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- ArticleJune 2024
Through the Eyes of the Expert: Aligning Human and Machine Attention for Industrial AI
AbstractHuman expertise and intuition are crucial in solving many tasks in expert-driven domains such as industrial manufacturing or medical diagnosis. In this work, we use the human expert’s gaze information to take a step towards transferring this ...
- ArticleMarch 2023
Grasping Partially Occluded Objects Using Autoencoder-Based Point Cloud Inpainting
Machine Learning and Knowledge Discovery in DatabasesSep 2022, Pages 219–235https://doi.org/10.1007/978-3-031-26422-1_14AbstractFlexible industrial production systems will play a central role in the future of manufacturing due to higher product individualization and customization. A key component in such systems is the robotic grasping of known or unknown objects in random ...
- ArticleJune 2019
A Recommender System for Complex Real-World Applications with Nonlinear Dependencies and Knowledge Graph Context
- Marcel Hildebrandt,
- Swathi Shyam Sunder,
- Serghei Mogoreanu,
- Mitchell Joblin,
- Akhil Mehta,
- Ingo Thon,
- Volker Tresp
AbstractMost latent feature methods for recommender systems learn to encode user preferences and item characteristics based on past user-item interactions. While such approaches work well for standalone items (e.g., books, movies), they are not as well ...
- ArticleJanuary 2019
Configuration of Industrial Automation Solutions Using Multi-relational Recommender Systems
Machine Learning and Knowledge Discovery in DatabasesSep 2018, Pages 271–287https://doi.org/10.1007/978-3-030-10997-4_17AbstractBuilding complex automation solutions, common to process industries and building automation, requires the selection of components early on in the engineering process. Typically, recommender systems guide the user in the selection of appropriate ...
- ArticleJuly 2015
Inducing probabilistic relational rules from probabilistic examples
IJCAI'15: Proceedings of the 24th International Conference on Artificial IntelligenceJuly 2015, Pages 1835–1843We study the problem of inducing logic programs in a probabilistic setting, in which both the example descriptions and their classification can be probabilistic. The setting is incorporated in the probabilistic rule learner ProbFOIL+, which combines ...
- ArticleJuly 2013
MCMC estimation of conditional probabilities in probabilistic programming languages
ECSQARU'13: Proceedings of the 12th European conference on Symbolic and Quantitative Approaches to Reasoning with UncertaintyJuly 2013, Pages 436–448https://doi.org/10.1007/978-3-642-39091-3_37Probabilistic logic programming languages are powerful formalisms that can model complex problems where it is necessary to represent both structure and uncertainty. Using exact inference methods to compute conditional probabilities in these languages is ...
- ArticleSeptember 2011
Learning the parameters of probabilistic logic programs from interpretations
ECML PKDD'11: Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part ISeptember 2011, Pages 581–596ProbLog is a recently introduced probabilistic extension of the logic programming language Prolog, in which facts can be annotated with the probability that they hold. The advantage of this probabilistic language is that it naturally expresses a ...
- ArticleSeptember 2011
Learning the parameters of probabilistic logic programs from interpretations
ECMLPKDD'11: Proceedings of the 2011th European Conference on Machine Learning and Knowledge Discovery in Databases - Volume Part ISeptember 2011, Pages 581–596https://doi.org/10.1007/978-3-642-23780-5_47ProbLog is a recently introduced probabilistic extension of the logic programming language Prolog, in which facts can be annotated with the probability that they hold. The advantage of this probabilistic language is that it naturally expresses a ...
- ArticleJuly 2011
Inference in probabilistic logic programs using weighted CNF's
UAI'11: Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial IntelligenceJuly 2011, Pages 211–220Probabilistic logic programs are logic programs in which some of the facts are annotated with probabilities. Several classical probabilistic inference tasks (such as MAP and computing marginals) have not yet received a lot of attention for this ...
- articleFebruary 2011
Stochastic relational processes: Efficient inference and applications
Machine Language (MALE), Volume 82, Issue 2February 2011, Pages 239–272https://doi.org/10.1007/s10994-010-5213-8One 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 ...
- ArticleJuly 2010
DTPROBLOG: a decision-theoretic probabilistic prolog
AAAI'10: Proceedings of the Twenty-Fourth AAAI Conference on Artificial IntelligenceJuly 2010, Pages 1217–1222We introduce DTPROBLOG, a decision-theoretic extension of Prolog and its probabilistic variant ProbLog. DT-PROBLOG is a simple but expressive probabilistic programming language that allows the modeling of a wide variety of domains, such as viral ...
- ArticleJune 2010
Probabilistic rule learning
ILP'10: Proceedings of the 20th international conference on Inductive logic programmingJune 2010, Pages 47–58Traditionally, rule learners have learned deterministic rules from deterministic data, that is, the rules have been expressed as logical statements and also the examples and their classification have been purely logical. We upgrade rule learning to a ...
- ArticleJanuary 2010
Probabilistic programming for planning problems
AAAIWS'10-06: Proceedings of the 6th AAAI Conference on Statistical Relational Artificial IntelligenceJanuary 2010, Pages 98–99Probabilistic programing is an emerging field at the intersection of statistical learning and programming languages. An appealing property of probabilistic programming languages (PPL) is their support for constructing arbitrary probability ...
- ArticleJuly 2009
Don't fear optimality: sampling for probabilistic-logic sequence models
ILP'09: Proceedings of the 19th international conference on Inductive logic programmingJuly 2009, Pages 226–233One of the current challenges in artificial intelligence is modeling dynamic environments that change due to the actions or activities undertaken by people or agents. The task of inferring hidden states, e.g. the activities or intentions of people, ...
- articleJanuary 2009
Relational Transformation-based Tagging for Activity Recognition
Fundamenta Informaticae (FUNI), Volume 89, Issue 1January 2009, Pages 111–129The ability to recognize human activities from sensory information is essential for developing the next generation of smart devices. Many human activity recognition tasks are - from a machine learning perspective - quite similar to tagging tasks in ...
- ArticleSeptember 2008
A simple model for sequences of relational state descriptions
ECMLPKDD'08: Proceedings of the 2008th European Conference on Machine Learning and Knowledge Discovery in Databases - Volume Part IISeptember 2008, Pages 506–521Artificial intelligence aims at developing 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. Standard ...
- articleJanuary 2008
Relational Transformation-based Tagging for Activity Recognition
Fundamenta Informaticae (FUNI), Volume 89, Issue 1January 2008, Pages 111–129The ability to recognize human activities from sensory information is essential for developing the next generation of smart devices. Many human activity recognition tasks are - from a machine learning perspective - quite similar to tagging tasks in ...