When Accidents Become Design Choices: Navigation Systems, Rat-Running, and AI Safety

Machine learning algorithms work particularly well if we want to find the best solution to a given problem from the set of all possible solutions. However, such an unprecedented ability to solve optimisation problems only stresses the need to carefully pick out the right goal to be optimised. In this regard, and taking route-planning services as a guiding example, I claim that the current problem definition for route-planning algorithms prompts disruptive driving practices such as intelligent rat-running which create, in turn, global problems by intending to optimise local ones. In order to avoid this, I defend that the design approach to such algorithms should aim for hybrid search strategies that constrain the local benefit to the global costs of a given solution, in order to set the grounds for a safer AI in the future

Learning Reasoning Strategies in End-to-End Differentiable Proving

Attempts to render deep learning models interpretable, data-efficient, and robust have seen some success through hybridisation with rule-based systems, for example, in Neural Theorem Provers (NTPs). These neuro-symbolic models can induce interpretable rules and learn representations from data via back-propagation, while providing logical explanations for their predictions. However, they are restricted by their computational complexity, as they need to consider all possible proof paths for explaining a goal, thus rendering them unfit for large-scale applications. We present Conditional Theorem Provers (CTPs), an extension to NTPs that learns an optimal rule selection strategy via gradient-based optimisation. We show that CTPs are scalable and yield state-of-the-art results on the CLUTRR dataset, which tests systematic generalisation of neural models by learning to reason over smaller graphs and evaluating on larger ones. Finally, CTPs show better link prediction results on standard benchmarks in comparison with other neural-symbolic models, while being explainable. All source code and datasets are available online, at https://github.com/uclnlp/ctp.Comment: Proceedings of the 37th International Conference on Machine Learning (ICML 2020

Improving the predictive skills of hydrological models using a combinatorial optimization algorithm and artificial neural networks

[Abstract:] Ensemble modelling is a numerical technique used to combine the results of a number of different individual models in order to obtain more robust, better-fitting predictions. The main drawback of ensemble modeling is the identification of the individual models that can be efficiently combined. The present study proposes a strategy based on the Random-Restart Hill-Climbing algorithm to efficiently build ANN-based hydrological ensemble models. The proposed technique is applied in a case study, using three different criteria for identifying the model combinations, different number of individual models to build the ensemble, and two different ANN training algorithms. The results show that model combinations based on the Pearson coefficient produce the best ensembles, outperforming the best individual model in 100% of the cases, and reaching NSE values up to 0.91 in the validation period. Furthermore, the Levenberg-Marquardt training algorithm showed a much lower computational cost than the Bayesian regularisation algorithm, with no significant differences in terms of accuracy.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This study is financed by the Galician government (Xunta de Galicia) as part of its pre-doctoral fellowship program (Axudas de apoio á etapa predoutoral 2019) Register No ED481A-2019/014.Xunta de Galicia; ED481A-2019/01

A Prolog application for reasoning on maths puzzles with diagrams

open5noDespite the indisputable progresses of artificial intelligence, some tasks that are rather easy for a human being are still challenging for a machine. An emblematic example is the resolution of mathematical puzzles with diagrams. Sub-symbolical approaches have proven successful in fields like image recognition and natural language processing, but the combination of these techniques into a multimodal approach towards the identification of the puzzle’s answer appears to be a matter of reasoning, more suitable for the application of a symbolic technique. In this work, we employ logic programming to perform spatial reasoning on the puzzle’s diagram and integrate the deriving knowledge into the solving process. Analysing the resolution strategies required by the puzzles of an international competition for humans, we draw the design principles of a Prolog reasoning library, which interacts with image processing software to formulate the puzzle’s constraints. The library integrates the knowledge from different sources, and relies on the Prolog inference engine to provide the answer. This work can be considered as a first step towards the ambitious goal of a machine autonomously solving a problem in a generic context starting from its textual-graphical presentation. An ability that can help potentially every human–machine interaction.openBuscaroli, Riccardo; Chesani, Federico; Giuliani, Giulia; Loreti, Daniela; Mello, PaolaBuscaroli, Riccardo; Chesani, Federico; Giuliani, Giulia; Loreti, Daniela; Mello, Paol

Exploratory Action Selection to Learn Object Properties Through Robot Manipulation

