1,721,015 research outputs found
Learning answer set programs with aggregates via sampling and genetic programming
No full textThe goal of inductive logic programming is to learn a logic program that models the examples provided as input. The search space of the possible programs is constrained by a language bias, which defines the atoms and literals allowed in rules. Answer set programming is a powerful formalism to represent complex combinatorial domains, also thanks to syntactic constructs such as aggregates. However, learning answer set programs from data is challenging, and often existing tools do not support the specification of aggregates in the language bias. In this paper, we introduce GENTIANS, a tool based on a genetic algorithm to learn answer set programs possibly with aggregates, arithmetic, and comparison operators, from examples. Empirical results, also against an existing solver, show that GENTIANS is able to provide accurate solutions even when the search space contains millions of clauses. Additionally, experiments on noisy datasets show the effectiveness of our approach
Mixtures of probabilistic logic programs
No full textStructure learning (SL) is a fundamental task in Statistical Relational Artificial Intelligence, where the goal is to learn a program from data. Among the possible target languages, there is Probabilistic Logic Programming. Mixture models have recently gained attention thanks to their effectiveness in modeling complex distributions by combining simpler ones. In this paper, we propose learning a mixture of probabilistic logic programs to handle SL. Our method consists of three steps: 1) generating mixture components with a specific structure, 2) applying parameter learning to each component, and 3) optimizing the weights associated with each component. Furthermore, to possibly reduce the number of components and mitigate overfitting, we also explore the use of L1 and L2 regularization. Empirical results obtained by considering both the full set of components and only a fraction of them demonstrate that our approach, despite being seemingly simple, is competitive with state-of-the-art solvers
A Brief Discussion about the Credal Semantics for Probabilistic Answer Set Programs
No full textAmong the different logic-based programming languages, Answer Set Programming has emerged as an effective paradigm to solve complex combinatorial tasks. Since most of the real-world data are uncertain, several semantics have been proposed to extend Answer Set Programming to manage uncertainty, where rules are associated with a weight, or a probability, expressing a degree of belief about the truth value of certain atoms. In this paper, we focus on one of these semantics, the Credal Semantics, highlight some of the differences with other proposals, and discuss some possible future works
A Constrained Optimization Approach to Set the Parameters of Probabilistic Answer Set Programs
No full textProbabilistic Answer Set Programming under the credal semantics has emerged as one of the possible formalisms to encode uncertain domains described by an answer set program extended with probabilistic facts. Some problems require associating probability values to probabilistic facts such that the probability of a query is above a certain threshold. To solve this, we propose a new class of programs, called Probabilistic Optimizable Answer Set Programs, together with a practical algorithm based on constrained optimization to solve the task
Evolutionary learning of probabilistic logic programs
No full textLearning a logic program that effectively describes input data has been a long-standing goal in Artificial Intelligence, particularly within the field of Inductive Logic Programming. Learning it is even more challenging when information is uncertain, due to the inherent complexity of probabilistic reasoning. In this paper, we propose an approach based on an evolutionary algorithm to learn probabilistic logic programs. Our empirical evaluation shows that the proposed method outperforms existing tools in terms of log-likelihood, AUCROC, and AUCPR, while also providing more compact and interpretable theories
Fast Inference for Probabilistic Answer Set Programs Via the Residual Program
No full textWhen we want to compute the probability of a query from a probabilistic answer set program, some parts of a program may not influence the probability of a query, but they impact on the size of the grounding. Identifying and removing them is crucial to speed up the computation. Algorithms for SLG resolution offer the possibility of returning the residual program which can be used for computing answer sets for normal programs that do have a total well-founded model. The residual program does not contain the parts of the program that do not influence the probability. In this paper, we propose to exploit the residual program for performing inference. Empirical results on graph datasets show that the approach leads to significantly faster inference
Probabilistic Answer Set Programming with Discrete and Continuous Random Variables
No full textProbabilistic Answer Set Programming under the credal semantics extends Answer Set Programming with probabilistic facts that represent uncertain information. The probabilistic facts are discrete with Bernoulli distributions. However, several real-world scenarios require a combination of both discrete and continuous random variables. In this paper, we extend the PASP framework to support continuous random variables and propose Hybrid Probabilistic Answer Set Programming. Moreover, we discuss, implement, and assess the performance of two exact algorithms based on projected answer set enumeration and knowledge compilation and two approximate algorithms based on sampling. Empirical results, also in line with known theoretical results, show that exact inference is feasible only for small instances, but knowledge compilation has a huge positive impact on performance. Sampling allows handling larger instances but sometimes requires an increasing amount of memory
Optimizing Probabilities in Probabilistic Logic Programs
No full textProbabilistic logic programming is an effective formalism for encoding problems characterized by uncertainty. Some of these problems may require the optimization of probability values subject to constraints among probability distributions of random variables. Here, we introduce a new class of probabilistic logic programs, namely probabilistic optimizable logic programs, and we provide an effective algorithm to find the best assignment to probabilities of random variables, such that a set of constraints is satisfied and an objective function is optimized
Inference in Probabilistic Answer Set Programs with Imprecise Probabilities via Optimization
No full textProbabilistic answer set programming has recently been extended to manage imprecise probabilities by means of credal probabilistic facts and credal annotated disjunctions. This increases the expressivity of the language but, at the same time, the cost of inference. In this paper, we cast inference in probabilistic answer set programs with credal probabilistic facts and credal annotated disjunctions as a constrained nonlinear optimization problem where the function to optimize is obtained via knowledge compilation. Empirical results on different datasets with multiple configurations shows the effectiveness of our approach
Anastomosis formation and nuclear and protoplasmic exchange in arbuscular mycorrhizal fungi
Full text linkWe observed anastomosis between hyphae originating from the same spore and from different spores of the same isolate of the arbuscular mycorrhizal fungi Glomus mosseae, Glomus caledonium, and Glomus intraradices. The percentage of contacts leading to anastomosis ranged from 35 to 69% in hyphae from the same germling and from 34 to 90% in hyphae from different germlings. The number of anastomoses ranged from 0.6 to 1.3 per cm (length) of hyphae in mycelia originating from the same spore. No anastomoses were observed between hyphae from the same or different germlings of Gigaspora rosea and Scutellospora castanea; no interspecific or intergeneric hyphal fusions were observed. We monitored anastomosis formation with time-lapse and video- enhanced light microscopy. We observed complete fusion of hyphal walls and the migration of a mass of particles in both directions within the hyphal bridges. In hyphal bridges of G. caledonium, light-opaque particles moved at the speed of 1.8 ± 0.06 μm/s. We observed nuclear migration between hyphae of the same germling and between hyphae belonging to different germlings of the same isolate of three Glomus species. Our work suggests that genetic exchange may occur through intermingling of nuclei during anastomosis formation and opens the way to studies of vegetative compatibility in natural populations of arbuscular mycorrhizal fungi
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