- Relational - Graphical Models Wolfram Burgard, Luc De Raedt, Kristian Kersting, Bernhard Nebel Albert-Ludwigs University Freiburg, Germany PCWP CO HRBP.

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- Relational - Graphical Models Wolfram Burgard, Luc De Raedt, Kristian Kersting, Bernhard Nebel Albert-Ludwigs University Freiburg, Germany PCWP CO HRBP HREKG HRSAT ERRCAUTER HR HISTORY CATECHOL SAO2 EXPCO2 ARTCO2 VENTALV VENTLUNG VENITUBE DISCONNECT MINVOLSET VENTMACH KINKEDTUBE INTUBATIONPULMEMBOLUS PAPSHUNT ANAPHYLAXIS MINOVL PVSAT FIO2 PRESS INSUFFANESTHTPR LVFAILURE ERRBLOWOUTPUT STROEVOLUMELVEDVOLUME HYPOVOLEMIA CVP BP Advanced I WS 06/07 Based on Cussens and Kersting‘s ICML 2004 tutorial, De Raedt and Kersting‘s ECML/PKDD 2005 tutorial, and Friedman and Koller‘s NIPS 1999 tutorial

Bayesian Networks Advanced I WS 06/07 Outline Introduction Reminder: Probability theory Basics of Bayesian Networks Modeling Bayesian networks Inference (VE, Junction tree) [Excourse: Markov Networks] Learning Bayesian networks Relational Models

Bayesian Networks Advanced I WS 06/07 Bayesian Networks 1.Finite, acyclic graph 2.Nodes: (discrete) random variables 3.Edges: direct influences 4.Associated with each node: a table representing a conditional probability distribution (CPD), quantifying the effect the parents have on the node MJ E B A - Relational

Bayesian Networks Advanced I WS 06/07 Bayesian Networks The “ICU alarm” network 37 binary random variables 509 parameters instead of PCWP CO HRBP HREKG HRSAT ERRCAUTER HR HISTORY CATECHOL SAO2 EXPCO2 ARTCO2 VENTALV VENTLUNG VENITUBE DISCONNECT MINVOLSET VENTMACH KINKEDTUBE INTUBATIONPULMEMBOLUS PAPSHUNT ANAPHYLAXIS MINOVL PVSAT FIO2 PRESS INSUFFANESTHTPR LVFAILURE ERRBLOWOUTPUT STROEVOLUMELVEDVOLUME HYPOVOLEMIA CVP BP - Relational

Bayesian Networks Advanced I WS 06/07 Bayesian Networks - Relational Effective (and to some extend efficient) inference algorithms –Variable elimination –Junction Trees –MPE Effective (and to some extend efficient) learning approaches –Expectation Maximization –Gradient Ascent Knowledge Acquisition Bottleneck, Data cheap Learning Dealing with noisy data, missing data and hidden variables Probability

Bayesian Networks Advanced I WS 06/07 Bayesian Networks: Problem Intelligence Difficulty Grade - Relational [slide due to Friedman and Koller] Bayesian nets use propositional representation Real world has objects, related to each other

Bayesian Networks Advanced I WS 06/07 Bayesian Networks: Problem Bayesian nets use propositional representation Real world has objects, related to each other Intell_J.Doe Diffic_CS101 Grade_JDoe_CS101 Intell_FGump Diffic_Geo101 Grade_FGump_Geo101 Intell_FGump Diffic_CS101 Grade_FGump_CS101 These “instances” are not independent! A C - Relational [slide due to Friedman and Koller]

Bayesian Networks Advanced I WS 06/07 How to Craft and Publish Papers Real World Are there similar papers? Which papers are relevant? Keywords Extraction Does anybody know L. D. Raedt? - Relational

Bayesian Networks Advanced I WS 06/07 How to Craft and Publish Papers P3 published-in author JLP A1 A2 J1 publication medium follow-up author-of L. D. Raedt? - Relational

Bayesian Networks Advanced I WS 06/07 How to Craft and Publish Papers P3 published-in author JLP A1 A2 J1 publication medium follow-up author-of ICRA A3 A4 P7 P2 P8 C2 C3 CIVR T-RO J2 P4 P5 P6 C1 ILP P1 P2 L. D. Raedt? - Relational

