Spain

ECAI 2020

Improving VAE generations of multimodal data through data-dependent conditional priors

vaes

generative models

deep learning

Computational creativity is the art, science, philosophy and engineering of computational systems which, by taking on particular responsibilities, exhibit behaviours that unbiased observers would deem to be creative. As a field of research, this area is thriving, with progress in formalising what it means for software to be creative, along with many exciting and valuable applications of creative software in the sciences, the arts, literature, gaming and elsewhere.

The ICCC conference series organized by the Association for Computational Creativity since 2010 is the only scientific conference that focuses on computational creativity alone and also covers all aspects of it.


ICCC'20

The ICCC conference series organized by the Association for Computational Creativity since 2010 is the only scientific conference that focuses on computational creativity alone and also covers all aspects of it.
Computational creativity is the art, science, philosophy and engineering of computational systems which, by taking on particular responsibilities, exhibit behaviors that unbiased observers would deem to be creative. As a field of research, this area is thriving, with progress in formalizing what it means for software to be creative, along with many exciting and valuable applications of creative software in the sciences, the arts, literature, gaming and elsewhere.



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.

Explanations for Ontology-Mediated Query Answering in Description Logics

A Fuzzy Inference System for a Visually Grounded Robot State of Mind

Neural Network Heuristics for Classical Planning: A Study of Hyperparameter Space

Maximal Information Propagation with Budgets

On the Complexity of Constructive Control under Nearly Single-Peaked Preferences

RPN: A Residual Pooling Network for Efficient Federated Learning

Logic is responsible for scientific progress in many disciplines.  In particular, computer science and AI would be impossible  without it. Classical logics have long been accepted as a normative
 framework for human reasoning to capture correct reasoning. However,  many psychological findings such as the Wason Selection Task  have demonstrated that classical logic cannot serve as a possible  descriptive language for the human inference process, which is the   aim of what we call a cognitive logic. Recently, some nonmonotonic  logics have been employed to explain human inferences for some  well-known examples. In this paper, we discuss possible features of
 cognitive logics, present first results, and highlight new challenges.

Cognitive Logics: Features, Formalisms, and Challenges

This paper presents the Adaptive Decision Support for Electricity Markets Negotiations (AiD-EM) system. AiD-EM is a multi-agent system that provides decision support to market players by incorporating multiple sub-(agent-based) systems, directed to the decision support of specific problems. These sub-systems make use of different artificial intelligence methodologies, such as machine learning and evolutionary computation, to enable players adaptation in the planning phase and in actual negotiations in auction-based markets and bilateral negotiations

Adaptive learning in multiagent systems for automated energy contacts negotiation

In the past few years, several large companies have published ethical principles of Artificial Intelligence (AI). National governments, the European Commission, and inter-governmental organizations have come up with requirements to ensure the good use of AI. However, individual organizations that want to join this effort, are faced with many unsolved questions. This paper proposes guidelines for organizations committed to the responsible use of AI, but lack the required knowledge and experience. The guidelines consist of two parts: i) helping organizations to decide what principles to adopt, and ii) a methodology for implementing the principles in organizational processes. In case of future AI regulation, organizations following this approach will be well- prepared.

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