Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice

Necessity and sufficiency are the building blocks of all successful explanations. Yet despite their importance, these notions have been conceptually underdeveloped and inconsistently applied in explainable artificial intelligence (XAI), a fast-growing research area that is so far lacking in firm theoretical foundations. In this article, an expanded version of a paper originally presented at the 37th Conference on Uncertainty in Artificial Intelligence (Watson et al., 2021), we attempt to fill this gap. Building on work in logic, probability, and causality, we establish the central role of necessity and sufficiency in XAI, unifying seemingly disparate methods in a single formal framework. We propose a novel formulation of these concepts, and demonstrate its advantages over leading alternatives. We present a sound and complete algorithm for computing explanatory factors with respect to a given context and set of agentive preferences, allowing users to identify necessary and sufficient conditions for desired outcomes at minimal cost. Experiments on real and simulated data confirm our method’s competitive performance against state of the art XAI tools on a diverse array of tasks

Computer Science and Technology Series : XV Argentine Congress of Computer Science. Selected papers

CACIC'09 was the fifteenth Congress in the CACIC series. It was organized by the School of Engineering of the National University of Jujuy. The Congress included 9 Workshops with 130 accepted papers, 1 main Conference, 4 invited tutorials, different meetings related with Computer Science Education (Professors, PhD students, Curricula) and an International School with 5 courses. CACIC 2009 was organized following the traditional Congress format, with 9 Workshops covering a diversity of dimensions of Computer Science Research. Each topic was supervised by a committee of three chairs of different Universities. The call for papers attracted a total of 267 submissions. An average of 2.7 review reports were collected for each paper, for a grand total of 720 review reports that involved about 300 different reviewers. A total of 130 full papers were accepted and 20 of them were selected for this book.Red de Universidades con Carreras en Informática (RedUNCI

Learning in evolutionary environments

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Efficient knowledge retrieval to calibrate input variables in forest fire prediction

Forest fires are a serious threat to humans and nature from an ecological, social and economic point of view. Predicting their behaviour by simulation still delivers unreliable results and remains a challenging task. Latest approaches try to calibrate input variables, often tainted with imprecision, using optimisation techniques like Genetic Algorithms. To converge faster towards fitter solutions, the GA is guided with knowledge obtained from historical or synthetical fires. We developed a robust and efficient knowledge storage and retrieval method. Nearest neighbour search is applied to find the fire configuration from knowledge base most similar to the current configuration. Therefore, a distance measure was elaborated and implemented in several ways. Experiments show the performance of the different implementations regarding occupied storage and retrieval time with overly satisfactory results.Los incendios forestales son una grave amenaza para seres humanos y para la naturalza desde el punto de vista ecológico, social y económico. Predecir su comportamiento usando simulaciones todavía da resultados poco fiables y sigue siendo una tarea desafiante. Trabajos más recientes, intentan calibrar variables de entrada, muchas veces imprecisas, aplicando técnicas de optimización como algoritmos genéticos. Para converger más rápido hacia soluciones más adecuadas, el algoritmo genético es guiado con conocimiento obtenido de fuegos históricos o sintéticos. Hemos desarrollado un método robusto y eficiente para almacenar y recuperar ese conocimiento. Aplicamos la búsqueda del vecino más cercano para encontrar la configuración del fuego más similar a la configuración actual dentro de la base de conocimiento. Para esto, hemos elaborado una función de distancia y la hemos implementado de diferentes maneras. Experimentos muestran el rendimiento de las distintas implementaciones considerando el almacenamiento ocupado y el tiempo de recuperación con resultados muy satisfactorios.Els incendis forestals són una amenaça important tant pels homes com per a la natura des d'un punt de vista ecològic, social i econòmic. La predicció del comportament dels incendis forestals utilitzant simulació encara genera resultats poc fiables i, per tant, segueix essent un desafiament important. Aproximacions recents a aquest problema, intenten calibrar les variables d'entrada dels simuladors, les quals sovint presenten un grau important d'incertesa, utilitzant tècniques d'optimització com poden ser els Algoritmes Genètics (AG). Per tal de que la convergència dels AG a una solució bona sigui ràpida, l'AG es guia mitjançant el coneixement obtingut d'històrics d'incendis o focs sintètics. Per aquest treball s'ha desenvolupat un mètode eficient i robust d'emmagatzemament i recuperació del coneixement. El mètode anomenat Nearest Neighbour Search s'aplica per trobar la configuracióo guardada en la base de coneixements que més s'assembli a la configuracióo real de l'incendi. Per a tal efecte, s'ha desenvolupat una mètrica de distància la qual ha estat implementada de diferents formes alternatives. L'experimentació realitzada mostra resultats encoratjadors en el rendiment de les diferents implementacions tenint en compte l'emmagatzemament ocupat i el temps de recuperació de la informació

