DTAI Thesis Topics Dept. Computer Science KU Leuven 2018-2019 http://people.cs.kuleuven.be/~luc.deraedt/dtaithesis18-19.pdf Luc De Raedt
Lab for Declarative Languages and Artificial Intelligence Machine Learning 4 ZAP , 1 res. manager, 1 res. expert ± 4 post-docs ± 25 Ph.D. students Declarative Languages and Systems 5 ZAP ± 3 post-docs ±12 Ph.D. students Bruynooghe retired 2
AI is hot! Self-driving cars - Eve (the robot scientist) Siri IBM Watson in Jeopardy and “Machine Reading” AlphaGo — (Deep) learning … 3
DTAI's focus on AI Machine Learning & Data Mining how to extract knowledge from data Uncertainty reasoning how to represent and reason about uncertainty Knowledge Representation how to represent and reason about knowledge 4
DTAI's focus on Declarative Languages Declarative = specify the what rather than the how Different types of languages Logic Functional Constraints Probabilistic 5
DTAI's methodology involves Fundamental research (theoretical as well as empirical) Systems, Solvers and Software Applications Thesis can focus on one or more aspects, depending on interests student This presentation does not go in depth about techniques but every thesis does 6
This presentation Overview of research illustrations of possible thesis topics. List of contact persons for topics Full information — see online Own topic should be aligned with interests professor 7
Research Topics Probabilistic Programming and Predictive Learning and Automated Data Science Statistical Relational Learning Clustering Exploratory Data Mining Graph and Network Mining Privacy, Non-discrimination and Ethical aspects Constraints Knowledge-Base Systems Verification of AI and ML Static Analysis for Declarative Functional Programming Programming Languages ( dtai.cs.kuleuven.be/research )
Applications Sports Analytics Health Engineering & Sensors Robotics Games Text and Web Humor (Comp. Creativity) Applications of the Knowledge Base System Paradigm ( dtai.cs.kuleuven.be/research ) 9
Research topics
Predictive learning and clustering Contact: Hendrik Blockeel, Jesse Davis “Standard” machine learning develop new algorithms for machine learning Decision Trees Predictive Clustering Probabilistic Graphical Models evaluation of machine learning (ROC etc.) 11
Automated Data Science Contact: Luc De Raedt, Anton Dries Can we (partly) automate data science ? Can we automatically derive the right features ? the right representations ? Can we automatically discover what we can learn / predict ? Can we learn constraints ? Example database about students, professors, courses, and marks … The SYNTH project — the democratisation of Data Science the automation of Data Science 12
Automated Data Science Contact: Luc De Raedt, Anton Dries Inductive Programming FlashFill in Excel 13
Automated Data Science Contact: Luc De Raedt, Anton Dries Learning constraints I What are the formulas here? T1[:, 6] = SUM(T1[:, 3:5], row) I T2[:, 2] = SUMIF(T1[:, 1]=T2[:, 1], T1[:, 6]) I 14
Probabilistic Programming and Statistical Relational Learning Contact: Luc De Raedt, Hendrik Blockeel, Jesse Davis, Gerda Janssens Key open question in AI — integrate Probabilistic reasoning Logical or relational Machine learning representations statistical relational probabilistic programming learning 15
Probabilistic Programming and Statistical Relational Learning E.g. ProbLog: a probabilistic Prolog i 0.05 :: burglary. alarm :- burglary. 0.01 :: earthquake. alarm :- earthquake. 0.7 :: hears_alarm(john). calls(Pers) :- alarm, hears_alarm(Pers). 0.6 :: hears_alarm(mary). P( hears_alarm(john) | burglary = true) ? Challenges on inference, learning, implementation, application, ... 16
Probabilistic Programming and Statistical Relational Learning Action and activity learning / Dynamics Travian: A massively multiplayer real-time strategy game Commercial game run by TravianGames GmbH ~3.000.000 players spread over different “worlds” Can we build a model of this world ? Can we use it for playing better ? [Thon et al. ECML 08] 17
Verifying AI & ML systems Contact: Luc De Raedt, Hendrik Blockeel, Jesse Davis, Bettina Berendt & Wannes Meert Verification of software has a long tradition (eg model checking techniques) How to verify systems that learn ? that use AI ? Our approach — combined principles of probabilistic logics with verification Topics inductive synthesis of specifications Markov Decision Processes (& reinforcement leanring) Derive properties of learned systems … 18
