Content: Workshop on Hybrid Reasoning @ IJCAI 2015 - Program and Accepted Papers

Workshop on Hybrid Reasoning @ IJCAI 2015 - Program and Accepted Papers

Program

Sunday, July 26

08:45-09:00Welcome
09:00-09:30Masoumeh Mansouri, Federico Pecora and Henrik Andreasson.
Towards Hybrid Reasoning for Automated Industrial Fleet Management (Paper)
09:30-10:00Eugenia Ternovska and Shahab Tasharrofi.
Modular Systems (Paper)
10:00-10:30Mohan Sridharan, Michael Gelfond, Shiqi Zhang and Jeremy Wyatt.
Mixing Non-Monotonic Logical Reasoning and Probabilistic Planning for Robots (Paper)
10:30-11:00Coffee Break
11:00-12:00Invited Talk
Jochen Renz, Australian National University, Australia.
Hybrid Physical Reasoning: From Angry Birds to Robust AI (Abstract)
12:00-12:30Gerhard Lakemeyer
The DFG Research Unit "Hybrid Reasoning for Intelligent Systems"
12:30-14:00Lunch
14:00-14:30Lavindra de Silva and Felipe Meneguzzi.
On the Design of Symbolic-Geometric Online Planning Systems (Paper)
14:30-15:30Invited Talk
Luc De Raedt, KU Leuven, Belgium
An introduction to hybrid probabilistic (logic) programming (Abstract)
15:30-16:00Coffee Break
16:00-16:30Vaishak Belle, Andrea Passerini and Guy Van den Broeck.
Probabilistic Inference in Hybrid Domains by Weighted Model Integration (Paper)
16:30-17:00Rodrigo de Salvo Braz, Ciaran O'Reilly, Vibhav Gogate and Rina Dechter.
Probabilistic Inference Modulo Theories (Paper)
17:00-17:30Leonardo A. Ferreira, Paulo E. Santos and Reinaldo A. C. Bianchi.
Constraining a MDP's Search Space by Usage of Planning Trajectories (Paper)
19:00Dinner

Invited Talks

Hybrid Physical Reasoning: From Angry Birds to Robust AI

Jochen Renz, Australian National University, Australia.

Abstract: TBA

An introduction to hybrid probabilistic (logic) programming

Luc De Raedt, KU Leuven, Belgium

Abstract:
Recently, there has been a lot of attention for statistical relational learning and probabilistic programming, which provide rich representations for coping with uncertainty, with structure and for learning. In this talk I shall focus on probabilistic *logic* programming languages, which naturally belong to both of these paradigms as they combine the power of a programming language with a possible world semantics. They are typically based on Sato’s distribution semantics and they have been studied for over twenty years now. In this talk, I shall introduce the concepts underlying probabilistic logic programming, their semantics, different inference and learning mechanisms and I shall then present some recent extensions towards dealing with continuous distributions and dynamics. This is the framework of distributional clauses that is being applied to robotics, for tracking relational worlds in which objects or their properties are occluded in real time, and to planning. Finally, I shall discuss some open challenges and opportunities for research.

Links:
http://dtai.cs.kuleuven.be/problog/
http://dtai.cs.kuleuven.be/ml/systems/DC/

References:
[1] Nitti, D., De Laet, T., & De Raedt, L. (2013). A particle filter for hybrid relational domains. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2013.
[2] De Raedt, L., & Kimmig, A. Probabilistic (Logic) Programming Concepts, Machine Learning, in press, see arXiv:1312.4328, 2013. for a preliminary version.
[3] Gutmann, B., Thon,I., Kimmig,A., Bruynooghe,M., and De Raedt, L.: The magic of logical inference in probabilistic programming. TPLP 11(4-5): 663-680 (2011)