Spain

ECAI 2020

Studying lack of information through type-2 fuzzy strong negation

strong negations

lack of information

type-2 fuzzy sets

fuzzy systems

AIAI 2020 (16th International Conference on Artificial Intelligence Applications and Innovations) should be held in Halkidiki, Greece. Due to current Covid-19 situation the conference will be held virtually. Underline will host a repository of video talks and preserve them for the future. Videos will be released on Underline one day after the conference.

AIAI 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!

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.

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.

Driven by Commonsense: On the Role of Human-Centred Visual Explainability for Autonomous Vehicles

The Winograd Schema Challenge (WSC) was proposed  by Levesque et al. in 2011 as a new test in Artificial Intelligence (AI)  and possibly as an alternative to the Turing test. WSC is a complex
 coreference resolution task which requires applying knowledge on  commonsense reasoning. It is an easy task for humans but it still remains  an unsolved challenge for computers. There are two categories  of proposed approaches for tackling the WSC. The first encompasses  techniques based on formalized Knowledge Representation and Reasoning  (KRR), while the second entails Machine Learning (ML)  approaches. In this paper we provide a review of the state-of-theart
 approaches proposed from both categories and we outline their  strengths and weaknesses. Additionally, we discuss a recent work  which combines techniques from both categories as it seems to be a  promising and innovative approach.

Review of the Recent Techniques for Learning Commonsense Knowledge applied to the Winograd Schema Challenge

This paper provides an overview of an architecture based on the principle of step-wise refinement for knowledge representation, explainable reasoning, and interactive learning, in robotics. We briefly describe the individual components of the architecture, and emphasize how the interplay between representation, reasoning, and learning, can be exploited to enable reliable and efficient decision making in dynamic domains

REBA-KRL: Refinement-based Architecture for Knowledge Representation, Explainable Reasoning, and Interactive Learning in Robotics

The problem of reasoning with context dependent knowledge has recently gained interest in the area of description logic based knowledge bases (KBs). Among the several proposals, we consider the Contextualized Knowledge Repository (CKR) framework. The CKR model has been recently extended with the capability of reasoning with global (context independent) defeasible axioms that can be overridden by local (context specific) knowledge. In CKR applications it is often useful to reason over a hierarchical organization of contexts. We highlight here our recent efforts on extending the CKR framework to allow for the representation of exception handling in the inheritance of knowledge across local contexts. We first concentrate on a limitation to a particular kind of context organization, i.e. ranked hierarchies, which allows us to simplify the definition of reasoning procedures.We then further generalize the proposal to extend the reasoning on exception handling over general contextual hierarchies.

Reasoning with Justifiable Exceptions in Contextual Hierarchies

Leo-III is an effective automated theorem prover for extensional type theory with Henkin semantics. It is based on an extensional higher-order paramodulation calculus and supports reasoning in monomorphic and rank-1 polymorphic first-order and higher-order logics. Leo-III also automates various non-classical logics, including almost every normal higher-order modal logic.

The Higher-Order Prover Leo-III

Actual Causality Canvas: A General Framework for Explanation-based Socio-Technical Constructs

Entropy-based adaptive exploit-explore coefficient for Monte-Carlo path planning

"Semantic interpretation of dynamic visuospatial imagery calls for a general and systematic integration of methods in knowledge representation and computer vision. Towards this, we highlight research articulating and developing deep (visuospatial) semantics, characterised by the existence of declarative models –e.g., pertaining space and motion– and corresponding formalisation and reasoning methods supporting capabilities such as semantic question-answering, relational visuospatial learning, and (non-monotonic) visuospatial explanation. We position a working model for deep semantics by highlighting our select recent works from IJCAI 2019, AAAI 2018, and ILP 2016 & ACS 2018. 

We posit that human-centred, explainable visual sensemaking necessitates both high-level semantics and low-level visual computing, with the highlighted works providing a model for systematic, modular integration of diverse multifaceted techniques and perspectives from AI, ML, Vision, Visual Perception, and Spatial Cognition."

Downloads

Next from ECAI 2020

A Syntactic Approach for Privacy-Preserving Federated Learning

Similar lecture

Hypotheses tests using non-asymptotic fuzzy estimators and fuzzy critical values

.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

A Syntactic Approach for Privacy-Preserving Federated Learning

Similar lecture

Hypotheses tests using non-asymptotic fuzzy estimators and fuzzy critical values

Downloads