Výber prieskumných akcií je pojem popisujúci proces autonómnej selekcie krokov, ktoré vedú agenta k predurčenému cieľu. V tejto práci, je cieľom skúmanie vlastností a celkovo kategórie daného objektu (napr. materiál, krabica, šálka a pod.) robotickým manipulátorom. Extrahovať vlastnosti objektu len vizuálne je limitujúce, najmä v spojitosti s fyzikálnymi/materiálnymi vlastnosťami ako povrchové trenie, tuhosť, či hmotnosť. V rámci tejto práce je hlavným interaktívnym prvkom dotyk, teda najviac informácii je získavaných z haptickej manipulácie predmetom. Narozdiel od vizuálnych vnemov, ktoré sú pasívne---fotografie zaobstarané statickou kamerou---haptické skúmanie je v samotnej podstate aktívne: spôsob manipulácie priamo ovplyvňuje množstvo informácií, ktoré je možné získať. V tejto práci je táto idea sformalizovaná, kde sú volené ďalšie robotické akcie (stláčanie, či dvíhanie objektov) na základe toho, ako je pravdepodobné, že na základe danej akcie príde k zníženiu neistoty v rámci vlastností--teda na základe ich očakávaného informačného zisku. Akcia, ktorá prináša informácií najviac, je zvolená. Očakávaný informačný zisk je počítaný v troch rôznych módoch založených na informačnej entropii. Informačná entropia je odhadovaná ako pre diskrétne pravdepodobnostné rozdelenie materiálovej kategórie, tak i pre spojité pravdepodobnostné rozdelenie vlastností, ako pružnosť, či hustota. Používame klasifikáciu ako proxy metriku toho, ako veľmi sú rozhodnutia algoritmu ohľadne selekcie akcií optimálne. Mód optimalizujúci pre informačný zisk spojitej premennej vykazuje najlepšie výsledky. Učenie sa vlastností objektov je zabezpečené pomocou Bayesovskej aktualizácie z meraní priamo manipulátorom. Takýto výber akcií vedie k viac efektívnemu učenie o okolí a ako výsledok pomáha agentovi v navigácií reálnym svetom, kde je potrebné očakávať aj neočakávané.Action selection is a term used to describe a process of autonomous selection of steps that lead an agent to a predetermined goal. In this work, discovering object properties and the overall object category (e.g., material, or box, mug, etc.) by a robot manipulator is the desired goal. Extracting properties of objects from visual input only is limited, especially regarding physical/material properties like surface roughness, stiffness, or mass. Here, haptic exploration, i.e., mainly proprioceptive and tactile input during manipulation of the object, is indispensable. Furthermore, unlike visual sensing, which is often passive---images taken by a static camera---haptic exploration is intrinsically active: the particular way of manipulating the object determines the quality of information that can be acquired. Here, this idea is formalized, and robot actions (compressing or lifting objects) are assessed by how much they are likely to reduce uncertainty about specific object properties---their expected information gain. The most informative action is then chosen. The expected information gain is calculated in three different modes based on information entropy, which is estimated for both discrete probability distribution of material composition of the object (e.g., plastic, ceramics, metal) and continuous distribution of each property like elasticity or density. We use classification as a proxy metric of how optimal are the choices of the action selection algorithm. Overall the mode optimizing for the information gain of the continuous properties results in the best classification. Learning of object properties is accomplished in the form of a Bayesian update from real measurement actions. Such selection of actions leads to more efficient learning about the environment and, as a result, helps the agent in navigating the real world, where the unexpected shall be expected

Aprendizaje y toma de decisiones bajo incertidumbre

Una de las principales ramas de Machine Learning es Reinforcement Learning, donde un "agente" aprende a lo largo de sucesivas iteraciones de un "entorno", a través de acciones que le llevan a cambiar ese entorno y obtener recompensas. El objetivo de este campo es la creación de estrategias o políticas que optimicen las recompensas totales obtenidas. De los muchos desafíos de este campo, uno de los que más destaca es el "Restless Multiarmed Bandit Problem", empleado en problemas re gestión de recursos con numerosas aplicaciones como el manejo de la carga de trabajo en servidores, la detección de canales de comunicación, gestión de sistemas de salud, etc. En él, múltiples procesos estocásticos evolucionan simultáneamente, los cuales pueden realizar en cada iteración dos acciones distintas. Trabajos previos han dado lugar a una política basada en la indexación de los estados de las cadenas de Markov que modelan estos problemas: los índices de Whittle. Bajo esta heurística, es posible reducir la dimensionalidad del problema, haciendo posible la obtención de políticas para incluso los problemas más complejos.En nuestro trabajo, hemos desarrollado nuevos algoritmos para el cálculo de estos índices, basados en la simulación de dos escalas de tiempo, para obtener unas condiciones de convergencia accesibles para nuestro programa.<br /

Desenvolvimento de um Guia de Princípios Éticos para aplicações no contexto de IA

Trabalho de conclusão de curso (graduação)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2021.Em um mundo onde os avanços tecnológicos são tão rápidos, a tomada de decisão para uma ágil implantação e/ou adaptação se torna também necessária, levando à criação de várias metodologias que vão auxiliar desde a idealização, passando pelo planejamento e implantação, até a finalização destes projetos que foram pensados. Com este desafio se aplicando a todo o mundo, não seria impensável que mesmo no ramo de Inteligência Ar- tificial o modelo de desenvolvimento de software ágil também estivesse incluso. Porém, neste modelo também vemos a necessidade de pensar nas implicações éticas do uso da tecnologia, que traz consigo importantes desafios a serem superados. Neste trabalho, é discutido como a aplicação da metodologia ágil impactou na criação de um guia para auxiliar os desenvolvedores e usuários finais na implantação de princípios éticos no con- texto de aplicações de Inteligência Artificial (IA). Além disso, é apresentado como foi o desenvolvimento de um guia e seu devido uso através de um protótipo, utilizando o método ECCOLA criado por Vakkuri et al. [1], gerando assim um impacto imediato para os potenciais usuários. Mostramos também como modificar o guia para uso com outros parâmetros desenvolvidos, casos de uso e possibilidades de melhorias futuras, tanto nos projetos futuros quanto no projeto da própria ferramenta.In a world where technological improvements are really quick, having the correct decision for a faster creation and/or adaption becomes more and more requested, asking for the creation of various methodologies which can help since the idealization, proceed through the planning and implementing until the end of those projects that were thought. With that challenge being applied for all the world, it would not be unthinkable that even when Artificial Intelligence area the agile software development were included. But, with this model we see how thinking on ethical implications using this technology becomes required to passthrough those challenges. On this work, we’ll discuss how the agile methodology has impacted on the creation for a guide to help developers and stakeholders implementing those ethical principles at the Artificial Intelligence context. Besides that, it is presented how the development for this guide was proceeeded and using the ECCOLA methodology, created by Vakkuri et al. [1] as a prototype for this tool, generating an immediate impact for potential users. We also show how to modify this tool to use with other methodologies with other parameters, user cases and future improvements, both on future projects and on the development