@article {28224,
	title = {Artificial intelligence in dry eye disease},
	journal = {The Ocular Surface},
	volume = {23},
	year = {2022},
	month = {Jan-01-2022},
	pages = {74 - 86},
	publisher = {Elsevier},
	abstract = {Dry eye disease (DED) has a prevalence of between 5 and 50\%, depending on the diagnostic criteria used and population under study. However, it remains one of the most underdiagnosed and undertreated conditions in ophthalmology. Many tests used in the diagnosis of DED rely on an experienced observer for image interpretation, which may be considered subjective and result in variation in diagnosis. Since artificial intelligence (AI) systems are capable of advanced problem solving, use of such techniques could lead to more objective diagnosis. Although the term {\textquoteleft}AI{\textquoteright} is commonly used, recent success in its applications to medicine is mainly due to advancements in the sub-field of machine learning, which has been used to automatically classify images and predict medical outcomes. Powerful machine learning techniques have been harnessed to understand nuances in patient data and medical images, aiming for consistent diagnosis and stratification of disease severity. This is the first literature review on the use of AI in DED. We provide a brief introduction to AI, report its current use in DED research and its potential for application in the clinic. Our review found that AI has been employed in a wide range of DED clinical tests and research applications, primarily for interpretation of\&nbsp;interferometry, slit-lamp and\&nbsp;meibography\&nbsp;images. While initial results are promising, much work is still needed on model development, clinical testing and standardisation.},
	keywords = {artificial intelligence, Dry eye disease, Machine learning},
	issn = {15420124},
	doi = {10.1016/j.jtos.2021.11.004},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1542012421001324},
	author = {Stor{\r a}s, Andrea and Str{\"u}mke, Inga and Riegler, Michael and Grauslund, Jakob and Hugo Lewi {Hammer} and Yazidi, Anis and Halvorsen, P{\r a}l and Kjell G. {Gundersen} and Tor P. {Utheim} and Catherine J. {Jackson}}
}
@article {hammer2022efficient,
	title = {Efficient quantile tracking using an oracle},
	journal = {Applied Intelligence},
	volume = {53},
	year = {2022},
	pages = {289{\textendash}300},
	publisher = {Springer},
	author = {Hugo Lewi {Hammer} and Yazidi, Anis and Riegler, Michael and Rue, H{\r a}vard}
}
@article {hammer2022estimating,
	title = {Estimating tukey depth using incremental quantile estimators},
	journal = {Pattern Recognition},
	volume = {122},
	year = {2022},
	pages = {108339},
	publisher = {Elsevier},
	author = {Hugo Lewi {Hammer} and Yazidi, Anis and Rue, H\aavard}
}
@article {42795,
	title = {Towards the Neuroevolution of Low-level artificial general intelligence},
	journal = {Frontiers in Robotics and AI},
	volume = {9},
	year = {2022},
	month = {10/2022},
	publisher = {Frontiers},
	abstract = {In this work, we argue that the search for Artificial General Intelligence should start from a much lower level than human-level intelligence. The circumstances of intelligent behavior in nature resulted from an organism interacting with its surrounding environment, which could change over time and exert pressure on the organism to allow for learning of new behaviors or environment models. Our hypothesis is that learning occurs through interpreting sensory feedback when an agent acts in an environment. For that to happen, a body and a reactive environment are needed. We evaluate a method to evolve a biologically- inspired artificial neural network that learns from environment reactions named Neuroevolution of Artificial General Intelligence, a framework for low-level artificial general intelligence. This method allows the evolutionary complexification of a randomly-initialized spiking neural network with adaptive synapses, which controls agents instantiated in mutable environments. Such a configuration allows us to benchmark the adaptivity and generality of the controllers. The chosen tasks in the mutable environments are food foraging, emulation of logic gates, and cart-pole balancing. The three tasks are successfully solved with rather small network topologies and therefore it opens up the possibility of experimenting with more complex tasks and scenarios where curriculum learning is beneficial.},