Bayesian Networks Advanced I WS 06/07 Blood Type / Genetics/ Breeding 2 Alleles: A and a Probability of Genotypes AA, Aa, aa ? AA Aa AA Aa aa Aa aa Aa AA Aa aa AA aa Aa Prior for founders Father Mother Offsprings - Relational

Bayesian Networks Advanced I WS 06/07 Blood Type / Genetics/ Breeding 2 Alleles: A and a Probability of Genotypes AA, Aa, aa ? CEPH Genotype DB, AA Aa AA Aa aa Aa aa Aa AA Aa aa AA aa Aa Prior for founders Father Mother Offsprings - Relational

Bayesian Networks Advanced I WS 06/07 Bongard´s Problems Noise? Some objects are opaque? (e.g. in relation is not always observed) - Relational

Bayesian Networks Advanced I WS 06/07 Bongard´s Problems Clustering? Noise? Some objects are opaque? (e.g. in relation is not always observed) - Relational

Bayesian Networks Advanced I WS 06/07... Other Application Areas BioInformatics Scene interpretation/ segmentation Social Networks ? Robotics Natural Language Processing Activity Recognition Planning ab dc e eab dc ab dc e ab d e c Games Data Cleaning - Relational

Bayesian Networks Advanced I WS 06/07 Why do we need relational models? Rich Probabilistic Models Comprehensibility Generalization (similar situations/individuals) Knowledge sharing Parameter Reduction / Compression Learning –Reuse of experience (training one RV might improve prediction at other RV) –More robust –Speed-up - Relational

Bayesian Networks Advanced I WS 06/07 When to apply relational models ? When it is impossible to elegantly represent your problem in attribute value form –variable number of ‘objects’ in examples –relations among objects are important - Relational A1A2A3A4A5A6 true ? false ? true ?? false... truefalse ? true ? attribute value form

Bayesian Networks Advanced I WS 06/07 Statistical Relational Learning … deals with machine learning and data mining in relational domains where observations may be missing, partially observed, and/or noisy … and is one of the key open questions in AI. Probability Learning Logic - Relational

Bayesian Networks Advanced I WS 06/07 BNs = Probabilistic Propositional Logic E. B. A :- E, B. J :- A. M :- A. MJ E B A + CPDs +

Bayesian Networks Advanced I WS 06/07 Logic Programming The maternal information mc/2 depends on the maternal and paternal pc/2 information of the mother mother/2 : mchrom(fred,a). mchrom(fred,b),... father(rex,fred). mother(ann,fred). father(brian,doro). mother(utta, doro). father(fred,henry). mother(doro,henry). pc(rex,a). mc(rex,a). pc(ann,a). mc(ann,b).... or better mc(P,a) :- mother(M,P), pc(M,a), mc(M,a). mc(P,a) :- mother(M,P), pc(M,a), mc(M,b). mc(P,b) :- mother(M,P), pc(M,a), mc(M,b) Relational Placeholder Could be rex, fred, doro, …

Bayesian Networks Advanced I WS 06/07 How to Craft and Publish Papers P3 published-in author JLP A1 A2 J1 ICRA A3 A4 P7 P2 P8 C2 C3 CIVR T-RO J2 P4 P5 P6 C1 ILP P1 P2 publication medium follow-up author-of publication(p1). publication(p2). … author(a1). author(a2). … medium(c2). medium(m2). … proceedings(m1). … journal(m1). … author-of(a1,p3). author-of(a1,p3). … author-of(a1,p1). author-of(a2,p2). … published-in(p1,m1). published-in(p3,m2). … sameAuthor(A1,A2) :- nth-author-of(A1,P1), sameTitle(P1,P2), nth-author-of(A2,P2). sameAuthor(A1,A2) :- nth-author-of(A1,P1), sameTitle(P1,P2), nth-author-of(A2,P2). Use general rules with placeholders - Relational

Bayesian Networks Advanced I WS 06/07 Outline Relational Models Relational Models –Probabilistic Relational Models –Baysian Logic Programs –Relational Markov networks –Markov Logic - Relational

