ABSTRACT. Modelling languages such as ZIMPL are tremendously useful ways of
compactly writing down integer programs with large numbers of
variables and constraints. A ZIMPL statement such as "forall a in A
forall b in B: x[a] <= x[b]" expresses a (linear) constraint for
each pair (a,b). But prior to sending the problem to a solver such an
expression will be "propositionalised" into a (possibly large) set of
ground instances of the constraint. In this talk I aim to explore
whether this "grounding out" can be avoided, and whether there is any
advantage in so doing. I will be discussing cutting plane algorithms
(as a representation of sets of constraints) and column generation
algorithms (as a representation of sets of variables). My talk will
not assume any prior knowledge of integer programming.

ABSTRACT. ProPPR is a recently introduced probabilistic logic programming language inspired by stochastic logic programs that uses personalized PageRank for efficient inference. We clarify the link between these two frameworks by mapping the personalized PageRank distribution of a ProPPR program to an incomplete stochastic logic program and showing that the resulting programs induce the same probability distribution over queries.

ABSTRACT. This paper presents the recent progress concerning prefix and infix probability for PCFGs in a logic-based modeling language PRISM. A prefix is an initial substring of a sentence and likewise an infix is a substring that occurs within a sentence. The prefix probability computation is already introduced to PRISM but applications are still scarce. We describe a new application to web data that identifies visitors’ intentions, or goals visiting a website from observed sequences of their actions using prefix probability. We also discuss infix probability computation that generalizes prefix probability computation. Unlike previous approaches, we compute it through parsing followed by solving a set of non-linear equations.

ABSTRACT. Knowledge compilation algorithms transform a probabilistic logic program into a circuit representation that permits efficient probability computation. Knowledge compilation underlies algorithms for exact probabilistic inference and parameter learning in several languages, including ProbLog, PRISM, and LPADs. Developing such algorithms involves a choice, of which circuit language to target, and which compilation algorithm to use. Historically, Binary Decision Diagrams (BDDs) have been a popular target language, whereas recently, deterministic-Decomposable Negation Normal Form (d-DNNF) circuits were shown to outperform BDDs on these tasks. We investigate the use of a new language, called Sentential Decision Diagrams (SDDs), for inference in probabilistic logic programs. SDDs combine desirable properties of BDDs and d-DNNFs. Like BDDs, they support bottom-up compilation and circuit minimization, yet they are a more general and flexible representation. Our preliminary experiments show that compilation to SDD yields smaller circuits and more scalable inference, outperforming the state of the art in ProbLog inference.

ABSTRACT. A program in the Probabilistic Logic Programming language ProbLog defines a distribution over possible worlds. Adding evidence (a set of ground probabilistic atoms with observed truth values) rules out some of the possible worlds. Generalizing the evidence atoms to First Order Logic constraints increases the expressive power of ProbLog. In this paper we introduce the first implementation of cProbLog – the extension of ProbLog with constraints. Our implementation transforms cProbLog programs with FOL constraints into ProbLog programs with evidence that specify the same possible worlds. We backup our design and implementation decisions with a series of examples.

ABSTRACT. This paper is a work in progress showing our search for a modification of Probabilistic Answer Set Programming (PASP), a technique that allows modeling complex theories and checking its satisfiability with respect to a set of probabilistic data, to obtain approximated solutions.

ABSTRACT. In this paper we describe how we used the logic-based probabilistic programming language PRISM to conduct a systematic comparison of several probabilistic models of symbolic music, including zeroth and first-order Markov models over pitches and intervals, and a probabilistic grammar with two parameterisations. Using PRISM allows us to take advantage of variational Bayesian methods for assessing the goodness of fit of the models. When applied to a corpus of Bach chorales and the Essen folk song collection, we found that, depending on various parameters, the probabilistic grammars sometimes but not always out-perform the simple Markov models. Examining how the models perform on smaller subsets of pieces, we find that the simpler Markov models do out-perform the best grammar-based model at the small end of the scale.

ABSTRACT. We present an approximate query answering algorithm for the Probabilistic Logic Programming language CP-logic. It complements existing sampling algorithms by using the rules from body to head instead of in the other direction. We present an implementation in OpenCL, which is able to exploit the multicore architecture of modern GPUs to compute a large number of samples in parallel, and demonstrate that this is competitive with existing inference algorithms.

ABSTRACT. First-order model counting emerged recently as a novel reasoning task, at the core of efficient algorithms for probabilistic logics like MLNs. For certain subsets of first-order logic, lifted model counters were shown to run in time polynomial in the number of objects in the domain of discourse, where propositional model counters require exponential time. However, these guarantees apply only to Skolem normal form theories (i.e., no existential quantifiers). Since textbook Skolemization is not sound for model counting, these restrictions precluded efficient model counting for directed models, such as probabilistic logic programs, which rely on existential quantification. Recently, we presented a novel Skolemization algorithm for model counting problems that eliminates existential quantifiers from a first-order logic theory without changing its weighted model count. Our Skolemization procedure extends the applicability of first-order model counters to probabilistic logic programming.

ABSTRACT. The third round of the Kurt Gödel Research Prize Fellowships Program, under the title: Connecting Foundations and Technology, aims at supporting young scholars in early stages of their academic careers by offering highest fellowships in history of logic, kindly supported by the John Templeton Foundation. Young scholars being less or exactly 40 years old at the time of the commencement of the Vienna Summer of Logic (July 9, 2014) will be awarded one fellowship award in the amount of EUR 100,000, in each of the following categories:

• Logical Foundations of Mathematics,
• Logical Foundations of Computer Science, and
• Logical Foundations of Artificial Intelligence

The following three Boards of Jurors were in charge of choosing the winners:

• Logical Foundations of Mathematics: Jan Krajíček, Angus Macintyre, and Dana Scott (Chair).
• Logical Foundations of Computer Science: Franz Baader, Johann Makowsky, and Wolfgang Thomas (Chair).
• Logical Foundations of Artificial Intelligence: Luigia Carlucci Aiello, Georg Gottlob (Chair), and Bernhard Nebel.

http://fellowship.logic.at/

ABSTRACT. The aim of the FLoC Olympic Games is to start a tradition in the spirit of the ancient Olympic Games, a Panhellenic sport festival held every four years in the sanctuary of Olympia in Greece, this time in the scientific community of computational logic. Every four years, as part of the Federated Logic Conference, the Games will gather together all the challenging disciplines from a variety of computational logic in the form of the solver competitions.

At the Award Ceremonies, the competition organizers will have the opportunity to present their competitions to the public and give away special prizes, the prestigious Kurt Gödel medals, to their successful competitors. This reinforces the main goal of the FLoC Olympic Games, that is, to facilitate the visibility of the competitions associated with the conferences and workshops of the Federated Logic Conference during the Vienna Summer of Logic.

This award ceremony will host the

• 3rd Confluence Competition (CoCo 2014);
• Configurable SAT Solver Challenge (CSSC 2014);
• Ninth Max-SAT Evaluation (Max-SAT 2014);
• QBF Gallery 2014; and
• SAT Competition 2014 (SAT-COMP 2014).

ABSTRACT. This talk will discuss the recent results achieved in inference and learning for probabilistic logic programs under the distribution semantics  and will propose directions for future work.