Semantically-enhanced recommendations in cultural heritage

In the Web 2.0 environment, institutes and organizations are starting to open up their previously isolated and heterogeneous collections in order to provide visitors with maximal access. Semantic Web technologies act as instrumental in integrating these rich collections of metadata by defining ontologies which accommodate different representation schemata and inconsistent naming conventions over the various vocabularies. Facing the large amount of metadata with complex semantic structures, it is becoming more and more important to support visitors with a proper selection and presentation of information. In this context, the Dutch Science Foundation (NWO) funded the Cultural Heritage Information Personalization (CHIP) project in early 2005, as part of the Continuous Access to Cultural Heritage (CATCH) program in the Netherlands. It is a collaborative project between the Rijksmuseum Amsterdam, the Eindhoven University of Technology and the Telematica Instituut. The problem statement that guides the research of this thesis is as follows: Can we support visitors with personalized access to semantically-enriched collections? To study this question, we chose cultural heritage (museums) as an application domain, and the semantically rich background knowledge about the museum collection provides a basis to our research. On top of it, we deployed user modeling and recommendation technologies in order to provide personalized services for museum visitors. Our main contributions are: (i) we developed an interactive rating dialog of artworks and art concepts for a quick instantiation of the CHIP user model, which is built as a specialization of FOAF and mapped to an existing event model ontology SEM; (ii) we proposed a hybrid recommendation algorithm, combining both explicit and implicit relations from the semantic structure of the collection. On the presentation level, we developed three tools for end-users: Art Recommender, Tour Wizard and Mobile Tour Guide. Following a user-centered design cycle, we performed a series of evaluations with museum visitors to test the effectiveness of recommendations using the rating dialog, different ways to build an optimal user model and the prediction accuracy of the hybrid algorithm. Chapter 1 introduces the research questions, our approaches and the outline of this thesis. Chapter 2 gives an overview of our work at the first stage. It includes (i) the semantic enrichment of the Rijksmuseum collection, which is mapped to three Getty vocabularies (ULAN, AAT, TGN) and the Iconclass thesaurus; (ii) the minimal user model ontology defined as a specialization of FOAF, which only stores user ratings at that time, (iii) the first implementation of the content-based recommendation algorithm in our first tool, the CHIP Art Recommender. Chapter 3 presents two other tools: Tour Wizard and Mobile Tour Guide. Based on the user's ratings, the Web-based Tour Wizard recommends museum tours consisting of recommended artworks that are currently available for museum exhibitions. The Mobile Tour Guide converts recommended tours to mobile devices (e.g. PDA) that can be used in the physical museum space. To connect users' various interactions with these tools, we made a conversion of the online user model stored in RDF into XML format which the mobile guide can parse, and in this way we keep the online and on-site user models dynamically synchronized. Chapter 4 presents the second generation of the Mobile Tour Guide with a real time routing system on different mobile devices (e.g. iPod). Compared with the first generation, it can adapt museum tours based on the user's ratings artworks and concepts, her/his current location in the physical museum and the coordinates of the artworks and rooms in the museum. In addition, we mapped the CHIP user model to an existing event model ontology SEM. Besides ratings, it can store additional user activities, such as following a tour and viewing artworks. Chapter 5 identifies a number of semantic relations within one vocabulary (e.g. a concept has a broader/narrower concept) and across multiple vocabularies (e.g. an artist is associated to an art style). We applied all these relations as well as the basic artwork features in content-based recommendations and compared all of them in terms of usefulness. This investigation also enables us to look at the combined use of artwork features and semantic relations in sequence and derive user navigation patterns. Chapter 6 defines the task of personalized recommendations and decomposes the task into a number of inference steps for ontology-based recommender systems, from a perspective of knowledge engineering. We proposed a hybrid approach combining both explicit and implicit recommendations. The explicit relations include artworks features and semantic relations with preliminary weights which are derived from the evaluation in Chapter 5. The implicit relations are built between art concepts based on instance-based ontology matching. Chapter 7 gives an example of reusing user interaction data generated by one application into another one for providing cross-application recommendations. In this example, user tagging about cultural events, gathered by iCITY, is used to enrich the user model for generating content-based recommendations in the CHIP Art Recommender. To realize full tagging interoperability, we investigated the problems that arise in mapping user tags to domain ontologies, and proposed additional mechanisms, such as the use of SKOS matching operators to deal with the possible mis-alignment of tags and domain-specific ontologies. We summarized to what extent the problem statement and each of the research questions are answered in Chapter 8. We also discussed a number of limitations in our research and looked ahead at what may follow as future work

