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International Conference on Autonomous Agents and Multiagent Systems

作者： Kevin McAreavey Kim Bauters Weiru Liu Jun Hong Queen's University Belfast University of Bristol University of the West of England

ISBN: (纸本)9781510855076

We propose a new probabilistic extension to the event calculus using the probabilistic logic programming (PLP) language ProbLog, and a language construct called the annotated disjunction. This is the first extension of the event calculus capable of handling numerous sources of uncertainty (e.g. from primitive event observations and from composite event definitions). It is also the first extension capable of handling multiple sources of event observations (e.g. in multi-sensor environments). We describe characteristics of this new extension (e.g. rationality of conclusions), and prove some important properties (e.g. validity in ProbLog). Our extension is directly implementable in ProbLog, and we successfully apply it to the problem of activity recognition under uncertainty in an event detection data set obtained from vision analytics of bus surveillance video.

关键词： The event calculus Event reasoning probabilistic logic programming ProbLog Annotated disjunction Temporal logic probabilistic logic Activity recognition programming rationality

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Personalized Recommendations using Knowledge Graphs: A probabilistic logic programming Approach 10

Personalized Recommendations using Knowledge Graphs: A Proba...

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10th ACM Conference on Recommender Systems (RecSys)

作者： Catherine, Rose Cohen, William Carnegie Mellon Univ Language Technol Inst Pittsburgh PA 15213 USA Carnegie Mellon Univ Machine Learning Dept Pittsburgh PA 15213 USA

ISBN: (纸本)9781450340359

Improving the performance of recommender systems using knowledge graphs is an important task. There have been many hybrid systems proposed in the past that use a mix of content-based and collaborative filtering techniques to boost the performance. More recently, some work has focused on recommendations that use external knowledge graphs (KGs) to supplement content-based recommendation. In this paper, we investigate three methods for making KG based recommendations using a general-purpose probabilistic logic system called ProPPR. The simplest of the models, EntitySim, uses only the links of the graph. We then extend the model to TypeSim that also uses the types of the entities to boost its generalization capabilities. Next, we develop a graph based latent factor model, GraphLF, which combines the strengths of latent factorization with graphs. We compare our approaches to a recently proposed state-of-the-art graph recommendation method on two large datasets, Yelp and MovieLens-100K. The experiments illustrate that our approaches can give large performance improvements. Additionally, we demonstrate that knowledge graphs give maximum advantage when the dataset is sparse, and gradually become redundant as more training data becomes available, and hence are most useful in cold-start settings.

关键词： Knowledge Graph based Recommendations probabilistic logic programming Graph based Matrix Factorization

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Markov Decision Processes Specified by probabilistic logic programming: Representation and Solution 5

Markov Decision Processes Specified by Probabilistic Logic P...

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5th Brazilian Conference on Intelligent Systems (BRACIS)

作者： Bueno, Thiago P. Maua, Denis D. de Barros, Leliane N. Cozman, Fabio G. Univ Sao Paulo Inst Matemat & Estat Rua Matao 1010 Sao Paulo SP Brazil Univ Sao Paulo Escola Politecn Av Prof Mello Moraes 2231 Sao Paulo SP Brazil

ISBN: (纸本)9781509035663

probabilistic logic programming combines logic and probability, so as to obtain a rich modeling language. In this work, we extend PROBLOG, a popular probabilistic logic programming language, with new constructs that allow the representation of (infinite-horizon) Markov decision processes. This new language can represent relational statements, including symmetric and transitive definitions, an advantage over other planning domain languages such as RDDL. We show how to exploit the logic structure in the language to perform Value Iteration. Preliminary experiments demonstrate the effectiveness of our framework.