	doi = {10.3389/frobt.2022.1007547},
	url = {https://www.frontiersin.org/articles/10.3389/frobt.2022.1007547/full},
	author = {Pontes-Filho, Sidney and Olsen, Kristoffer and Yazidi, Anis and Riegler, Michael and Halvorsen, P{\r a}l and Nichele, Stefano}
}
@inproceedings {glover2021dynamical,
	title = {The Dynamical Landscape of Reservoir Computing with Elementary Cellular Automata},
	journal = {The 2021 Conference on Artificial Life (ALIFE 2021)},
	year = {2021},
	publisher = {MIT Press},
	author = {Tom Eivind {Glover} and Lind, Pedro and Yazidi, Anis and Osipov, Evgeny and Nichele, Stefano}
}
@article {mofrad2021enhanced,
	title = {Enhanced Equivalence Projective Simulation: A Framework for Modeling Formation of Stimulus Equivalence Classes},
	journal = {Neural Computation},
	volume = {33},
	year = {2021},
	pages = {483{\textendash}527},
	publisher = {MIT Press},
	doi = {https://doi.org/10.1162/neco_a_01346},
	author = {Asieh Abolpour {Mofrad} and Yazidi, Anis and Samaneh Abolpour {Mofrad} and Hugo Lewi {Hammer} and Arntzen, Erik}
}
@article {hammer2021joint,
	title = {Joint tracking of multiple quantiles through conditional quantiles},
	journal = {Information Sciences},
	volume = {563},
	year = {2021},
	pages = {40{\textendash}58},
	publisher = {Elsevier},
	author = {Hugo Lewi {Hammer} and Yazidi, Anis and Rue, H\aavard}
}
@article {yazidi2020achieving,
	title = {Achieving Fair Load Balancing by Invoking a Learning Automata-Based Two-Time-Scale Separation Paradigm},
	journal = {IEEE Transactions on Neural Networks and Learning Systems},
	year = {2020},
	pages = {1-14},
	publisher = {IEEE},
	author = {Yazidi, Anis and Hassan, Ismail and Hugo Lewi {Hammer} and Oommen, John}
}
@article {yazidi2020balanced,
	title = {Balanced difficulty task finder: an adaptive recommendation method for learning tasks based on the concept of state of flow},
	journal = {Cognitive Neurodynamics},
	volume = {14},
	number = {5},
	year = {2020},
	pages = {675{\textendash}687},
	publisher = {Springer},
	author = {Yazidi, Anis and Asieh Abolpour {Mofrad} and Goodwin, Morten and Hugo Lewi {Hammer} and Arntzen, Erik}
}
@article {mofrad2020equivalence,
	title = {Equivalence Projective Simulation as a Framework for Modeling Formation of Stimulus Equivalence Classes},
	journal = {Neural Computation},
	volume = {32},
	number = {5},
	year = {2020},
	pages = {912{\textendash}968},
	publisher = {MIT Press},
	address = {Cambridge, MA, USA},
	issn = {0899-7667},
	doi = {10.1162/neco_a_01274},
	url = {https://doi.org/10.1162/neco_a_01274},
	author = {Asieh Abolpour {Mofrad} and Yazidi, Anis and Hugo Lewi {Hammer} and Arntzen, Erik}
}
@article {mofrad2020equivalence,
	title = {Equivalence projective simulation as a framework for modeling formation of stimulus equivalence classes},
	journal = {Neural computation},
	volume = {32},
	number = {5},
	year = {2020},
	pages = {912{\textendash}968},
	publisher = {{MIT Press},
	abstract = {Stimulus equivalence (SE) and projective simulation (PS) study complex behavior, the former in human subjects and the latter in artificial agents. We apply the PS learning framework for modeling the formation of equivalence classes. For this purpose, we first modify the PS model to accommodate imitating the emergence of equivalence relations. Later, we formulate the SE formation through the matching-to-sample (MTS) procedure. The proposed version of PS model, called the equivalence projective simulation (EPS) model, is able to act within a varying action set and derive new relations without receiving feedback from the environment. To the best of our knowledge, it is the first time that the field of equivalence theory in behavior analysis has been linked to an artificial agent in a machine learning context. This model has many advantages over existing neural network models. Briefly, our EPS model is not a black box model, but rather a model with the capability of easy interpretation and flexibility for further modifications. To validate the model, some experimental results performed by prominent behavior analysts are simulated. The results confirm that the EPS model is able to reliably simulate and replicate the same behavior as real experiments in various settings, including formation of equivalence relations in typical participants, nonformation of equivalence relations in language-disabled children, and nodal effect in a linear series with nodal distance five. Moreover, through a hypothetical experiment, we discuss the possibility of applying EPS in further equivalence theory research.},
