Conference Program

Schedule of Workshop & Co-located Events

Sat 19th AM Sat 19th PM Sun 20th AM Sun 20th PM Mon 21st AM Mon 21st PM
WS38 WS38 WS15 WS15 WS2 WS2
WS1 WS1 WS1 WS33 WS33
WS6 WS6 WS6 WS21 WS21
WS26 WS26 WS20 WS20
WS7 WS7 DC DC
WS10 WS10 WS22 WS22
WS11 WS11 WS12 WS12 WS19 WS19
WS14 WS14 WS8 WS8
WS16 WS16 WS41 WS41 IFIP AIAI IFIP AIAI
WS24 WS24 WS23 WS23 WS37 WS37
WS25 WS25 WS17 WS17
WS28 WS28 WS27 WS27 WS13 WS13
WS40 WS40 WS9 WS9
WS29 WS29 WS34 WS34
WS30 WS30 WS35 WS35
WS31 WS31 WS36 WS36
WS32 WS32 WS39 WS39
WS3 WS3 WS5 WS5
WS18 WS18
IFIP AI4KM IFIP AI4KM
KSEM KSEM KSEM KSEM
AusDM AusDM AusDM AusDM
Aus AI Aus AI Aus AI Aus AI

Tutorial Schedule

Saturday
Room A
SaA1 (0.5) IoT Big Data Stream Mining
SaA2 (0.5) Data Mining and Machine Learning using Constraint Programming Languages
Room B
SaB1 (0.5) Multiagent Learning: Foundations and Recent Trends
SaB2 (0.5) Theory and practice of revenue optimal mechanism design
Room C
SaC1 (0.5) Computational Models for Social Influence and Diffusion
SaC2 (0.5) Machine learning for dynamic social network analysis
Sunday
Room A
SuA1 (0.5) Interactive Machine Learning: From Classifiers to Robotics
SuA2 (0.5) Deep Reinforcement Learning Through Policy Optimization
Room B
SuB1 (0.5) Multiwinner Elections: Applications, Axioms, and Algorithms
SuB2 (0.25) Strategic Voting and AI
SuB3 (0.25) Strategic Voting and Strategic Candidacy in Multi-Agent Systems
Room C
SuC1 (0.5) Declarative Spatial Reasoning: Theory, Methods, and Applications
SuC2 (0.25) Unifying Logic, Dynamics and Probability: Foundations, Algorithms and Challenges
SuC3 (0.25) First-Order Multi-agent Logics in Action
Monday
Room A
MA1 (0.5) Programming by Optimization: A Practical Paradigm for Computer-Aided Algorithm Design
MA2 (0.25) Theoretical Analysis of Policy Iteration
MA3 (0.25) Heterogeneous Learning: Recent Advance and Future Studies
Room B
MB1 (0.5) Argumentation in Artificial Intelligence: From Theory to Practice
SuB2 (0.5) Acquisition, Representation and Usage of Conceptual Hierarchies
Room C
MC1 (0.5) Markov Logic Networks: Recent Advances and Practical Applications
MC2 (0.5) Energy-based machine learning
Room D
MD1 (0.5) Learning and Decision-Making from Rank Data