NeuronRank Tayfun Gu¨rel, Luc De Raedt, and Stefan Rotter...................... 49 4 Adaptive Contextual Processing of Structured Data by Recursive Neural Networks: A Survey of Computational Properties Barbara Hammer, Alessio Micheli, and Alessandro Sperduti ........... 67 5 Markovian Bias of Neural-based Architectures With Feedback Connections Peter Tinˇo, Barbara Hammer, and Mikael Bod´en .................... 95 6 Time Series Prediction with the Self-Organizing Map: A Review Guilherme A. Barreto ............................................135 VIII Contents 7 A Dual Interaction Perspective for Robot Cognition: Grasping as a “Rosetta Stone” Helge Ritter, Robert Haschke, and Jochen J. Steil ....................159 Part II Logic and Neural Networks Introduction: Logic and Neural Networks......................181 8 SHRUTI: A Neurally Motivated Architecture for Rapid, Scalable Inference Lokendra Shastri.................................................183 9 The Core Method: Connectionist Model Generation for First-Order Logic Programs Sebastian Bader, Pascal Hitzler, Steffen Ho¨lldobler, and Andreas Witzel ..............................................205 10 LearningModels ofPredicateLogicalTheories withNeural Networks Based on Topos Theory Helmar Gust, Kai-Uwe Ku¨hnberger, and Peter Geibel.................233 11 Advances in Neural-Symbolic Learning Systems: Modal and Temporal Reasoning Artur S. d’Avila Garcez...........................................265 12 Connectionist Representation of Multi-Valued Logic Programs Ekaterina Komendantskaya, M´aire Lane and Anthony Karel Seda ......283 Index..........................................................315 Preface The human brain possesses the remarkable capability of understanding, in- terpreting,andproducinghumanlanguage,therebyrelyingmostlyontheleft hemisphere.Theabilitytoacquirelanguageisinnateascanbeseenfromdis- orders such as specific language impairment (SLI), which manifests itself in a missing sense for grammaticality. Language exhibits strong compositionality and structure. Hence biological neural networks are naturally connected to processing and generation of high-level symbolic structures. Unliketheirbiologicalcounterparts,artificialneuralnetworksandlogicdo not form such a close liason. Symbolic inference mechanisms and statistical machine learning constitute two major and very different paradigms in arti- ficial intelligence which both have their strengths and weaknesses: Statistical methods offer flexible and highly effective tools which are ideally suited for possibly corrupted or noisy data, high uncertainty and missing information as occur in everyday life such as sensor streams in robotics, measurements in medicine such as EEG and EKG, financial and market indices, etc. The mod- els,however,areoftenreducedtoblackboxmechanismswhichcomplicatethe integration of prior high level knowledge or human inspection, and they lack theabilitytocopewitharichstructureofobjects,classes,andrelations.Sym- bolic mechanisms, on the other hand, are perfectly applicative for intuitive human-machine interaction, the integration of complex prior knowledge, and well founded recursive inference. Their capability of dealing with uncertainty andnoiseandtheirefficiencywhenaddressingcorruptedlargescalereal-world datasets,however,islimited.Thus,theinherentstrengthsandweaknessesof these two methods ideally complement each other. Neuro-symbolic integration centers at the border of these two paradigms and tries to combine the strengths of the two directions while getting rid of their weaknesses eventually aiming at artificial systems which could be competitive to human capacities of data processing and inference. Different degrees of neuro-symbolic integration exist: (1) Researchers incorporate as- pects of symbolic structures into statistical learners or they enrich structural reasoning by statistical aspects to extend the applicability of the respective X Preface paradigm. As an example, logical inference mechanisms can be enlarged by statistical reasoning mainly relying on Bayesian statistics. The resulting sys- tems are able to solve complex real-world problems, such as impressively demonstrated in recent advances of statistical-relational learning. (2) Re- searchers try to exactly map inference mechanisms of one paradigm towards the other such that a direct relation can be established and the paradigm which is ideally suited for the task at hand can be chosen without any limita- tions on the setting. Recent results on the integration of logic programs into neural networks constitute a very interesting example of this ‘core method’. This book focuses on extensions of neural methodology towards symbolic integration. According to the possible degree of integration, it is split into two parts: ‘loose’ coupling of neural paradigms and symbolic mechanisms by means of extensions of neural networks to deal with complex structures, and ‘strong’ coupling of neural and logical paradigms by means of establishing directequivalencesofneuralnetworkmodelsandsymbolicmechanisms.More detailed introductions to the chapters contained in these two parts are given later, on pages 3 and 181, respectively. A selection of the most prominent researchers in the area has contributed to this volume. Most of the chapters contain overview articles on important scientific contributions by the authors to the field, and combined they deliver a state-of-the-art overview of the main aspects of neuro-symbolic integration. Assuch,thebookissuitableasatextbookforadvancedcoursesandstudents, as well as an introduction to the field for the interested researcher. We thank all contributors, not only for their superb chapters, but also because the production of this book was a very smooth process so that it was apleasuretoworktogetheronitscompletion.Wethanktheeditor-in-chiefof thisbookseries,JanuszKacprzyk,forsuggestingtoustoeditthisvolume,and wethankThomasDitzingerfromSpringerforaveryconstructivecooperation. Finally, we thank our families for bearing with our ever-increasing workload. Barbara Hammer & Pascal Hitzler Clausthal & Karlsruhe June 2007 List of Contributors Sebastian Bader Artur S. d’Avila Garcez International Center for Computa- Department of Computing tional Logic School of Informatics Technische Universit¨at Dresden City University London 01062 Dresden, Germany London EC1V 0HB, UK [email protected] [email protected] Guilherme A. Barreto Anthony Demco Department of Teleinformatics ISIS Group, School of Electronics Engineering and Computer Science Federal University of Ceara´ University of Southampton A v. Mister Hull, S/N - C.P. Southampton, SO17 1BJ, UK 6005, CEP 60455-760, Center of [email protected] Technology Campus of Pici, Fortaleza, Ceara´, Luc De Raedt Brazil Departement Computerwetenschap- [email protected] pen Mikael Bod´en Katholieke Universiteit Leuven School of Information Technology Celestijnenlaan 200 A and Electrical Engineering 3001 Heverlee, Belgium University of Queensland, Australia [email protected] [email protected] Fabrizio Costa Paolo Frasconi Machine Learning and Neural Machine Learning and Neural Networks Group Networks Group Dipartimento di Sistemi e Informat- Dipartimento di Sistemi e Informat- ica ica Universit`a degli Studi di Firenze Universit`a degli Studi di Firenze Via Santa Marta 3 Via Santa Marta 3, 50139 Firenze, 50139 Firenze, Italy Italy [email protected] [email protected]
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