One of the key open questions within arti?cial intelligence is how to combine probability and logic with learning. This question is getting an increased - tentioninseveraldisciplinessuchasknowledgerepresentation, reasoningabout uncertainty, data mining, and machine learning simulateously, resulting in the newlyemergingsub?eldknownasstatisticalrelationallearningandprobabil- ticinductivelogicprogramming.Amajordriving forceisthe explosivegrowth in the amount of heterogeneous data that is being collected in the business and scienti?c world. Example domains include bioinformatics, chemoinform- ics, transportation systems, communication networks, social network analysis, linkanalysis, robotics, amongothers.Thestructuresencounteredcanbeass- pleassequencesandtrees(suchasthosearisinginproteinsecondarystructure predictionandnaturallanguageparsing)orascomplexascitationgraphs, the WorldWideWeb, andrelationaldatabases. This book providesan introduction to this ?eld with an emphasison those methods based on logic programming principles. The book is also the main resultofthesuccessfulEuropeanISTFETprojectno.FP6-508861onAppli- tionofProbabilisticInductiveLogicProgramming(APRILII,2004-2007).This projectwascoordinatedbytheAlbertLudwigsUniversityofFreiburg(Germany, Luc De Raedt) and the partners were Imperial College London (UK, Stephen MuggletonandMichaelSternberg), theHelsinkiInstituteofInformationTe- nology(Finland, HeikkiMannila), theUniversit adegliStudidiFlorence(Italy, PaoloFrasconi), andtheInstitutNationaldeRechercheenInformatiqueet- tomatiqueRocquencourt(France, FrancoisFages).Itwasconcernedwiththeory, implementationsandapplicationsofprobabilisticinductivelogicprogramming. Thisstructureisalsore?ectedinthebook. The book starts with an introductory chapter to "Probabilistic Inductive LogicProgramming"byDeRaedtandKersting.Inasecondpart, itprovidesa detailedoverviewofthemostimportantprobabilisticlogiclearningformalisms and systems. We are very pleased and proud that the scientists behind the key probabilistic inductive logic programming systems (also those developed outside the APRIL project) have kindly contributed a chapter providing an overviewoftheircontributions.Thisincludes: relationalsequencelearningte- niques (Kersting et al.), using kernels with logical representations (Frasconi andPasserini), MarkovLogic(Domingosetal.), the PRISMsystem (Satoand Kameya), CLP(BN)(SantosCostaetal.), BayesianLogicPrograms(Kersting andDeRaedt), andtheIndependentChoiceLogic(Poole).Thethirdpartthen provides a detailed account of some show-caseapplications of probabilistic - ductive logic programming, more speci?cally: in protein fold discovery (Chen et al.), haplotyping (Landwehr and Mielik] ainen) and systems biology (Fages andSoliman). The ?nal parttouchesupon sometheoreticalinvestigationsand VI Preface includes chaptersonbehavioralcomparisonof probabilisticlogicprogramming representations(MuggletonandChen)andamodel-theoreticexpressivityan- ysis(Jaege

The three volume set LNAI 9851, LNAI 9852, and LNAI 9853 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2016, held in Riva del Garda, Italy, in September 2016. The 123 full papers and 16 short papers presented were carefully reviewed and selected from a total of 460 submissions. The papers presented focus on practical and real-world studies of machine learning, knowledge discovery, data mining; innovative prototype implementations or mature systems that use machine learning techniques and knowledge discovery processes in a real setting; recent advances at the frontier of machine learning and data mining with other disciplines. Part I and Part II of the proceedings contain the full papers of the contributions presented in the scientific track and abstracts of the scientific plenary talks. Part III contains the full papers of the contributions presented in the industrial track, short papers describing demonstration, the nectar papers, and the abstracts of the industrial plenary talks.

The three volume set LNAI 9851, LNAI 9852, and LNAI 9853 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2016, held in Riva del Garda, Italy, in September 2016. The 123 full papers and 16 short papers presented were carefully reviewed and selected from a total of 460 submissions. The papers presented focus on practical and real-world studies of machine learning, knowledge discovery, data mining; innovative prototype implementations or mature systems that use machine learning techniques and knowledge discovery processes in a real setting; recent advances at the frontier of machine learning and data mining with other disciplines. Part I and Part II of the proceedings contain the full papers of the contributions presented in the scientific track and abstracts of the scientific plenary talks. Part III contains the full papers of the contributions presented in the industrial track, short papers describing demonstration, the nectar papers, and the abstracts of the industrial plenary talks.