TY  - JOUR
A1  - Thon, Ingo
A1  - Landwehr, Niels
A1  - De Raedt, Luc
T1  - Stochastic relational processes efficient inference and applications
JF  - Machine learning
N2  - One of the goals of artificial intelligence is to develop agents that learn and act in complex environments. Realistic environments typically feature a variable number of objects, relations amongst them, and non-deterministic transition behavior. While standard probabilistic sequence models provide efficient inference and learning techniques for sequential data, they typically cannot fully capture the relational complexity. On the other hand, statistical relational learning techniques are often too inefficient to cope with complex sequential data. In this paper, we introduce a simple model that occupies an intermediate position in this expressiveness/efficiency trade-off. It is based on CP-logic (Causal Probabilistic Logic), an expressive probabilistic logic for modeling causality. However, by specializing CP-logic to represent a probability distribution over sequences of relational state descriptions and employing a Markov assumption, inference and learning become more tractable and effective. Specifically, we show how to solve part of the inference and learning problems directly at the first-order level, while transforming the remaining part into the problem of computing all satisfying assignments for a Boolean formula in a binary decision diagram. We experimentally validate that the resulting technique is able to handle probabilistic relational domains with a substantial number of objects and relations.
KW  - Statistical relational learning
KW  - Stochastic relational process
KW  - Markov processes
KW  - Time series
KW  - CP-Logic
Y1  - 2011
U6  - https://doi.org/10.1007/s10994-010-5213-8
SN  - 0885-6125
VL  - 82
IS  - 2
SP  - 239
EP  - 272
PB  - Springer
CY  - Dordrecht
ER  - 
TY  - JOUR
A1  - Cilia, Elisa
A1  - Landwehr, Niels
A1  - Passerini, Andrea
T1  - Relational feature mining with hierarchical multitask kFOIL
JF  - Fundamenta informaticae
N2  - We introduce hierarchical kFOIL as a simple extension of the multitask kFOIL learning algorithm. The algorithm first learns a core logic representation common to all tasks, and then refines it by specialization on a per-task basis. The approach can be easily generalized to a deeper hierarchy of tasks. A task clustering algorithm is also proposed in order to automatically generate the task hierarchy. The approach is validated on problems of drug-resistance mutation prediction and protein structural classification. Experimental results show the advantage of the hierarchical version over both single and multi task alternatives and its potential usefulness in providing explanatory features for the domain. Task clustering allows to further improve performance when a deeper hierarchy is considered.
Y1  - 2011
U6  - https://doi.org/10.3233/FI-2011-604
SN  - 0169-2968
VL  - 113
IS  - 2
SP  - 151
EP  - 177
PB  - IOS Press
CY  - Amsterdam
ER  -