Spain

ECAI 2020

Integrating Open-Ended Learning in the Sense-Plan-Act Robot Control Paradigm

knowledge abstraction

learning

planning

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!

ECAI 2020 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.

technical paper

Applying the Closed World Assumption to SUMO-based FOL Ontologies for Effective Commonsense Reasoning

Group Behavior Recognition Using Attention- and Graph-Based Neural Networks

How Much Knowledge is in a Knowledge Base? Introducing Knowledge Measures (Preliminary Report)

Human Activity Recognition from Wearable Sensor Data Using Self-Attention

Learning Temporal Action Models via Constraint Programming

Finite and Con dent Teaching in Expectation: Sampling from In finite Concept Classes

A Shared-Word Sensitive Sequence-to-Sequence Features Extractor for Sentences Matching

A Convergent Off-Policy Temporal Difference Algorithm

BTDE: Block Term Decomposition Embedding for Link Prediction in Knowledge Graph

Personalized Automated Machine Learning

https://d3smihljt9218e.cloudfront.net/transcript/1084/subtitle/576e052e9323689480e7858ec588c95f.vtt

English (automatic)

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Applying the Closed World Assumption to SUMO-based FOL Ontologies for Effective Commonsense Reasoning

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Learning First-Order Symbolic Representations from the Structure of the State Space

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Next from ECAI 2020

Applying the Closed World Assumption to SUMO-based FOL Ontologies for Effective Commonsense Reasoning

Similar lecture

Learning First-Order Symbolic Representations from the Structure of the State Space

Downloads