	author = {Asieh Abolpour {Mofrad} and Yazidi, Anis and Hugo Lewi {Hammer} and Arntzen, Erik}
}
@article {mofrad2020equivalence,
	title = {Equivalence projective simulation as a framework for modeling formation of stimulus equivalence classes},
	journal = {Neural computation},
	volume = {32},
	number = {5},
	year = {2020},
	pages = {912{\textendash}968},
	publisher = {{MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info~{\textellipsis}},
	author = {Asieh Abolpour {Mofrad} and Yazidi, Anis and Hugo Lewi {Hammer} and Arntzen, Erik}
}
@inproceedings {pontes2020evodynamic,
	title = {EvoDynamic: A Framework for the Evolution of Generally Represented Dynamical Systems and Its Application to Criticality},
	journal = {International Conference on the Applications of Evolutionary Computation (Part of EvoStar)},
	year = {2020},
	pages = {133{\textendash}148},
	publisher = {Springer},
	address = {Cham},
	author = {Pontes-Filho, Sidney and Lind, Pedro and Yazidi, Anis and Zhang, Jianhua and Hugo Lewi {Hammer} and Gustavo BM {Mello} and Sandvig, Ioanna and Tufte, Gunnar and Nichele, Stefano}
}
@article {yazidi2020game,
	title = {Game-Theoretic Learning for Sensor Reliability Evaluation Without Knowledge of the Ground Truth},
	journal = {IEEE Transactions on Cybernetics},
	year = {2020},
	publisher = {IEEE},
	author = {Yazidi, Anis and Hugo Lewi {Hammer} and Samouylov, Konstantin and Herrera-Viedma, Enrique}
}
@article {kongshavn2020mitigating,
	title = {Mitigating DDoS using weight-based geographical clustering},
	journal = {Concurrency and Computation: Practice and Experience},
	volume = {32},
	number = {11},
	year = {2020},
	pages = {e5679},
	publisher = {Wiley Online Library},
	author = {Kongshavn, Madeleine and Haugerud, H\aarek and Yazidi, Anis and Torle {Maseng, iv} and Hugo Lewi {Hammer}}
}
@article {pontes2020neuro,
	title = {A neuro-inspired general framework for the evolution of stochastic dynamical systems: Cellular automata, random Boolean networks and echo state networks towards criticality},
	journal = {Cognitive Neurodynamics},
	volume = {14},
	year = {2020},
	pages = {1{\textendash}18},
	publisher = {Springer},
	url = {https://doi.org/10.1007/s11571-020-09600-x},
	author = {Pontes-Filho, Sidney and Lind, Pedro and Yazidi, Anis and Zhang, Jianhua and Hugo Lewi {Hammer} and Gustavo BM {Mello} and Sandvig, Ioanna and Tufte, Gunnar and Nichele, Stefano}
}
@article {yazidi2020solving,
	title = {Solving Sensor Identification Problem Without Knowledge of the Ground Truth Using Replicator Dynamics},
	journal = {IEEE Transactions on Cybernetics},
	year = {2020},
	pages = {1-9},
	publisher = {IEEE},
	author = {Yazidi, Anis and Marco Antonio {Pinto-Orellana} and Hugo Lewi {Hammer} and Mirtaheri, Peyman and Herrera-Viedma, Enrique}
}
@article {hammer2019new,
	title = {A new quantile tracking algorithm using a generalized exponentially weighted average of observations},
	journal = {Applied Intelligence},
	volume = {49},
	number = {4},
	year = {2019},
	pages = {1406{\textendash}1420},
	publisher = {{Springer},
	author = {Hugo Lewi {Hammer} and Yazidi, Anis and Rue, H\aavard}
}
@article {hammer2019smooth,
	title = {Smooth estimates of multiple quantiles in dynamically varying data streams},
	journal = {Pattern Analysis and Applications},
	year = {2019},
	pages = {1{\textendash}12},
	publisher = {{Springer},
	author = {Hugo Lewi {Hammer} and Yazidi, Anis}
}
@article {mofrad2019solving,
	title = {On solving the SPL problem using the concept of probability flux},
	journal = {Applied Intelligence},
	volume = {49},
	number = {7},
	year = {2019},
	pages = {2699{\textendash}2722},
	publisher = {{Springer},
	author = {Asieh Abolpour {Mofrad} and Yazidi, Anis and Hugo Lewi {Hammer}}
}
@article {yazidi2019two,
	title = {Two-time scale learning automata: an efficient decision making mechanism for stochastic nonlinear resource allocation},
	journal = {Applied Intelligence},
	year = {2019},
	pages = {1{\textendash}14},
	publisher = {{Springer},
	author = {Yazidi, Anis and Hugo Lewi {Hammer} and Tore M {Jonassen}}
}