关键词： probabilistic planning Markov decision process sequential decision making probabilistic logic programming

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A new probabilistic constraint logic programming language based on a generalised distribution semantics

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ARTIFICIAL INTELLIGENCE 2015年 228卷 1-44页

作者： Michels, Steffen Hommersom, Arjen Lucas, Peter J. F. Velikova, Marina Radboud Univ Nijmegen Inst Comp & Informat Sci NL-6525 ED Nijmegen Netherlands TNO Embedded Syst Innovat The Hague Netherlands

probabilistic logics combine the expressive power of logic with the ability to reason with uncertainty. Several probabilistic logic languages have been proposed in the past, each of them with their own features. We focus on a class of probabilistic logic based on Sato's distribution semantics, which extends logic programming with probability distributions on binary random variables and guarantees a unique probability distribution. For many applications binary random variables are, however, not sufficient and one requires random variables with arbitrary ranges, e.g. real numbers. We tackle this problem by developing a generalised distribution semantics for a new probabilistic constraint logic programming language. In order to perform exact inference, imprecise probabilities are taken as a starting point, i.e. we deal with sets of probability distributions rather than a single one. It is shown that given any continuous distribution, conditional probabilities of events can be approximated arbitrarily close to the true probability. Furthermore, for this setting an inference algorithm that is a generalisation of weighted model counting is developed, making use of SMT solvers. We show that inference has similar complexity properties as precise probabilistic inference, unlike most imprecise methods for which inference is more complex. We also experimentally confirm that our algorithm is able to exploit local structure, such as determinism, which further reduces the computational complexity. (C) 2015 Elsevier B.V. All rights reserved.

关键词： probabilistic logic programming Imprecise probabilities Continuous probability distributions Exact probabilistic inference

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probabilistic (logic) programming concepts

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MACHINE LEARNING 2015年第1期100卷 5-47页

作者： De Raedt, Luc Kimmig, Angelika Katholieke Univ Leuven Dept Comp Sci B-3001 Heverlee Belgium

A multitude of different probabilistic programming languages exists today, all extending a traditional programming language with primitives to support modeling of complex, structured probability distributions. Each of these languages employs its own probabilistic primitives, and comes with a particular syntax, semantics and inference procedure. This makes it hard to understand the underlying programming concepts and appreciate the differences between the different languages. To obtain a better understanding of probabilistic programming, we identify a number of core programming concepts underlying the primitives used by various probabilistic languages, discuss the execution mechanisms that they require and use these to position and survey state-of-the-art probabilistic languages and their implementation. While doing so, we focus on probabilistic extensions of logic programming languages such as Prolog, which have been considered for over 20 years.

关键词： probabilistic programming languages probabilistic logic programming Statistical relational learning Inference in probabilistic languages

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Lifted Variable Elimination for probabilistic logic programming

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THEORY AND PRACTICE OF logic programming 2014年第4-5期14卷 681-695页

作者： Bellodi, Elena Lamma, Evelina Riguzzi, Fabrizio Costa, Vitor Santos Zese, Riccardo Univ Ferrara Dipartimento Ingn I-44122 Ferrara Italy Univ Ferrara Dipartimento Matemat & Informat I-44122 Ferrara Italy Univ Porto CRACS INESC TEC P-4169007 Oporto Portugal Univ Porto DCC FCUP P-4169007 Oporto Portugal

Lifted inference has been proposed for various probabilistic logical frameworks in order to compute the probability of queries in a time that depends on the size of the domains of the random variables rather than the number of instances. Even if various authors have underlined its importance for probabilistic logic programming (PLP), lifted inference has been applied up to now only to relational languages outside of logic programming. In this paper we adapt Generalized Counting First Order Variable Elimination (GC-FOVE) to the problem of computing the probability of queries to probabilistic logic programs under the distribution semantics. In particular, we extend the Prolog Factor Language (PFL) to include two new types of factors that are needed for representing ProbLog programs. These factors take into account the existing causal independence relationships among random variables and are managed by the extension to variable elimination proposed by Zhang and Poole for dealing with convergent variables and heterogeneous factors. Two new operators are added to GC-FOVE for treating heterogeneous factors. The resulting algorithm, called LP2 for Lifted probabilistic logic programming, has been implemented by modifying the PFL implementation of GC-FOVE and tested on three benchmarks for lifted inference. A comparison with PITA and ProbLog2 shows the potential of the approach.

