945 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    Toward relevant answers to queries on incomplete databases

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    Incomplete and uncertain information is ubiquitous in database management applications. However, the techniques specifically developed to handle incomplete data are not sufficient. Even the evaluation of SQL queries on databases containing NULL values remains a challenge after 40 years. There is no consensus on what an answer to a query on an incomplete database should be, and the existing notions often have limited applicability. One of the most prevalent techniques in the literature is based on finding answers that are certainly true, independently of how missing values are interpreted. However, this notion has yielded several conflicting formal definitions for certain answers. Based on the fact that incomplete data can be enriched by some additional knowledge, we designed a notion able to unify and explain the different definitions for certain answers. Moreover, the knowledge-preserving certain answers notion is able to provide the first well-founded definition of certain answers for the relational bag data model and value-inventing queries, addressing some key limitations of previous approaches. However, it doesn’t provide any guarantee about the relevancy of the answers it captures. To understand what would be relevant answers to queries on incomplete databases, we designed and conducted a survey on the everyday usage of NULL values among database users. One of the findings from this socio-technical study is that even when users agree on the possible interpretation of NULL values, they may not agree on what a satisfactory query answer is. Therefore, to be relevant, query evaluation on incomplete databases must account for users’ tasks and preferences. We model users’ preferences and tasks with the notion of regret. The regret function captures the task-dependent loss a user endures when he considers a database as ground truth instead of another. Thanks to this notion, we designed the first framework able to provide a score accounting for the risk associated with query answers. It allows us to define the risk-minimizing answers to queries on incomplete databases. We show that for some regret functions, regret-minimizing answers coincide with certain answers. Moreover, as the notion is more agile, it can capture more nuanced answers and more interpretations of incompleteness. A different approach to improve the relevancy of an answer is to explain its provenance. We propose to partition the incompleteness into sources and measure their respective contribution to the risk of answer. As a first milestone, we study several models to predict the evolution of the risk when we clean a source of incompleteness. We implemented the framework, and it exhibits promising results on relational databases and queries with aggregate and grouping operations. Indeed, the model allows us to infer the risk reduction obtained by cleaning an attribute. Finally, by considering a game theoretical approach, the model can provide an explanation for answers based on the contribution of each attributes to the risk

    Natural Language Reasoning on ALC knowledge bases using Large Language Models

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    Τα προεκπαιδευμένα γλωσσικά μοντέλα έχουν κυριαρχήσει στην επεξεργασία φυσικής γλώσσας, αποτελώντας πρόκληση για τη χρήση γλωσσών αναπαράστασης γνώσης για την περιγραφή του κόσμου. Ενώ οι γλώσσες αυτές δεν είναι αρκετά εκφραστικές για να καλύψουν πλήρως τη φυσική γλώσσα, τα γλωσσικά μοντέλα έχουν ήδη δείξει σπουδαία αποτελέσματα όσον αφορά την κατανόηση και την ανάκτηση πληροφοριών απευθείας σε δεδομένα φυσικής γλώσσας. Διερευνούμε τις επιδόσεις των γλωσσικών μοντέλων για συλλογιστική φυσικής γλώσσας στη περιγραφική λογική ALC. Δημιουργούμε ένα σύνολο δεδομένων από τυχαίες βάσεις γνώσης ALC, μεταφρασμένες σε φυσική γλώσσα, ώστε να αξιολογήσουμε την ικανότητα των γλωσσικών μοντέλων να λειτουργούν ως συστήματα απάντησης ερωτήσεων πάνω σε βάσεις γνώσης φυσικής γλώσσας.Pretrained language models have dominated natural language processing, challenging the use of knowledge representation languages to describe the world. While these lan- guages are not expressive enough to fully cover natural language, language models have already shown great results in terms of understanding and information retrieval directly on natural language data. We explore language models’ performance at the downstream task of natural language reasoning in the description logic ALC. We generate a dataset of random ALC knowledge bases, translated in natural language, in order to assess the language models’ ability to function as question-answering systems over natural language knowledge bases

