Spain

This tutorial aims to provide an initial foundation in the concepts underlying computational argumentation and its role and relevance in the context of Human-Centric AI. We will also discuss potential applications in this context.

ECAI 2020

Computational Argumentation in the context of Human-Agent Interaction

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.

Scalable Deep Learning: How far is one billion neurons?

Practical Uses of Existential Rules in Knowledge Representation

Tutorial on Distributed Constraint Optimization for the Internet-of-Things (DCOP for IoT)

Knowledge graph embeddings are supervised learning models that learn vector representations of nodes and edges of labeled, directed multigraphs. We describe their design rationale, and explain why they are receiving growing attention within the burgeoning graph representation learning community. We highlight their limitations, open research directions, and real-world applicative scenarios. Besides a theoretical overview, we also provide a hands-on session, where we show how to use such models in practice. https://kge-tutorial-ecai2020.github.io/

Knowledge Graph Embeddings: From Theory to Practice

"This tutorial teaches the fundamental tools and techniques for running GPU-accelerated Python applications using CUDA® and the NUMBA compilers. Each participant will have access to a GPU workstation in the cloud on which to complete the many example programs used to demonstrate the concepts. Participants will work through dozens of hands-on coding exercises, and at the end of the training, implement a new workflow to accelerate an example program originally designed for CPUs, observing impressive performance gains.
On completion of this tutorial, participants will have additional resources to help them create new GPU-accelerated applications on their own.  Moreover, upon successful completion of the final assessment, participants will receive an NVIDIA Deep Learning Institute (DLI) certificate to recognize their subject matter competency and support professional career growth.
The content for this tutorial has been developed by NVIDIA’s Deep Learning Institute (https://www.nvidia.com/en-us/deep-learning-ai/education/)

The tutorial covers 3 main topics (note that if not all these topics can be covered in the time allocated for the workshop, participants can complete them through self-study thereafter):

1.	Introduction to CUDA Python with Numba: 
a.	Begin working with the Numba compiler and CUDA programming in Python.
b.	Use Numba decorators to GPU-accelerate numerical Python functions.
c.	Optimize host-to-device and device-to-host memory transfers.
2.	Create CUDA kernels in Python with Numba:
a.	Learn CUDA’s parallel thread hierarchy and how to extend parallel program possibilities.
b.	Launch massively parallel custom CUDA kernels on the GPU.
c.	Utilize CUDA atomic operations to avoid race conditions during parallel execution.
3.	Multi-dimensional grids and shared memory for CUDA Python with Numba:
a.	Learn support for GPU-accelerated Monte Carlo methods.
b.	Learn multidimensional grid creation and how to work in parallel on 2D matrices.
c.	Leverage on-device shared memory to promote memory coalescing while reshaping 2D matrices.

The tutorial is aimed at anyone wishing to accelerate Python applications. By making use of the Numba (just-in-time) compiler, CPU-based applications can be easily modified to execute on a GPU with possible performance gains. 
Prerequisites include: 

1.	Basic Python competency, including familiarity with variable types, loops, conditional statements, functions, and array manipulations. 
2.	NumPy competency, including the use of ndarrays and ufuncs. 
Note that no previous knowledge of CUDA programming is required.
Please note that prior to the start of the tutorial, all participants should have created an account on the NVIDIA developer site:  courses.nvidia.com/join 
"

Fundamentals of Accelerated Computing with CUDA Python

Mathematical Morphology and Logics: In propositional logics, considering the lattice of formulas, morphological operators will act on formulas (and on their models). In modal logics, dilation and erosion can define modal operators. Such examples will be described in several logics. Then we will show how they can be used to define revision operators satisfying the AGM postulates, merging operators, or explanatory relations.

Mathematical Morphology and Artificial Intelligence

Cognitive Logics: Mechanisms Predicting Human Inference Patterns

The tutorial introduces the audience to the concept of referring expressions, formulae that can be used to communicate identities of otherwise abstract objects. The formalism provides foundations for a successful and unambiguous exchange of information about individuals between agents sharing common knowledge about such individuals, a task that is indispensable in most modern applications of knowledge representation and semantic technologies.

Referring Expressions in Knowledge Representation System

The 1985 paper by Carlos Alchourrón (1931–1996), Peter Gärdenfors, and David Makinson (AGM), “On the Logic of Theory Change: Partial Meet Contraction and Revision Functions” was the starting-point of a large and rapidly growing literature that employs formal models in the investigation of changes in belief states and databases.

In this tutorial, the first 35 years of this development are summarized.

The topics covered include equivalent characterizations of AGM operations, extended representations of the belief states, change operators not included in the original framework, iterated change, applications of the model, its connections with other formal frameworks, and criticism of the model.

Belief Change: From 1985 to present days

The internet of things (IoT) has been expanding through an increased number of IoT devices and the vast deployment of sensors. Simultaneously, there have been significant advancements in artificial intelligence (AI), in particular machine learning (ML), due to the increased availability of data and computation capabilities. We consider that IoT enhanced with AI would enrich our lives in the future. This tutorial focuses on combining IoT and ML for what we call situation classification. Situation classification targets recognizing real-world events and generating insights for an accurate understanding of the environment's dynamics.

This tutorial's particular theme is the situation classification for future smart cities and smart mobility IoT applications, where we generate insights for relevant events happening in these domains. For instance, we may detect situations such as accidents, emergencies, and traffic congestions in smart cities in real-time for the responsiveness of the city to such incidents. Moreover, IoT data captured from various sensors may enable us for more efficient crowd management and improved public safety. Lastly, autonomous systems such as autonomous driving vehicles, robots, and unmanned aerial vehicles would undoubtedly benefit from situation classification by accurately understanding their environments and planning decisions based on their enhanced perspectives.

"

Computational Argumentation in the context of Human-Agent Interaction

Downloads

Next from ECAI 2020

Scalable Deep Learning: How far is one billion neurons?

Computational Argumentation in the context of Human-Agent Interaction

.css-70qvj9{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}Downloads

Next from ECAI 2020

Scalable Deep Learning: How far is one billion neurons?

Downloads