Bayesian Networks Advanced I WS 06/07 Probabilistic Relational Models (PRMs) Database theory Entity-Relationship Models –Attributes = RVs alarm system Earthquake e b e be b b e BE P(A | B,E) Earthquake JohnCalls Alarm MaryCalls Burglary Alarm MaryCallsJohnCalls Table Attribute Database - Relational [Koller,Pfeffer,Getoor]

Bayesian Networks Advanced I WS 06/07 Probabilistic Relational Models (PRMs) Person Bloodtype M-chromosome P-chromosome Person Bloodtype M-chromosome P-chromosome (Father) Person Bloodtype M-chromosome P-chromosome (Mother) Table Binary Relation - Relational [Koller,Pfeffer,Getoor]

Bayesian Networks Advanced I WS 06/07 Probabilistic Relational Models (PRMs) bt(Person,BT). pc(Person,PC). mc(Person,MC). bt(Person,BT) :- pc(Person,PC), mc(Person,MC). pc(Person,PC) :- pc_father(Father,PCf), mc_father(Father,MCf). pc_father(Person,PCf) | father(Father,Person),pc(Father,PC).... father(Father,Person). Person Bloodtype M-chromosome P-chromosome Person Bloodtype M-chromosome P-chromosome (Father) Person Bloodtype M-chromosome P-chromosome (Mother) View : Dependencies (CPDs associated with): mother(Mother,Person). - Relational [Koller,Pfeffer,Getoor]

Bayesian Networks Advanced I WS 06/07 Probabilistic Relational Models (PRMs) father(rex,fred). mother(ann,fred). father(brian,doro). mother(utta, doro). father(fred,henry). mother(doro,henry). bt(Person,BT) | pc(Person,PC), mc(Person,MC). pc(Person,PC) | pc_father(Person,PCf), mc_father(Person,MCf). mc(Person,MC) | pc_mother(Person,PCm), pc_mother(Person,MCm). RV State pc_father(Person,PCf) | father(Father,Person),pc(Father,PC).... mc(rex) bt(rex) pc(rex) mc(ann) pc(ann) bt(ann) mc(fred) pc(fred) bt(fred) mc(brian) bt(brian) pc(brian) mc(utta) pc(utta) bt(utta) mc(doro) pc(doro) bt(doro) mc(henry) pc(henry) bt(henry) - Relational [Koller,Pfeffer,Getoor]

Bayesian Networks Advanced I WS 06/07 PRM Application: Collaborative Filterting User preference relationships for products / information. Traditionally: single dyactic relationship between the objects. classPers1 classProd1 buys11 buys12 buysNM classPersN classProdM... classPers2 classProd2 [Getoor, Sahami] - Relational

Bayesian Networks Advanced I WS 06/07 Relational Naive Bayes PRM Application: Collaborative Filtering classPers/1 subscribes/2 classProd/1 visits/2 manufactures reputationCompany/1 topicPage/1 topicPeriodical/1 buys/2 colorProd/1 costProd/1 incomePers/1 [Getoor, Sahami; simplified representation] - Relational

Bayesian Networks Advanced I WS 06/07 Probabilistic Relational Models (PRMs) Database View Unique Probability Distribution over finite Herbrand interpretations –No self-dependency Discrete and continuous RV BN used to do inference Graphical Representation [Koller,Pfeffer,Getoor] - Relational

Bayesian Networks Advanced I WS 06/07 Outline Relational Models Relational Models –Probabilistic Relational Models –Baysian Logic Programs –Relational Markov networks –Markov Logic - Relational

Bayesian Networks Advanced I WS 06/ e b e be b b e BE P(A | B,E) Earthquake JohnCalls Alarm MaryCalls Burglary Bayesian Logic Programs (BLPs) - Relational [Kersting, De Raedt]

Bayesian Networks Advanced I WS 06/ e b e be b b e BE P(A | B,E) Earthquake JohnCalls Alarm MaryCalls Burglary Bayesian Logic Programs (BLPs) alarm/0 earthquake/0 burglary/0 maryCalls/0 johnCalls/0 Rule Graph - Relational [Kersting, De Raedt]

