DTAI Thesis Topics Dept. Computer Science KU Leuven 2018-2019 - - PowerPoint PPT Presentation
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
http://people.cs.kuleuven.be/~luc.deraedt/dtaithesis18-19.pdf Luc De Raedt
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Declarative Languages and Systems
Bruynooghe retired
Self-driving cars - Eve (the robot scientist) Siri IBM Watson in Jeopardy and “Machine Reading” AlphaGo — (Deep) learning …
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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
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Declarative = specify the what rather than the how Different types of languages Logic Functional Constraints Probabilistic
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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
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
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Probabilistic Programming and Statistical Relational Learning Predictive Learning and Clustering Graph and Network Mining Static Analysis for Declarative Programming Languages Exploratory Data Mining Privacy, Non-discrimination and Ethical aspects Knowledge-Base Systems Constraints
(dtai.cs.kuleuven.be/research)Functional Programming Automated Data Science Verification of AI and ML
9 Sports Analytics Text and Web Health Games
Engineering & Sensors Humor (Comp. Creativity) Robotics Applications of the Knowledge Base System Paradigm
(dtai.cs.kuleuven.be/research) 11
Predictive learning and clustering
Contact: Hendrik Blockeel, Jesse Davis
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Automated Data Science
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
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Automated Data Science
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Automated Data Science
I What are the formulas here? I
T1[:, 6] = SUM(T1[:, 3:5], row)
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T2[:, 2] = SUMIF(T1[:, 1]=T2[:, 1], T1[:, 6])
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Probabilistic reasoning Logical or relational representations Machine learning
Contact: Luc De Raedt, Hendrik Blockeel, Jesse Davis, Gerda Janssens
Probabilistic Programming and Statistical Relational Learning
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E.g. ProbLog: a probabilistic Prolog P( hears_alarm(john) | burglary = true) ? Challenges on inference, learning, implementation, application, ...
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0.05 :: burglary. 0.01 :: earthquake. 0.7 :: hears_alarm(john). 0.6 :: hears_alarm(mary). alarm :- burglary. alarm :- earthquake. calls(Pers) :- alarm, hears_alarm(Pers).
Probabilistic Programming and Statistical Relational Learning
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Travian: A massively multiplayer real-time strategy game Commercial game run by TravianGames GmbH ~3.000.000 players spread over different “worlds”
[Thon et al. ECML 08]
Can we build a model of this world ? Can we use it for playing better ?
Probabilistic Programming and Statistical Relational Learning
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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 …
Verifying AI & ML systems
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Learn probabilistic - logic model
Shelf push Shelf tap Shelf grasp
Moldovan et al. ICRA 12, 13, 14
Contact: Luc De Raedt
Robotics (and Vision)
The visual genome
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Help users manage friends and privacy by data mining
Socially Aware Data Mining
Graph and Network Mining
Contact: Bettina Berendt, Jesse Davis
Extraction of information from the web / social media Taxonomy learning Machine reading / Natural language processing NaturalMachine reading …
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Text and Web
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)
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Knowledge-Base Systems
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
Advanced algorithmics and implementation + extending/optimising the IDP software package.
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Knowledge-Base Systems
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) } } procedure minSources(feature) { setvocabulary(feature, Vms) return minimize(Tms, feature, NbOfSources)[1] }
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Applications of the Knowledge Base System Paradigm
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 Base System Paradigm
27 Applications of the Knowledge Base System Paradigm
Winning the RuleML Challenge Insurance application Propagation constraints and choices Fill out necessary values
Advanced algorithmics and implementation + extending/
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)
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Knowledge-Base Systems
Contact: Tom Schrijvers, Marc Denecker, & Luc De Raedt
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Constraints
and optimization problems — formalisation
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Functional Programming
Haskell
★ Explicit Side-Effects
★ Advanced Type Systems
★ Domain-Specific Languages
★ Much more…
Monads Effect Handlers Transformers Type Classes Polymorphism Kinds Design Infrastructure Applications
UITLEG: Je kent Functional Programming van de taal Haskell uit het vak Declaratieve Talen. Op onderzoeksgebied werken we rond alle aspecten van functionele talen, en Haskell in het bijzonder. Actuele onderwerpen zijn:
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λ calculus 1936 Alonzo Church 1958 Lisp John McCarthy 1973 ML Robin Milner 1987 Haskell Haskell Committee 2014 Java 8 Swift 2011 C++11
Functional Languages Mainstream
2007 C#
FP now mainstream
Haskell Language + GHC Compiler Finance Many Others Telecom
Haskell in industry
UITLEG: Heel wat interessante uitdagingen komen voort uit de gr mainstream adoptie van Functional Pr Hoe langer hoe meer bedrijven gaan aan de slag met functionele talen zoals Haskell en F# (F-sharp), en mainstream talen zoals Java en C# adopter concepten.
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GHC compiler abs x | x >= 0 = x | x < 0 = -x your oracle
UITLEG:
guards in Haskell-programma’s alle gevallen dekken
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★ Type Checking ★ Termination Analysis ★ Reasoning about Coroutines
UITLEG: Je kent de Declaratieve Taal Prolog uit het vak Declaratieve Talen. Op onderzoeksgebied werken we rond de automatische analyse van Prolog-programma’s. Actuele onderwerpen zijn:
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append([],L,L). append([X|Xs],Ys,[X|Zs]) :- append(Xs,Ys,Zs).
powerful dynamic flexible
correctness termination
UITLEG: Delcaratieve talen zoals Prolog zijn heel krachtig, dynamisch en flexibel. De uitdaging bestaat erin om automatisch belangrijke eigenschappen af te leiden van Prolog programma’s om na te gaan of ze correct zijn, altijd eindigen en hoe je ze efficient kan compileren.
bugs
Delcarative Programming Languages
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Prosyn Expert System 1 MegaLoC Prolog
Case Study: Industrial Partner
Prolog Program Types
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your type checker
UITLEG: Prolog is een ongetypeerde taal. Hierdoor is het makkelijke om via schrijffouten moeilijk op te sporen bugs te introduceren. In deze thesis ontwikkel je een type systeem voor Prolog: De programmeur schrijft type-signaturen voor zijn predikaten, en jouw type checker gebruikt die om bugs op te sporen. Je evalueert je type checker op het Prosyn expert systeem van onze industriele partner. Dat bestaat uit 1 miljoen lijnen Prolog code.
