Spain

ECAI 2020

The art painting style Classifier based on Logic Aggregators and qualitative colour Descriptors (C-LAD)

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

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!

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.

Robust Decision Tree Inductuion from Unreliable Data Sources

Cost-Based Budget Active Learning for Deep Learning

Towards Multi-Robot Task-Motion Planning for Navigation in Belief Space

Towards Traceability of Decision-making in Intelligent Context-based Adaptive System Environments

Stability in Minimization-Based Altruistic Hedonic Games

Towards deep learning reliable gender estimation from dental panoramic radiographs

Defining Argumentation Semantics under a Claim-centric View

Towards Human-Based Models of Behaviour in Social Robots: Exploring Age-Related Differences in the Processing of Gaze Cues in Human-Robot Interaction

A Machine Learning Approach to Boredom Detection using Smartphone's Sensors

We introduce and study SL[F] — a quantitative extension of SL (Strategy Logic), one of the most natural and expressive logics describing strategic behaviours. The satisfaction value of an SL[F] formula is a real value in [0, 1], reflecting “how much” or “how well” the strategic on-going objectives of the underlying agents are satisfied. We demonstrate the applications of SL[F] in quantitative reasoning about multi-agent systems, by showing how it can express concepts of stability in multi-agent systems, and how it generalises some fuzzy temporal logics. We also provide a model-checking algorithm for our logic, based on a quantitative extension of Quantified CTL* .

The art painting style Classifier based on Logic Aggregators and qualitative colour Descriptors (C-LAD)

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Robust Decision Tree Inductuion from Unreliable Data Sources

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The art painting style Classifier based on Logic Aggregators and qualitative colour Descriptors (C-LAD)

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Next from ECAI 2020

Robust Decision Tree Inductuion from Unreliable Data Sources

Similar lecture

Learning Adversarially Robust Sparse Networks via Weight Reparameterization

Downloads