Bayesian Networks Advanced I WS 06/ e b e be b b e BE P(A | B,E) Earthquake JohnCalls Alarm MaryCalls Burglary Bayesian Logic Programs (BLPs) alarm/0 earthquake/0 burglary/0 maryCalls/0 johnCalls/0 Rule Graph alarm earthquake burglary e b e be b b e BE P(A | B,E) local BN fragment alarm :- earthquake, burglary. - Relational [Kersting, De Raedt]

Bayesian Networks Advanced I WS 06/07 bt pc mc Person ba(.03,.03,.9,.03)... aa(.9,.03,.03,.03) mc(Person)pc(Person)bt(Person) bt(Person) :- pc(Person),mc(Person). Bayesian Logic Programs (BLPs) bt/1 pc/1 mc/1 argument predicate atom variable Rule Graph mc pc mc Person mother ba(.495,.495,.01)... aa(.9,.05,.05) mc(Mother)pc(Mother)mc(Person) Mother - Relational [Kersting, De Raedt]

Bayesian Networks Advanced I WS 06/07 Bayesian Logic Programs (BLPs) bt/1 pc/1 mc/1 pc(Person) | father(Father,Person), pc(Father),mc(Father). mc(Person) | mother(Mother,Person), pc(Mother),mc(Mother). bt(Person) | pc(Person),mc(Person). pc mc Person father ba(.495,.495,.01)... aa(.9,.05,.05) mc(Father)pc(Father)pc(Person) Father - Relational [Kersting, De Raedt]

Bayesian Networks Advanced I WS 06/07 Bayesian Logic Programs (BLPs) father(rex,fred). mother(ann,fred). father(brian,doro). mother(utta, doro). father(fred,henry). mother(doro,henry). mc(rex) bt(rex) pc(rex) mc(ann) pc(ann) bt(ann) mc(fred) pc(fred) bt(fred) mc(brian) bt(brian) pc(brian) mc(utta) pc(utta) bt(utta) mc(doro) pc(doro) bt(doro) mc(henry) pc(henry) bt(henry) pc(Person) | father(Father,Person), pc(Father),mc(Father). mc(Person) | mother(Mother,Person), pc(Mother),mc(Mother). bt(Person) | pc(Person),mc(Person). Bayesian Network induced over least Herbrand model - Relational [Kersting, De Raedt]

Bayesian Networks Advanced I WS 06/07 Answering Queries P( bt(ann) ) ? mc(rex) bt(rex) pc(rex) mc(ann) pc(ann) bt(ann) mc(fred) pc(fred) bt(fred) mc(brian) bt(brian) pc(brian) mc(utta) pc(utta) bt(utta) mc(doro) pc(doro) bt(doro) mc(henry) pc(henry) bt(henry) Bayesian Network induced over least Herbrand model - Relational

Bayesian Networks Advanced I WS 06/07 Answering Queries P( bt(ann), bt(fred) ) ? P( bt(ann) | bt(fred) ) = P( bt(ann),bt(fred) ) P( bt(fred) ) Bayes‘ rule mc(rex) bt(rex) pc(rex) mc(ann) pc(ann) bt(ann) mc(fred) pc(fred) bt(fred) mc(brian) bt(brian) pc(brian) mc(utta) pc(utta) bt(utta) mc(doro) pc(doro) bt(doro) mc(henry) pc(henry) bt(henry) Bayesian Network induced over least Herbrand model - Relational

Bayesian Networks Advanced I WS 06/07 Combining Partial Knowledge passes/1 read/1 prepared/2 discusses/2... bn passes Student prepared logic prepared passes(Student) | prepared(Student,bn), prepared(Student,logic). prepared Student read discusses Book Topic prepared(Student,Topic) | read(Student,Book), discusses(Book,Topic). - Relational

Bayesian Networks Advanced I WS 06/07 Combining Partial Knowledge prepared(s1,bn) discusses(b1,bn) prepared(s2,bn) discusses(b2,bn) variable # of parents for prepared/2 due to read/2 –whether a student prepared a topic depends on the books she read CPD only for one book-topic pair prepared Student read discusses Book Topic - Relational

Bayesian Networks Advanced I WS 06/07 Combining Rules P(A|B,C) P(A|B) and P(A|C) CR Any algorithm which has an empty output if and only if the input is empty combines a set of CPDs into a single (combined) CPD E.g. noisy-or, regression,... prepared Student read discusses Book Topic - Relational

