Zobrazeno 1 - 10
of 106
pro vyhledávání: '"Kimmig, Angelika"'
Argumentation problems are concerned with determining the acceptability of a set of arguments from their relational structure. When the available information is uncertain, probabilistic argumentation frameworks provide modelling tools to account for
Externí odkaz:
http://arxiv.org/abs/2304.00879
Autor:
De Smet, Lennert, Martires, Pedro Zuidberg Dos, Manhaeve, Robin, Marra, Giuseppe, Kimmig, Angelika, De Raedt, Luc
Neural-symbolic AI (NeSy) allows neural networks to exploit symbolic background knowledge in the form of logic. It has been shown to aid learning in the limited data regime and to facilitate inference on out-of-distribution data. Probabilistic NeSy f
Externí odkaz:
http://arxiv.org/abs/2303.04660
Over the past three decades, the logic programming paradigm has been successfully expanded to support probabilistic modeling, inference and learning. The resulting paradigm of probabilistic logic programming (PLP) and its programming languages owes m
Externí odkaz:
http://arxiv.org/abs/2302.10674
Quantitative extensions of logic programming often require the solution of so called second level inference tasks, i.e., problems that involve a third operation, such as maximization or normalization, on top of addition and multiplication, and thus g
Externí odkaz:
http://arxiv.org/abs/2205.07496
Autor:
Vilamala, Marc Roig, Xing, Tianwei, Taylor, Harrison, Garcia, Luis, Srivastava, Mani, Kaplan, Lance, Preece, Alun, Kimmig, Angelika, Cerutti, Federico
In this paper, we present an approach to Complex Event Processing (CEP) that is based on DeepProbLog. This approach has the following objectives: (i) allowing the use of subsymbolic data as an input, (ii) retaining the flexibility and modularity on t
Externí odkaz:
http://arxiv.org/abs/2110.08090
We introduce SMProbLog, a generalization of the probabilistic logic programming language ProbLog. A ProbLog program defines a distribution over logic programs by specifying for each clause the probability that it belongs to a randomly sampled program
Externí odkaz:
http://arxiv.org/abs/2110.01990
When collaborating with an AI system, we need to assess when to trust its recommendations. If we mistakenly trust it in regions where it is likely to err, catastrophic failures may occur, hence the need for Bayesian approaches for probabilistic reaso
Externí odkaz:
http://arxiv.org/abs/2102.10865
Autor:
Ricca, Francesco, Russo, Alessandra, Greco, Sergio, Leone, Nicola, Artikis, Alexander, Friedrich, Gerhard, Fodor, Paul, Kimmig, Angelika, Lisi, Francesca, Maratea, Marco, Mileo, Alessandra, Riguzzi, Fabrizio
Publikováno v:
EPTCS 325, 2020
Since the first conference held in Marseille in 1982, ICLP has been the premier international event for presenting research in logic programming. Contributions are solicited in all areas of logic programming and related areas, including but not restr
Externí odkaz:
http://arxiv.org/abs/2009.09158
Autor:
Vilamala, Marc Roig, Taylor, Harrison, Xing, Tianwei, Garcia, Luis, Srivastava, Mani, Kaplan, Lance, Preece, Alun, Kimmig, Angelika, Cerutti, Federico
Training a model to detect patterns of interrelated events that form situations of interest can be a complex problem: such situations tend to be uncommon, and only sparse data is available. We propose a hybrid neuro-symbolic architecture based on Eve
Externí odkaz:
http://arxiv.org/abs/2009.03420
Automated negotiation has been used in a variety of distributed settings, such as privacy in the Internet of Things (IoT) devices and power distribution in Smart Grids. The most common protocol under which these agents negotiate is the Alternating Of
Externí odkaz:
http://arxiv.org/abs/2003.13668