Learning in Evolutionary Environments

The purpose of this work is to present a sort of short selective guide to an enormous and diverse literature on learning processes in economics. We argue that learning is an ubiquitous characteristic of most economic and social systems but it acquires even greater importance in explicitly evolutionary environments where: a) heterogeneous agents systematically display various forms of "bounded rationality"; b) there is a persistent appearance of novelties, both as exogenous shocks and as the result of technological, behavioural and organisational innovations by the agents themselves; c) markets (and other interaction arrangements) perform as selection mechanisms; d) aggregate regularities are primarily emergent properties stemming from out-of-equilibrium interactions. We present, by means of examples, the most important classes of learning models, trying to show their links and differences, and setting them against a sort of ideal framework of "what one would like to understand about learning...". We put a signifiphasis on learning models in their bare-bone formal structure, but we also refer to the (generally richer) non-formal theorising about the same objects. This allows us to provide an easier mapping of a wide and largely unexplored research agenda.Learning, Evolutionary Environments, Economic Theory, Rationality

Mesh adaptation for high-order flow simulations

Mesh adaptation has only been considered for high-order flow simulations in recent years and many techniques are still to be made more robust and efficient with curvilinear meshes required by these high-order methods. This thesis covers the developments made to improve the mesh generation and adaptation capabilities of the open-source spectral/hp element framework Nektar++ and its dedicated mesh utility NekMesh. This thesis first covers the generation of quality initial meshes typically required before an iterative adaptation procedure can be used. For optimal performance of the spectral/hp element method, quadrilateral and hexahedral meshes are preferred and two methods are presented to achieve this, either entirely or partially. The first method, inspired from cross field methods, solves a Laplace problem to obtain a guiding field from which a valid two-dimensional quadrilateral block decomposition can be automatically obtained. In turn, naturally curved meshes are generated. The second method takes advantage of the medial axis to generate structured partitions in the boundary layer region of three-dimensional domains. The method proves to be robust in generating hybrid high-order meshes with boundary layer aligned prismatic elements near boundaries and tetrahedral elements elsewhere. The thesis goes on to explore the adaptation of high-order meshes for the simulation of flows using a spectral/hp element formulation. First a new approach to moving meshes, referred to here as r-adaptation, based on a variational framework, is described. This new r-adaptation module is then enhanced by p-adaptation for the simulation of compressible inviscid flows with shocks. Where the flow is smooth, p-adaptation is used to benefit from the spectral convergence of the spectral/hp element methods. Where the flow is discontinuous, e.g. at shock waves, r-adaptation clusters nodes together to better capture these field discontinuities. The benefits of this dual, rp-adaptation approach are demonstrated through two-dimensional benchmark test cases.Open Acces