Bayesian Networks Advanced I WS 06/07 Aggregates student_ranking/1 registration_grade/2... registered/2 - Relational Map multisets of values to summary values (e.g., sum, average, max, cardinality)

Bayesian Networks Advanced I WS 06/07 Aggregates student_ranking/1 registration_grade/2... registered/2 Map multisets of values to summary values (e.g., sum, average, max, cardinality) grade_avg/1 Deterministic grade_avg Student registration_grade Functional Dependency (average) registered/2 Course student_ranking Student grade_avg Probabilistic Dependency (CPD) - Relational

Bayesian Networks Advanced I WS 06/07 Experiments KDD Cup 2001 localization task predict the localization based on local features and interactions 862 training genes 381 test genes >1000 interactions 16 classes KDD Cup 2001 localization task predict the localization based on local features and interactions 862 training genes 381 test genes >1000 interactions 16 classes WebKB predict the type of web pages 877 web pages from 4 CS department 1516 links 6 classes WebKB predict the type of web pages 877 web pages from 4 CS department 1516 links 6 classes - Relational

Bayesian Networks Advanced I WS 06/07 KDD Cup: Protein Localization RFK (72.89%) better then Hayashi et al.’s KDD Cup 2001 winning nearest- neighbour approach (72.18%) - Relational

Bayesian Networks Advanced I WS 06/07 WebKB: Web Page Classification Leave-one-university-out cross-validation Collective NB ~ PRMs [Getoor et al. 02] RFK outperforms PRMs PRM with structural uncertainty over the links, best acc. (68%) on Washington Collective NB ~ PRMs [Getoor et al. 02] RFK outperforms PRMs PRM with structural uncertainty over the links, best acc. (68%) on Washington - Relational

Bayesian Networks Advanced I WS 06/07 Bayesian Logic Programs (BLPs) Unique probability distribution over Herbrand interpretations –Finite branching factor, finite proofs, no self-dependency Highlight –Separation of qualitative and quantitative parts –Functors Graphical Representation Discrete and continuous RV - Relational

Bayesian Networks Advanced I WS 06/07 Learning Tasks Parameter Estimation –Numerical Optimization Problem Model Selection –Combinatorical Search Database Model Learning Algorithm - Relational

Bayesian Networks Advanced I WS 06/07 RVs + States = (partial) Herbrand interpretation Probabilistic learning from interpretations What is the data about? Family(1) pc(brian)=b, bt(ann)=a, bt(brian)=?, bt(dorothy)=a Family(1) pc(brian)=b, bt(ann)=a, bt(brian)=?, bt(dorothy)=a Family(2) bt(cecily)=ab, pc(henry)=a, mc(fred)=?, bt(kim)=a, pc(bob)=b Family(2) bt(cecily)=ab, pc(henry)=a, mc(fred)=?, bt(kim)=a, pc(bob)=b Background m(ann,dorothy), f(brian,dorothy), m(cecily,fred), f(henry,fred), f(fred,bob), m(kim,bob),... Background m(ann,dorothy), f(brian,dorothy), m(cecily,fred), f(henry,fred), f(fred,bob), m(kim,bob),... Family(3) pc(rex)=b, bt(doro)=a, bt(brian)=? Family(3) pc(rex)=b, bt(doro)=a, bt(brian)=? - Relational

Bayesian Networks Advanced I WS 06/07 Parameter Estimation + - Relational

Bayesian Networks Advanced I WS 06/07 Parameter Estimation + Parameter tying - Relational

Bayesian Networks Advanced I WS 06/07 Expectation Maximization Initial Parameters q 0 Logic Program L Expected counts of a clause Expectation Inference Update parameters (ML, MAP) Maximization EM-algorithm: iterate until convergence Current Model ( M,qk ) P( head(GI), body(GI) | DC ) M M DataCase DC Ground Instance GI P( head(GI), body(GI) | DC ) M M DataCase DC Ground Instance GI P( body(GI) | DC ) M M DataCase DC Ground Instance GI - Relational