关键词： probabilistic logic programming Lifted Inference Variable Elimination Distribution Semantics ProbLog Statistical Relational Artificial Intelligence

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Semirings for probabilistic and neuro-symbolic logic programming

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2024年 171卷

作者： Derkinderen, Vincent Manhaeve, Robin Dos Martires, Pedro Zuidberg De Raedt, Luc Katholieke Univ Leuven Dept Comp Sci DTAI B-3000 Leuven Belgium Katholieke Univ Leuven Leuven AI Inst AI Leuven Belgium Orebro Univ Ctr Appl Autonomous Syst SE-70182 Orebro Sweden

The field of probabilistic logic programming (PLP) focuses on integrating probabilistic models into programming languages based on logic. Over the past 30 years, numerous languages and frameworks have been developed for modeling, inference and learning in probabilistic logic programs. While originally PLP focused on discrete probability, more recent approaches have incorporated continuous distributions as well as neural networks, effectively yielding neurosymbolic methods. We provide an overview and synthesis of this domain, thereby contributing a unified algebraic perspective on the different flavors of PLP, showing that many if not most of the extensions of PLP can be cast within a common algebraic logic programming framework, in which facts are labeled with elements of a semiring and disjunction and conjunction are replaced by addition and multiplication. This does not only hold for the PLP variations itself but also for the underlying execution mechanism that is based on (algebraic) model counting. In order to showcase and explain this unified perspective, we focus on the ProbLog language and its extensions.

关键词： probabilistic logic programming Neuro-symbolic AI Semiring programming Model counting

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Lifted Variable Elimination for probabilistic logic programming

Lifted Variable Elimination for Probabilistic Logic Programm...

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30th International Conference on logic programming

关键词： probabilistic logic programming Lifted Inference Variable Elimination Distribution Semantics ProbLog Statistical Relational Artificial Intelligence

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MCINTYRE: A Monte Carlo System for probabilistic logic programming

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FUNDAMENTA INFORMATICAE 2013年第4期124卷 521-541页

作者： Riguzzi, Fabrizio Univ Ferrara Dipartimento Matemat & Informat I-44122 Ferrara Italy

probabilistic logic programming is receiving an increasing attention for its ability to model domains with complex and uncertain relations among entities. In this paper we concentrate on the problem of approximate inference in probabilistic logic programming languages based on the distribution semantics. A successful approximate approach is based on Monte Carlo sampling, that consists in verifying the truth of the query in a normal program sampled from the probabilistic program. The ProbLog system includes such an algorithm and so does the cplint suite. In this paper we propose an approach for Monte Carlo inference that is based on a program transformation that translates a probabilistic program into a normal program to which the query can be posed. The current sample is stored in the internal database of the Yap Prolog engine. The resulting system, called MCINTYRE for Monte Carlo INference wiTh Yap REcord, is evaluated on various problems: biological networks, artificial datasets and a hidden Markov model. MCINTYRE is compared with the Monte Carlo algorithms of ProbLog and cplint and with the exact inference of the PITA system. The results show that MCINTYRE is faster than the other Monte Carlo systems.

关键词： probabilistic logic programming Monte Carlo Methods logic Programs with Annotated Disjunctions ProbLog

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Well-definedness and efficient inference for probabilistic logic programming under the distribution semantics

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THEORY AND PRACTICE OF logic programming 2013年第02期13卷 279-302页

作者： Riguzzi, Fabrizio Swift, Terrance Univ Ferrara ENDIF I-44122 Ferrara Italy Univ Nova Lisboa CENTRIA Caparica Portugal

Distribution semantics is one of the most prominent approaches for the combination of logic programming and probability theory. Many languages follow this semantics, such as Independent Choice logic, PRISM, pD, logic Programs with Annotated Disjunctions (LPADs), and ProbLog. When a program contains functions symbols, the distribution semantics is well-defined only if the set of explanations for a query is finite and so is each explanation. Well-definedness is usually either explicitly imposed or is achieved by severely limiting the class of allowed programs. In this paper, we identify a larger class of programs for which the semantics is well-defined together with an efficient procedure for computing the probability of queries. Since logic Programs with Annotated Disjunctions offer the most general syntax, we present our results for them, but our results are applicable to all languages under the distribution semantics. We present the algorithm "probabilistic Inference with Tabling and Answer subsumption" (PITA) that computes the probability of queries by transforming a probabilistic program into a normal program and then applying SLG resolution with answer subsumption. PITA has been implemented in XSB and tested on six domains: two with function symbols and four without. The execution times are compared with those of ProbLog, cplint, and CVE. PITA was almost always able to solve larger problems in a shorter time, on domains with and without function symbols.

关键词： probabilistic logic programming Tabling Answer subsumption logic programs with annotated disjunction Program transformation

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