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Contact: Wannes Meert
Boeing Jetairfly EuroMillions Basketball League 3E Sirris Thomson-Reuters Xenit Pepite Melexis Flanders Make imec Cern …
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Machine Learning for sports Soccer & basketball E-sports
Sports Analytics
Strategy detection Performance analysis & prediction Scouting
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Sports Analytics
Soccer analytics
Model flow of a game Quantify team performance Learn aging curves of players
Basketball analytics
Detect surprising events
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Sports Analytics
Continuous monitoring Injury risk profiles
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Performance management and Injury prevention Sensor fusion for surface detection and skill detection in runners Kinect monitoring for qualitative feedback during rehabilitation
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Contact: Wannes Meert, Jesse Davis
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http://www.pom-sbo.org http://www.pom2sbo.org ejector
Mebios-KU Leuven setup
Contact: Wannes Meert, Jesse Davis, Luc De Raedt
Badminton-spelende robot: http://www.youtube.com/watch?v=StPZLZq01Xs
Contact: Wannes Meert, Jesse Davis, Luc De Raedt
Large Hadron Collider maintenance (CERN)
Analysing data from airplanes
learning to solve science tests formulated in natural language (like SAT, GMAT, GRE, …) Tests as a testbed for intelligent behavior, for “reasoning” Allen AI Institute, Levesque’s Winograd test, IBM Watson …
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Contact: Luc De Raedt, Jesse Davis, Anton Dries, Hendrik Blockeel
T h r e e m a c h i n e s A , B a n d C p r
u c e 5 p e r c e n t , 3 p e r c e n t a n d 2 p e r c e n t
t h e t
a l p r
u c t i
r e s p e c t i v e l y . T h e p e r c e n t a g e
d e f e c t i v e p i e c e s i s 3 p e r c e n t , 4 p e r c e n t a n d 5 p e r c e n t r e s p e c t i v e l y . O n e c h
e s a p i e c e . I t i s d e f e c t i v e . W h a t i s t h e p r
a b i l i t y t h a t i t
i g i n a t e s f r
m a c h i n e A ?
A die is thrown 3 times. Find the probability that the sum of the dots is at least 5. M i k e h a s a b a g w i t h 4 r e d m a r b l e s a n d 3 g r e e n m a r b l e s . H e t a k e s
e m a r b l e f r
t h e b a g a n d i t i s r e d . W h a t i s t h e p r
a b i l i t y t h a t t h e s e c
d m a r b l e h e t a k e s f r
t h e b a g i s a l s
e d ? In a group of 10 people, 60 percent have brown eyes. Two people are selected from the group. What is the probability that neither of them has brown eyes? Suppose 0.1 percent of the population is infected with a certain disease. On a medical test for the disease, 98 percent of those infected give a positive result while 1 percent of those not infected give a positive
tested and gives a positive result, what is the probability the person has the disease? A g i n h a n d c
s i s t s
1 c a r d s f r
a d e c k
5 2 c a r d s , c
t a i n i n g 1 3 h e a r t s , 1 3 d i a m
d s , 1 3 c l u b s , a n d 1 3 s p a d e s . F i n d t h e p r
a b i l i t y t h a t a g i n h a n d h a s a l l 1 c a r d s
t h e s a m e s u i t .
48 Luc De Raedt
Artificial intelligence, reasoning about uncertainty, action- and activity learning, machine learning, data mining, constraint programming, probabilistic programming (ProbLog), automated data science, language for mining and learning. Applications in natural language, vision, robotics, automatic programming. Verification of AI and ML.
Hendrik Blockeel
Machine learning, data mining, probabilistic logics, declarative languages for data mining. Application domains include bio-informatics, arts, history, compiler development,
Jesse Davis
Machine learning, data mining for personalized medicine. Artificial intelligence, statistical relational learning, transfer learning Applications in healthcare (e.g., clinical practice, physical therapy, medical and biological texts, etc.). Applications to sport (e.g., football and basketball)
Bettina Berendt
Web mining, privacy, social media, user issues
Wannes Meert
Probabilistic programming and methods. Data Science Applications. Applications in
Anton Dries
Constraint programming, probabilistic programming, data mining. Automated Data Science. Design and implementation of AI systems and their applications.
49 Gerda Janssens
Performant probabilistic ILP data mining systems, integration of logic programming techniques in the knowledge representation language FO(.), program analysis and abstract interpretation, implementations of logic programs, verification of functional equivalence of C programs
Danny De Schreye
Computational creativity in Humor
Marc Denecker
Constraint programming, Knowledge Base Systems, SAT solving, declarative languages (formal modelling languages), Applications in configuration, scheduling, optimization, security, business rule systems, executable formal software specifications, logical workflow languages.
Tom Schrijvers
functional programming, constraint and logic programming, type systems, programming language theory, programming language design and implementation, program analysis
Advisable to contact promotors or daily advisors before selecting a topic Also, attend thesis info market after Easter Holidays