Spain

ECAI 2020

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

self-attentive dynamic rooting

charge prediction

capsule network

stages 

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING 2020

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

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.

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

Studying lack of information through type-2 fuzzy strong negation

A Syntactic Approach for Privacy-Preserving Federated Learning

Segmented Tables: an Efficient Modeling Tool for Constraint Reasoning

Within the autonomous driving domain, there is now a  clear need and tremendous potential for hybrid solutions (e.g., integrating  semantics, learning, visual computing) towards fulfilling essential  legal and ethical responsibilities involving explainability (e.g.,  for diagnosis), human-centred AI (e.g., interaction design), and industrial  standardisation (e.g, pertaining to representation, realisation  of rules & norms). In these contexts, this highlight paper positions
 recent research from IJCAI 2019 aimed at advancing humancentred  AI principles in the backdrop of the autonomous driving  application domain. From a technical viewpoint, the highlighted research  provides a model for advancing the state of the art in reasoning
 about space and motion, combining reasoning and learning, nonmonotonic  reasoning, and computational modelling of high-level visuospatial  commonsense. In addition to demonstrating the significance  of integrated vision and semantics solutions in autonomous  driving, we also highlight open questions emphasising the need for  interdisciplinary mixed-methods research –involving AI, Psychology,  HCI– to better appreciate the complexity and spectrum of varied
 human-centred challenges in diverse naturalistic driving situations.

Downloads

Next from ECAI 2020

Bidirectional Scene Text Recognition with a Single Decoder

Similar lecture

Regularized Attentive Capsule Network for Overlapped Relation Extraction

.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

Bidirectional Scene Text Recognition with a Single Decoder

Similar lecture

Regularized Attentive Capsule Network for Overlapped Relation Extraction

Downloads