Spain

ECAI 2020

Rewrite or not Rewrite? ML-based Algorithm Selection for Datalog Query Answering on Knowledge Graphs

datalog query answering

algorithm selection

knowledge graphs

The 9th Brazilian Conference on Intelligent Systems (BRACIS), the 16h Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), and the 12th Concurso de Teses e Dissertações em Inteligência Artificial e Computacional (CTDIAC) was held from 20 to 23 October, 2020.

BRACIS originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence - SBIA (22 editions), and the Brazilian Symposium on Neural Networks - SBRN (13 editions). ENIAC is a national forum for researchers, practitioners, educators and students to present and discuss innovations, trends, experiences, developments and work in progress in the fields of Artificial and Computational Intelligence. CTDIAC is a satellite event whose goals are to disseminate and award the best dissertations and theses (MSc and PhDs concluded in Brazilian universities from June 2018 to May 2020) in Artificial and Computational Intelligence areas.

In this year, the Symposium on Knowledge Discovery, Mining and Learning (KDMiLe) will be held in conjunction with BRACIS 2020. This sysposium aims at integrating researchers, practitioners, developers, students and users to present their research results, to discuss ideas, and to exchange techniques, tools, and practical experiences – related to the Data Mining and Machine Learning areas.

BRACIS 2020

technical paper

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

… and many more!

Unsupervised Adversarial Learning of Anomaly Detection in the Wild

Ensemble Methods for Object Detection

Interpretable & Time-Budget-Constrained Contextualization for Re-Ranking

Learning to Predict Charges for Legal Judgment via Self-Attentive Capsule Network

Bidirectional Scene Text Recognition with a Single Decoder

Humans are capable of abstracting away irrelevant details or distinguish the common properties when studying problems. This is especially noticeable for planning problems, as humans are able to disregard certain parts of the domain and focus on the key elements important for finding a plan. Recently, the notion of abstraction has been introduced for Answer Set Programming (ASP), a knowledge representation and reasoning paradigm widely used in problem solving, with the potential to understand the key elements of a program that play a role in finding a solution. This paper highlights the potential use of such an abstraction in getting to the essence of planning problems, expressed in ASP, by disregarding irrelevant details and computing abstract plans.

Abstraction for ASP Planning

In some abstract argumentation framework (AAF), arguments have a degree of uncertainty, which impacts on the degree of uncertainty of the extensions obtained under a semantics. In these approaches, both the uncertainty of the arguments and of the extensions are modeled by means of precise probability values. However, in many real life situations the exact probabilities values are unknown and sometimes there is a need for aggregating the probability values of different sources. In this paper, we tackle the problem of calculating the degree of uncertainty of the extensions considering that the probability values of the arguments are imprecise.

Towards an Imprecise Probability Approach for Abstract Argumentation

Local to Global Learning of a Latent Dynamic Bayesian Network

From Eigentrust to a Trust-measuring algorithm in the Max-Plus Algebra

We describe a reasoning framework for knowledge invention and creative problem solving that can integrate and extend the knowledge level mechanism of diverse cognitive architectures (CAs). This framework exploits an extension of a Description Logic (DL) of typicality able to combine prototypical (commonsense) descriptions of concepts. It works as follows: given a goal expressed as a set of properties, in case an intelligent agent cannot find a concept in its knowledge base (KB) able to fulfill these properties, our framework is able to dynamically recombine, in a goal-oriented perspective, the concepts in the KB in order to find a suitable creative combination able to satisfy the goal. The KB of the agent is then extended via a mechanism of commonsense concept combination where the resulting combined concept represents the solution for the initial goal. Here we discuss how such framework is compliant with the general tenets of the Standard Model of Mind and can extend the knowledge level capabilities of diverse CAs.

A goal-oriented framework for knowledge invention and creative problem solving in cognitive architectures

poster

https://d3smihljt9218e.cloudfront.net/transcript/1265/subtitle/ff7931219e754c8ce5f700183d43d810.vtt

English (automatic)

Rewrite or not Rewrite? ML-based Algorithm Selection for Datalog Query Answering on Knowledge Graphs

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Rewrite or not Rewrite? ML-based Algorithm Selection for Datalog Query Answering on Knowledge Graphs

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Next from ECAI 2020

Unsupervised Adversarial Learning of Anomaly Detection in the Wild

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Seleção Automática de Algoritmo para o Problema Quadrático de Alocação Usando Meta-aprendizagem e Métricas de Superfície de Aptidão

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

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