Bayesian Networks Advanced I WS 06/07 Model Selection Combination of ILP and BN learning Modify the general rules syntactically: –Add atoms: b(X,a) –Delete atoms –Unify placeholders: m(X,Y) -> m(X,X) –... Add, (reverse, and) delete bunches of edges simultaniously - Relational

Bayesian Networks Advanced I WS 06/07 Example mc(john) pc(john) bc(john) m(ann,john)f(eric,john) pc(ann) mc(ann)mc(eric) pc(eric) mc(X) | m(M,X), mc(M), pc(M). pc(X) | f(F,X), mc(F), pc(F). bt(X) | mc(X), pc(X). Original program {m(ann,john)=true, pc(ann)=a, mc(ann)=?, f(eric,john)=true, pc(eric)=b, mc(eric)=a, mc(john)=ab, pc(john)=a, bt(john) = ? }... Data cases - Relational

Bayesian Networks Advanced I WS 06/07 mc(john) pc(john) bc(john) m(ann,john)f(eric,john) pc(ann) mc(ann)mc(eric) pc(eric) Example mc(X) | m(M,X), mc(M), pc(M). pc(X) | f(F,X), mc(F), pc(F). bt(X) | mc(X), pc(X). Original program mc(X) | m(M,X). pc(X) | f(F,X). bt(X) | mc(X). Initial hypothesis - Relational

Bayesian Networks Advanced I WS 06/07 Example mc(X) | m(M,X), mc(M), pc(M). pc(X) | f(F,X), mc(F), pc(F). bt(X) | mc(X), pc(X). Original program mc(X) | m(M,X). pc(X) | f(F,X). bt(X) | mc(X). Initial hypothesis mc(john) pc(john) bc(john) m(ann,john)f(eric,john) pc(ann) mc(ann)mc(eric) pc(eric) - Relational

Bayesian Networks Advanced I WS 06/07 Example mc(X) | m(M,X), mc(M), pc(M). pc(X) | f(F,X), mc(F), pc(F). bt(X) | mc(X), pc(X). Original program mc(john) pc(john) bc(john) m(ann,john)f(eric,john) pc(ann) mc(ann)mc(eric) pc(eric) mc(X) | m(M,X). pc(X) | f(F,X). bt(X) | mc(X), pc(X). Refinement mc(X) | m(M,X). pc(X) | f(F,X). bt(X) | mc(X). Initial hypothesis - Relational

Bayesian Networks Advanced I WS 06/07 Example mc(X) | m(M,X), mc(M), pc(M). pc(X) | f(F,X), mc(F), pc(F). bt(X) | mc(X), pc(X). Original program mc(john) pc(john) bc(john) m(ann,john)f(eric,john) pc(ann) mc(ann)mc(eric) pc(eric) mc(X) | m(M,X),mc(X). pc(X) | f(F,X). bt(X) | mc(X), pc(X). Refinement mc(X) | m(M,X). pc(X) | f(F,X). bt(X) | mc(X). Initial hypothesis mc(X) | m(M,X). pc(X) | f(F,X). bt(X) | mc(X), pc(X). Refinement - Relational

Bayesian Networks Advanced I WS 06/07 Example mc(X) | m(M,X), mc(M), pc(M). pc(X) | f(F,X), mc(F), pc(F). bt(X) | mc(X), pc(X). Original program mc(john) pc(john) bc(john) m(ann,john)f(eric,john) pc(ann) mc(ann)mc(eric) pc(eric) mc(X) | m(M,X),pc(X). pc(X) | f(F,X). bt(X) | mc(X), pc(X). Refinement mc(X) | m(M,X). pc(X) | f(F,X). bt(X) | mc(X). Initial hypothesis mc(X) | m(M,X). pc(X) | f(F,X). bt(X) | mc(X), pc(X). Refinement - Relational

Bayesian Networks Advanced I WS 06/07 Example  mc(X) | m(M,X), mc(M), pc(M). pc(X) | f(F,X), mc(F), pc(F). bt(X) | mc(X), pc(X). Original program mc(john) pc(john) bc(john) m(ann,john)f(eric,john) pc(ann) mc(ann)mc(eric) pc(eric)... mc(X) | m(M,X),pc(X). pc(X) | f(F,X). bt(X) | mc(X), pc(X). Refinement mc(X) | m(M,X). pc(X) | f(F,X). bt(X) | mc(X). Initial hypothesis mc(X) | m(M,X). pc(X) | f(F,X). bt(X) | mc(X), pc(X). Refinement - Relational