    Reasoning about quantities and concepts: studies in social learning

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    We live and learn in a ‘society of mind’. This means that we form beliefs not just based on our own observations and prior expectations but also based on the communications from other people, such as our social network peers. Across seven experiments, I study how people combine their own private observations with other people’s communications to form and update beliefs about the environment. I will follow the tradition of rational analysis and benchmark human learning against optimal Bayesian inference at Marr’s computational level. To accommodate human resource constraints and cognitive biases, I will further contrast human learning with a variety of process level accounts. In Chapters 2–4, I examine how people reason about simple environmental quantities. I will focus on the effect of dependent information sources on the success of group and individual learning across a series of single-player and multi-player judgement tasks. Overall, the results from Chapters 2–4 highlight the nuances of real social network dynamics and provide insights into the conditions under which we can expect collective success versus failures such as the formation of inaccurate worldviews. In Chapter 5, I develop a more complex social learning task which goes beyond estimation of environmental quantities and focuses on inductive inference with symbolic concepts. Here, I investigate how people search compositional theory spaces to form and adapt their beliefs, and how symbolic belief adaptation interfaces with individual and social learning in a challenging active learning task. Results from Chapter 5 suggest that people might explore compositional theory spaces using local incremental search; and that it is difficult for people to use another person’s learning data to improve upon their hypothesis

    Procedural Constraint-based Generation for Game Development

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    Learning Possibilistic Logic Theories

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    Vi tar opp problemet med å lære tolkbare maskinlæringsmodeller fra usikker og manglende informasjon. Vi utvikler først en ny dyplæringsarkitektur, RIDDLE: Rule InDuction with Deep LEarning (regelinduksjon med dyp læring), basert på egenskapene til mulighetsteori. Med eksperimentelle resultater og sammenligning med FURIA, en eksisterende moderne metode for regelinduksjon, er RIDDLE en lovende regelinduksjonsalgoritme for å finne regler fra data. Deretter undersøker vi læringsoppgaven formelt ved å identifisere regler med konfidensgrad knyttet til dem i exact learning-modellen. Vi definerer formelt teoretiske rammer og viser forhold som må holde for å garantere at en læringsalgoritme vil identifisere reglene som holder i et domene. Til slutt utvikler vi en algoritme som lærer regler med tilhørende konfidensverdier i exact learning-modellen. Vi foreslår også en teknikk for å simulere spørringer i exact learning-modellen fra data. Eksperimenter viser oppmuntrende resultater for å lære et sett med regler som tilnærmer reglene som er kodet i data.We address the problem of learning interpretable machine learning models from uncertain and missing information. We first develop a novel deep learning architecture, named RIDDLE (Rule InDuction with Deep LEarning), based on properties of possibility theory. With experimental results and comparison with FURIA, a state of the art method, RIDDLE is a promising rule induction algorithm for finding rules from data. We then formally investigate the learning task of identifying rules with confidence degree associated to them in the exact learning model. We formally define theoretical frameworks and show conditions that must hold to guarantee that a learning algorithm will identify the rules that hold in a domain. Finally, we develop an algorithm that learns rules with associated confidence values in the exact learning model. We also propose a technique to simulate queries in the exact learning model from data. Experiments show encouraging results to learn a set of rules that approximate rules encoded in data.Doktorgradsavhandlin

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

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    LIPIcs, Volume 261, ICALP 2023, Complete Volum

    Scallop: A Language for Neurosymbolic Programming

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    We present Scallop, a language which combines the benefits of deep learning and logical reasoning. Scallop enables users to write a wide range of neurosymbolic applications and train them in a data- and compute-efficient manner. It achieves these goals through three key features: 1) a flexible symbolic representation that is based on the relational data model; 2) a declarative logic programming language that is based on Datalog and supports recursion, aggregation, and negation; and 3) a framework for automatic and efficient differentiable reasoning that is based on the theory of provenance semirings. We evaluate Scallop on a suite of eight neurosymbolic applications from the literature. Our evaluation demonstrates that Scallop is capable of expressing algorithmic reasoning in diverse and challenging AI tasks, provides a succinct interface for machine learning programmers to integrate logical domain knowledge, and yields solutions that are comparable or superior to state-of-the-art models in terms of accuracy. Furthermore, Scallop's solutions outperform these models in aspects such as runtime and data efficiency, interpretability, and generalizability
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