Constraint Propagation for Efficient Inference in Markov Logic

Papai, Tivadar; Singla, Parag; Kautz, Henry

doi:10.1007/978-3-642-23786-7_52

Cited by 6 publications

(4 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, the soft constraints S are grounded much more efficiently by taking frozen atoms into account. Our approach may also be seen as an extension of a proposal by Papai et al (2011).…”

Section: Boosting Inference Efficiencymentioning

confidence: 97%

Exploring Markov Logic Networks for Question Answering

Khot¹,

Balasubramanian²,

Gribkoff³

et al. 2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

View full text Add to dashboard Cite

Elementary-level science exams pose significant knowledge acquisition and reasoning challenges for automatic question answering. We develop a system that reasons with knowledge derived from textbooks, represented in a subset of firstorder logic. Automatic extraction, while scalable, often results in knowledge that is incomplete and noisy, motivating use of reasoning mechanisms that handle uncertainty. Markov Logic Networks (MLNs) seem a natural model for expressing such knowledge, but the exact way of leveraging MLNs is by no means obvious. We investigate three ways of applying MLNs to our task. First, we simply use the extracted science rules directly as MLN clauses and exploit the structure present in hard constraints to improve tractability. Second, we interpret science rules as describing prototypical entities, resulting in a drastically simplified but brittle network. Our third approach, called Praline, uses MLNs to align lexical elements as well as define and control how inference should be performed in this task. Praline demonstrates a 15% accuracy boost and a 10x reduction in runtime as compared to other MLNbased methods, and comparable accuracy to word-based baseline approaches.

show abstract

“…Finally, the soft constraints S are grounded much more efficiently by taking frozen atoms into account. Our approach may also be seen as an extension of a proposal by Papai et al (2011).…”

Section: Boosting Inference Efficiencymentioning

confidence: 97%

Exploring Markov Logic Networks for Question Answering

Khot¹,

Balasubramanian²,

Gribkoff³

et al. 2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

View full text Add to dashboard Cite

show abstract

“…Lifted LBP Another promising research area that has been recently explored seeks to improve the scalability of LBP on models that feature large networks. Here, mainstream work attempts to exploit some structural properties in the network like symmetry (Ahmadi et al 2013), determinism (Papai et al 2011;Ibrahim et al 2015), sparseness (Poon et al 2008), and type hierarchy (Kiddon and Domingos 2011) to scale LBP inference. For instance, Lifted Inference either directly operates on the first-order structure or uses the symmetry present in the structure of the network to reduce its size (e.g., Ahmadi et al 2013).…”

Section: Related Workmentioning

confidence: 99%

Improving probabilistic inference in graphical models with determinism and cycles

2016

View full text Add to dashboard Cite

Many important real-world applications of machine learning, statistical physics, constraint programming and information theory can be formulated using graphical models that involve determinism and cycles. Accurate and efficient inference and training of such graphical models remains a key challenge. Markov logic networks (MLNs) have recently emerged as a popular framework for expressing a number of problems which exhibit these properties. While loopy belief propagation (LBP) can be an effective solution in some cases; unfortunately, when both determinism and cycles are present, LBP frequently fails to converge or converges to inaccurate results. As such, sampling based algorithms have been found to be more effective and are more popular for general inference tasks in MLNs. In this paper, we introduce Generalized arc-consistency Expectation Maximization MessagePassing (GEM-MP), a novel message-passing approach to inference in an extended factor graph that combines constraint programming techniques with variational methods. We focus our experiments on Markov logic and Ising models but the method is applicable to graphical models in general. In contrast to LBP, GEM-MP formulates the message-passing structure as steps of variational expectation maximization. Moreover, in the algorithm we leverage the local structures in the factor graph by using generalized arc consistency when performing a variational mean-field approximation. Thus each such update increases a lower bound on the model evidence. Our experiments on Ising grids, entity resolution and link prediction problems demonstrate the accuracy and convergence of GEM-MP over existing state-of-theart inference algorithms such as MC-SAT, LBP, and Gibbs sampling, as well as convergent Editor:

show abstract

“…Current Markov logic solvers take advantage of the underlying logical structure to perform more powerful optimizations, such as Alchemy's lifted inference in belief propagation and MC-SAT (Poon & Domingos, 2006). Additionally, domain pruning, where one uses hard constraints to infer reduced domains for predicates, has been shown to lead to significant speed-ups (Papai, Singla, & Kautz, 2011).…”

Section: Related Workmentioning

confidence: 99%

Location-Based Reasoning about Complex Multi-Agent Behavior

Sadilek

Kautz

2012

jair

View full text Add to dashboard Cite

Recent research has shown that surprisingly rich models of human activity can be learned from GPS (positional) data. However, most effort to date has concentrated on modeling single individuals or statistical properties of groups of people. Moreover, prior work focused solely on modeling actual successful executions (and not failed or attempted executions) of the activities of interest. We, in contrast, take on the task of understanding human interactions, attempted interactions, and intentions from noisy sensor data in a fully relational multi-agent setting. We use a real-world game of capture the flag to illustrate our approach in a well-defined domain that involves many distinct cooperative and competitive joint activities. We model the domain using Markov logic, a statistical-relational language, and learn a theory that jointly denoises the data and infers occurrences of high-level activities, such as a player capturing an enemy. Our unified model combines constraints imposed by the geometry of the game area, the motion model of the players, and by the rules and dynamics of the game in a probabilistically and logically sound fashion. We show that while it may be impossible to directly detect a multi-agent activity due to sensor noise or malfunction, the occurrence of the activity can still be inferred by considering both its impact on the future behaviors of the people involved as well as the events that could have preceded it. Further, we show that given a model of successfully performed multi-agent activities, along with a set of examples of failed attempts at the same activities, our system automatically learns an augmented model that is capable of recognizing success and failure, as well as goals of people's actions with high accuracy. We compare our approach with other alternatives and show that our unified model, which takes into account not only relationships among individual players, but also relationships among activities over the entire length of a game, although more computationally costly, is significantly more accurate. Finally, we demonstrate that explicitly modeling unsuccessful attempts boosts performance on other important recognition tasks.

show abstract

Constraint Propagation for Efficient Inference in Markov Logic

Cited by 6 publications

References 4 publications

Exploring Markov Logic Networks for Question Answering

Exploring Markov Logic Networks for Question Answering

Improving probabilistic inference in graphical models with determinism and cycles

Location-Based Reasoning about Complex Multi-Agent Behavior

Contact Info

Product

Resources

About