Robotics Contact: Luc De Raedt Learn probabilistic - logic model Moldovan et al. ICRA 12, 13, 14 Shelf grasp Shelf Shelf tap push 19
Robotics (and Vision) Contact: Luc De Raedt The visual genome
Socially Aware Data Mining Graph and Network Mining Contact: Bettina Berendt Help users manage friends and privacy by data mining Focus on Privacy and (anti-discrimination) 21
Text and Web Contact: Bettina Berendt, Jesse Davis Extraction of information from the web / social media Taxonomy learning Machine reading / Natural language processing NaturalMachine reading … 22
Knowledge-Base Systems Contact: Marc Denecker, Gerda Janssens IDP Advanced KBS system developed by group FO(.) language rooted in predicate logic and logic programming separation of domain knowledge and problem solving Language extensions to increase expressivity E.g. design patterns for FO(.) (past thesis) Better solvers and more inference methods E.g. a solver for rational numbers (past thesis) 23
Knowledge-Base Systems Contact: Marc Denecker, Gerda Janssens Three themes for students : logical modeling of interesting AI problem + expressing AI knowledge domains logical analysis and implementation of software systems and tasks + software by applying inference on specifications Advanced algorithmics and implementation + extending/optimising the IDP software package. 24
Applications of the Knowledge Base System Paradigm Logical modeling of AI problems Analysing medieval manuscripts vocabulary Vms { extern vocabulary V IsSource(Manuscript ) } theory Tms : Vms { { ! x : IsSource(x) <- ~ ? y : CopiedBy(y, x) & VariantIn(y) = VariantIn(x). } } term NbOfSources : Vms { #{ x : IsSource(x) } - monks copied texts } - resulting in variants (colors) procedure minSources(feature) { setvocabulary(feature, Vms) - reconstruct history return minimize(Tms, feature, NbOfSources)[1] } - special-purpose datamining-program: 400 lines of Perl, bugs - description problem in IDP: 15 lines, correct, somewhat faster 25
Applications of the Knowledge Base System Paradigm Contact: Marc Denecker, Gerda Janssens Software = Knowledge Base + Logical Inference + User Interface E.g., An interactive configuration system for an insurance company AIM : Build cheap, correct, reusable, maintainable software from a logical specification 26
Applications of the Knowledge Winning the RuleML Challenge Base System Paradigm Insurance application Propagation constraints and choices Fill out necessary values 27
Knowledge-Base Systems Contact: Marc Denecker, Gerda Janssens Advanced algorithmics and implementation + extending/ optimising the IDP software package. help us win the next CP or ASP competition + E.g., structuring search space as a hierarchy of search problems + E.g., linear programming techniques in IDP + E.g., improved computation of definitions + E.g., algorithms for revision inference (updating solutions) 28
Constraints Contact: Tom Schrijvers, Marc Denecker, & Luc De Raedt • Hyper heuristics to solve constraint satisfaction and optimization problems — formalisation • Search Heuristics • Role in IDP • Role in Data Mining • Learning of constraints 29
UITLEG: Functional Programming Functional Programming Je kent Functional Programming van de taal Haskell uit het vak Declaratieve Talen. Contact: Tom Schrijvers Op onderzoeksgebied werken we rond alle aspecten van functionele Haskell talen, en Haskell in het bijzonder. Actuele onderwerpen zijn: - expliciete side-e ff ects zoals monads, - gevorderde type system features - domein-specifieke talen ★ Explicit Side-Effects - en nog veel meer Monads Transformers Effect Handlers ★ Advanced Type Systems Type Classes Polymorphism Kinds ★ Domain-Specific Languages Design Infrastructure Applications ★ Much more… 30
Functional Programming Widespread Adoption Haskell Language + GHC Compiler UITLEG: Heel wat interessante uitdagingen komen voort uit de gr Haskell mainstream adoptie van Functional Pr Finance Telecom Many Others Hoe langer hoe meer bedrijven gaan aan de slag met functionele talen in zoals Haskell en F# (F-sharp), en mainstream talen zoals Java en C# adopter industry concepten. Functional Languages Mainstream FP 1936 1973 1987 now 1958 2007 2014 λ calculus Lisp ML Haskell C# Java 8 mainstream 2011 Swift C++11 Alonzo John Robin Haskell Church McCarthy Milner Committee 31
Functional Programming 201: The Oracle of Haskell abs x | x >= 0 = x | x < 0 = -x GHC your oracle compiler ✓ exhaustive guards UITLEG: ontwikkel een orakel dat nagaat of guards in Haskell-programma’s alle gevallen dekken 32
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