Transparent, Reliable and Unbiased Smart Tool for AI

Livrables

This deliverable concerns a status report on the technical achievements of TRUST in the first nine months of the projects A brief description of the development of each task will be provided including documentation of procedures screenshots preliminary results and identified risks

Data management plan V2
Saliency measures for identifying causally variables of explanations

This report will present the saliency measures and code for identifying causally relevant variables of humanlike explanations The relevant variables are those to be considered during the discovery and communication of causal explanations These variables will be formalised and measurable in terms of their specificity insensitivity proximity and other characteristics known to be preferred by humans

User studies on the realization of explanations

The deliverable reports the result of the qualitative studies on the best way to present explanation content produced in WP3 and provides recommendations for Task 22

Framework requirements document

This deliverable will describe the functional and nonfunctional requirements of the framework as well as the interactions and dependencies between the building blocks The use case needs will also be detailed and reported in this document ensuring that the framework is adequate to different problems and sectors

Communication & Dissemination plan

This report will present the Communication Dissemination Plan of TRUST where the strategy to raise public awareness about the project outcomes will be detailed and scheduled In addition to academic publications and conferences the plan includes events promotion participation in working groups and online forums and educational content creation such as courseware and webinars The plan will include KPIs and their target values as well as the Partner responsible for each communicationdissemination method

Evaluation with healthcare experts of learned models

This deliverable concerns a formal validation of the AI models developed for the first simplified version of the healthcare problem These models will be designed by NWOI and validated by medical experts from LUMC The report will present the first insights on the models results and suggestions for modifications

Initial validation of the explainable AI models from business experts

This deliverable concerns a formal validation of the AI models developed for the first simplified version of the online retail problem These models will be designed by LTP and validated by practitioners from Sonae INESC will coordinate the development and validation process

Data management plan

This deliverable presents the Data Management Plan of TRUSTAI detailing the types of data generatedcollected how it will be exploited protected and the standards to be considered

Initial validation of the explainable AI models from energy experts

This deliverable concerns a formal validation of the AI models developed for the first simplified version of the energy problem These models will be designed by POLIS21 and validated by practitioners from the industry

Project website

This deliverable will present the specification, organization and features of TRUST-AI website. The DNS, URL to access and screenshots on each page will also be presented.

Publications

Multi-objective Genetic Programming for Explainable Reinforcement Learning

Auteurs: Videau, Mathurin; Ferreira Leite, Alessandro; Teytaud, Olivier; Schoenauer, Marc
Publié dans: EUROGP - 25th European Conference on Genetic Programming, part of EvoStar 2022, Numéro 25, 2022, Page(s) pp.278-293, ISBN 978-3-031-02055-1
Éditeur: Springer Verlag
DOI: 10.1007/978-3-031-02056-8_18

Building data models and data sharing. Purpose, approaches and a case study on explainable demand response

Auteurs: Nikos Sakkas, Ch. Chaniotaki, Nikitas. Sakkas, Costas Daskalakis
Publié dans: Emerging Concepts for Sustainable Built Environment, 2022
Éditeur: SBEfin 2022 Conference

Multi-modal multi-objective model-based genetic programming to find multiple diverse high-quality models

Auteurs: Sijben, Evi; Alderliesten, Tanja; Bosman, Peter
Publié dans: GECCO '22: Genetic and Evolutionary Computation Conference, 2022, ISBN 978-1-4503-9237-2
Éditeur: Association for Computing Machinery, New York, NY, United States
DOI: 10.48550/arxiv.2203.13347

Memetic Semantic Genetic Programming for Symbolic Regression

Auteurs: Alessandro Leite and Marc Schoenauer
Publié dans: 26th EuroGP - Part of EvoStar 2023, Numéro 26, 2023, Page(s) 198–212, ISBN 978-3-031-29572-0
Éditeur: Springer Verlag LNCS-13986
DOI: 10.1007/978-3-031-29573-7_13

Explanatory World Models via Look Ahead Attention for Credit Assignment

Auteurs: Oriol Corcoll and Raul Vicente
Publié dans: Numéro 26403498, 2022, ISSN 2640-3498
Éditeur: Proceedings of Machine Learning Research

Emergence of Adaptive Circadian Rhythms in Deep Reinforcement Learning

Auteurs: Labash, Aqeel; Fletzer, Florian; Majoral, Daniel; Vicente, Raul
Publié dans: ICML'23: Proceedings of the 40th International Conference on Machine Learning, Numéro 18, 2023
Éditeur: JMLR.org
DOI: 10.48550/arxiv.2307.12143

Real time Data and Application Sharing and Collaboration for the Building Energy Domain

Auteurs: N. Sakkas, M. Papadopoulou, D. Sakkas
Publié dans: WDBE 2021, 2021
Éditeur: World of Digital Built Environment WDBE 2021

Evolvability degeneration in multi-objective genetic programming for symbolic regression

Auteurs: Dazhuang Liu, Marco Virgolin, Tanja Alderliesten, Peter A. N. Bosman
Publié dans: GECCO '22: Genetic and Evolutionary Computation Conference, 2022
Éditeur: Association for Computing Machinery, New York, NY, United States
DOI: 10.1145/3512290.3528787

Mind the gap: challenges of deep learning approaches to Theory of Mind

Auteurs: Aru, Jaan; Labash, Aqeel; Corcoll, Oriol; Vicente, Raul
Publié dans: Artificial Ingtelligence Review, Numéro 3, 2023, ISSN 0269-2821
Éditeur: Kluwer Academic Publishers
DOI: 10.1007/s10462-023-10401-x

Open data or open access? The case of building data.

Auteurs: Sakkas, N., Yfanti, S
Publié dans: Academia Letters, 2021, ISSN 2771-9359
Éditeur: Academia.edu
DOI: 10.20935/al3629

Deep neural networks using a single neuron: folded-in-time architecture using feedback-modulated delay loops

Auteurs: Stelzer, Florian; Röhm, André; Vicente, Raul; Fischer, Ingo; Yanchuk, Serhiy
Publié dans: Nature Communications, Numéro 20411723, 2021, ISSN 2041-1723
Éditeur: Nature Publishing Group
DOI: 10.48550/arxiv.2011.10115

Quantifying Reinforcement-Learning Agent’s Autonomy, Reliance on Memory and Internalisation of the Environment

Auteurs: Anti Ingel, Abdullah Makkeh, Oriol Corcoll and Raul Vicente
Publié dans: Entropy, Numéro 10994300, 2022, ISSN 1099-4300
Éditeur: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/e24030401

Interpretable Forecasting of Energy Demand in the Residential Sector

Auteurs: Nikos Sakkas; Sofia Yfanti; Costas Daskalakis; Eduard Barbu; Marharyta Domnich
Publié dans: Energies, Numéro 1, 2021, ISSN 1996-1073
Éditeur: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/en14206568

Do Deep Reinforcement Learning Agents Model Intentions?

Auteurs: Tambet Matiisen; Aqeel Labash; Daniel Majoral; Jaan Aru; Raul Vicente
Publié dans: Stats, Vol 6, Iss 1, Pp 50-66 (2022), Numéro 5, 2022, ISSN 2571-905X
Éditeur: MDPI
DOI: 10.3390/stats6010004

Drivers of and counterfactuals for the final energy and electricity consumption in EU industry

Auteurs: Sakkas, N., Athanasiou, N.
Publié dans: Academia Letters, Numéro 27719359, 2021, ISSN 2771-9359
Éditeur: Academia.edu
DOI: 10.20935/al3451

Livrables

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