Bayesian Networks Advanced I WS 06/07 Outline Relational Models Relational Models –Probabilistic Relational Models –Baysian Logic Programs –Relational Markov networks –Markov Logic - Relational

Bayesian Networks Advanced I WS 06/07 Undirected Relational Models So far, directed graphical models only Impose acyclicity constraint Undirected graphical models do not impose the acyclicity constraint - Relational

Bayesian Networks Advanced I WS 06/07 Undirected Relational Models Two approaches –Relational Markov Networks (RMNs) (Taskar et al.) –Markov Logic Networks (MLNs) (Anderson et al.) Idea –Semantics = Markov Networks –More natural for certain applications RMNs ~ undirected PRM MLNs ~ undirected BLP - Relational

Bayesian Networks Advanced I WS 06/07 Markov Networks To each clique c, a potential is associated Given the values of all nodes in the Markov Network B B D D C C A A - Relational

Bayesian Networks Advanced I WS 06/07 Relational Markov Networks SELECT doc1.Category,doc2.Category FROM doc1,doc2,Link link WHERE link.From=doc1.key and link.To=doc2.key Doc1Doc2 Link Doc1 - Relational

Bayesian Networks Advanced I WS 06/07 Markov Logic Networks Cancer(A) Smokes(A) Smokes(B) Cancer(B) Suppose we have two constants: Anna (A) and Bob (B) slides by Pedro Domingos - Relational

Bayesian Networks Advanced I WS 06/07 Markov Logic Networks Cancer(A) Smokes(A) Friends(A,A) Friends(B,A) Smokes(B) Friends(A,B) Cancer(B) Friends(B,B) Suppose we have two constants: Anna (A) and Bob (B) slides by Pedro Domingos - Relational

Bayesian Networks Advanced I WS 06/07 Markov Logic Networks Cancer(A) Smokes(A) Friends(A,A) Friends(B,A) Smokes(B) Friends(A,B) Cancer(B) Friends(B,B) Suppose we have two constants: Anna (A) and Bob (B) slides by Pedro Domingos - Relational

Bayesian Networks Advanced I WS 06/07 Markov Logic Networks Cancer(A) Smokes(A) Friends(A,A) Friends(B,A) Smokes(B) Friends(A,B) Cancer(B) Friends(B,B) Suppose we have two constants: Anna (A) and Bob (B) slides by Pedro Domingos - Relational

Bayesian Networks Advanced I WS 06/07 Learning Undirected PRMs Parameter estimation –discriminative (gradient, max-margin) –generative setting using pseudo- likelihood Structure learning –Similar to PRMs, BLPs - Relational

Bayesian Networks Advanced I WS 06/07 Applications Computer Vision –(Taskar et al.) Citation Analysis –(Taskar et al., Singla&Domingos) Activity Recognition –(Liao et al.) - Relational

Bayesian Networks Advanced I WS 06/07 Activity Recognition [Fox et al. IJCAI03] Will you go to the AdvancedAI lecture or will you visit some friends in a cafe? Lecture Hall Cafe - Relational

Bayesian Networks Advanced I WS 06/07 3D Scan Data Segmentation [Anguelov et al. CVPR05, Triebel et al. ICRA06] How do you recognize the lecture hall? - Relational

Bayesian Networks Advanced I WS 06/07 Outline Relational Models Relational Models –Probabilistic Relational Models –Baysian Logic Programs –Relational Markov networks –Markov Logic - Relational

Bayesian Networks Advanced I WS 06/07 Conclusions SRL = Probability + Logic + Learning Covers full AI spectrum: Logic, probability, learning, kernels, sequences, planning, reinforcement learning, … Considered to be a revolution in ML Logical variables/Placeholders: group random variables/states Unification: context-specific prob. information - Relational

Bayesian Networks Advanced I WS 06/07 … for your attention … and enjoy the other parts of the lecture ! - Relational Thanks