Search · four archives
Search · four archives
21 papers · ranked by Valyu relevance
Vaishak Belle
Probabilistic planning attempts to incorporate stochastic models directly into the planning process, which is the problem of synthesizing a sequence of actions that achieves some objective for a putative agent. Probabilistic programming has rapidly emerged as a key paradigm to integrate probabilistic concepts with…
Germán Vidal
The generation of comprehensible explanations is an essential feature of modern artificial intelligence systems. In this work, we consider probabilistic logic programming, an extension of logic programming which can be useful to model domains with relational structure and uncertainty. Essentially, a program specifies a…
Damiano Azzolini, Fabrizio Riguzzi
Probabilistic 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…
Daan Fierens, Guy Van den Broeck, Ingo Thon, Bernd Gutmann + 1 more
'Luc De Raedt'] Probabilistic logic programs are logic programs in which some of the facts are annotated with probabilities. Several classical probabilistic inference tasks (such as MAP and computing marginals) have not yet received a lot of attention for this formalism. The contribution of this paper is that we…
Simon Vandevelde, Victor Verreet, Luc De Raedt, Joost Vennekens
We present Probabilistic Decision Model and Notation (pDMN), a probabilistic extension of Decision Model and Notation (DMN). DMN is a modeling notation for deterministic decision logic, which intends to be user-friendly and low in complexity. pDMN extends DMN with probabilistic reasoning, predicates, functions…
Pedro Zuidberg Dos Martires, Nitesh Kumar, Andreas Persson, Amy Loutfi + 1 more
'Amy Loutfi' 'Luc De Raedt'] Robotic agents should be able to learn from sub-symbolic sensor data and, at the same time, be able to reason about objects and communicate with humans on a symbolic level. This raises the question of how to overcome the gap between symbolic and sub-symbolic artificial intelligence. We…
Daan Fierens, Guy Van den Broeck, Joris Renkens, Dimitar Shterionov + 4 more
'Bernd Gutmann' 'Ingo Thon' 'Gerda Janssens' 'Luc De Raedt'] Probabilistic logic programs are logic programs in which some of the facts are annotated with probabilities. This paper investigates how classical inference and learning tasks known from the graphical model community can be tackled for probabilistic logic…
Francesca Toni, Nico Potyka, Markus Ulbricht, Pietro Totis
ProbLog is a popular probabilistic logic programming language/tool, widely used for applications requiring to deal with inherent uncertainties in structured domains. In this paper we study connections between ProbLog and a variant of another well-known formalism combining symbolic reasoning and reasoning under…
Majd Abdallah, Valentin Iovene, Gaston Zanitti, Demian Wassermann
Inferring reliable brain-behavior associations requires synthesizing evidence from thousands of functional neuroimaging studies through meta-analysis. However, existing meta-analysis tools are limited to investigating simple neuroscience concepts and expressing a restricted range of questions. Here, we expand the scope…
Luc De Raedt, Angelika Kimmig
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…
Parisa Kordjamshidi, Dan Roth, Kristian Kersting
Data-driven approaches are becoming increasingly common as problem-solving tools in many areas of science and technology. In most cases, machine learning models are the key component of these solutions. Often, a solution involves multiple learning models, along with significant levels of reasoning with the models'…
Majd Abdallah, Valentin Iovene, Gaston Zanitti, Demian Wassermann
Inferring reliable brain-behavior associations requires synthesizing evidence from thousands of functional neuroimaging studies through meta-analysis. However, existing meta-analysis tools are limited to investigating simple neuroscience concepts and expressing a restricted range of questions. Here, we expand the scope…
Joohyung Lee, Yi Wang
We introduce the concept of weighted rules under the stable model semantics following the log-linear models of Markov Logic. This provides versatile methods to overcome the deterministic nature of the stable model semantics, such as resolving inconsistencies in answer set programs, ranking stable models, associating…
Timothy van Bremen, Anton Dries, Jean Christoph Jung
We present onto2problog, a tool that supports ontology-mediated querying of probabilistic data via probabilistic logic programming engines. Our tool supports conjunctive queries on probabilistic data under ontologies encoded in the description logic $\mathcal{ELH}^{dr}$, thus capturing a large part of the OWL 2 EL…
Philippe Desjardins-Proulx, Timothée Poisot, Dominique Gravel
Artificial Intelligence presents an important paradigm shift for science. Science is traditionally founded on theories and models, most often formalized with mathematical formulas handcrafted by theoretical scientists and refined through experiments. Machine learning, an important branch of modern Artificial…
Viktor Senderov, Jan Kudlicka, Daniel Lundén, Viktor Palmkvist + 4 more
We present TreePPL, a language for probabilistic modeling and inference in statistical phylogenetics. Specifically, TreePPL is a domain-specific universal probabilistic programming language (PPL), particularly designed for describing phylogenetic models. The core idea is to express the model as a computer program…
Malte Rørmose Damgaard, Rasmus Pedersen, Thomas Bak
Title: Summary Inspired by the “cognitive hourglass” model presented by the researchers behind the cognitive architecture called Sigma, we propose a framework for developing cognitive architectures for cognitive robotics. The main purpose of the proposed framework is to ease development of cognitive architectures by…
Theo Knijnenburg, Gunnar Klau, Francesco Iorio, Mathew Garnett + 3 more
Mining large datasets using machine learning approaches often leads to models that are hard to interpret and not amenable to the generation of hypotheses that can be experimentally tested. Finding ‘actionable knowledge’ is becoming more important, but also more challenging as datasets grow in size and complexity. We…
Lewis Grozinger, Jesús Miró-Bueno, Ángel Goñi-Moreño
The programming of computations in living cells can be done by manipulating information flows within genetic networks. Typically, a single bit of information is encoded by a single gene’s steady state expression. Expression is discretized into high and low levels that correspond to 0 and 1 logic values, analogous to…
Authors not listed
Step-by-step thinking is essential in all domains of chemical sciences and engineering. While machine learning tools are broadly used, algorithms that automate reasoning are far less common. We elaborate on seven categories of human reasoning activities and connect each to applications in chemical science and…
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In the real world, many reversal phenomena occur—for example, cases in which a statement once regarded as false is later recognized as true. Upside-Down Logic is a framework designed to formalize such reversal phenomena as a logical system. It inverts the truth and falsity of propositions through contextual…