322,917 research outputs found
Ensuring trustworthy and ethical behavior in intelligent logical agents?
No full textAutonomous Intelligent Agents are employed in many important autonomous applications upon which the life and welfare of living beings and vital social functions may depend. Therefore, agents should be trustworthy. Apriori certification techniques can be useful, but are not sufficient for agents that evolve, and thus modify their epistemic and belief state. In this paper we propose/ refine/extend techniques for run-time assurance, based upon introspective self-monitoring and checking. The aim is to build a 'toolkit' to allow an agent designer/developer to ensure trustworthy and ethical behavior
Computing Uniform Interpolants for EUF via (conditional) DAG-based Compact Representations
No full textThe concept of a uniform interpolant for a quantifier-free formula from a given formula with a list of symbols, while well-known in the logic literature, has been unknown to the formal methods and automated reasoning community. This concept is precisely defined. Two algorithms for computing the uniform interpolant of a quantifier-free formula in EUF endowed with a list of symbols to be eliminated are proposed. The first algorithm is non-deterministic and generates a uniform interpolant expressed as a disjunction of conjunction of literals, whereas the second algorithm gives a compact representation of a uniform interpolant as a conjunction of Horn clauses. Both algorithms exploit efficient dedicated DAG representations of terms. Correctness and completeness proofs are supplied, using arguments combining rewrite techniques with model theory
Logic-based machine learning for transparent ethical agents
No full textAutonomous intelligent agents are increasingly engaging in human communities. Thus, they must be expected to follow social and ethical norms of the community in which they are deployed in. In this work we present an approach for developing such ethical agents which are able to develop ethical decision making and judgment capabilities by learning from interactions with the users. Our approach is a logic-based approach and the resulting ethical agents are transparent by design.Autonomous intelligent agents are increasingly engaging in human communities. Thus, they must be expected to follow social and ethical norms of the community in which they are deployed in. In this work we present an approach for developing such ethical agents which are able to develop ethical decision making and judgment capabilities by learning from interactions with the users. Our approach is a logic-based approach and the resulting ethical agents are transparent by design.Autonomous intelligent agents are increasingly engaging in human communities. Thus, they must be expected to follow social and ethical norms of the community in which they are deployed in. In this work we present an approach for developing such ethical agents which are able to develop ethical decision making and judgment capabilities by learning from interactions with the users. Our approach is a logic-based approach and the resulting ethical agents are transparent by design
Data reduction and data visualization for automatic diagnosis using gene expression and clinical data
No full textAccurate diagnoses of specific diseases require, in general, the review of the whole medical history of a patient. Currently, even though many advances have been made for disease monitoring, domain experts are still requested to perform direct analyses in order to get a precise classification, thus implying significant efforts and costs. In this work we present a framework for automated diagnosis based on high-dimensional gene expression and clinical data. Given that high-dimensional data can be difficult to analyze and computationally expensive to process, we first perform data reduction to transform high-dimensional representations of data into a lower dimensional space, yet keeping them meaningful for our purposes. We used then different data visualization techniques to embed complex pieces of information in 2-D images, that are in turn used to perform diagnosis relying on deep learning approaches. Experimental analyses show that the proposed method achieves good performance, featuring a prediction Recall value between 91% and 99%
Understanding Automatic Diagnosis and Classification Processes with Data Visualization
No full textProviding accurate diagnosis of diseases generally requires complex analyses of many clinical, biological and pathological variables. In this context, solutions based on machine learning techniques achieved relevant results in specific disease detection and classification, and can hence provide significant clinical decision support. However, such approaches suffer from the lack of proper means for interpreting the choices made by the models, especially in case of deep-learning ones. In order to improve interpretability and explainability in the process of making qualified decisions, we designed a system that allows for a partial opening of this black box by means of proper investigations on the rationale behind the decisions; this can provide improved understandings into which pre-processing steps are crucial for better performance. We tested our approach over artificial neural networks trained for automatic medical diagnosis based on high-dimensional gene expression and clinical data. Our tool analyzed the internal processes performed by the networks during the classification tasks in order to identify the most important elements involved in the training process that influence the network's decisions.We report the results of an experimental analysis aimed at assessing the viability of the proposed approach
A Natural Deduction Calculus for Godel-Dummett Logic Internalizing Proof-search Control Mechanisms
No full textWe introduce a natural deduction calculus for the G ̈odelDummett Logic LC semantically characterized by linearly ordered Kripke models. Our calculus is inspired by an analogous calculus for Intuitionistic logic (IPL) internalizing mechanisms to reduce the proof-search space that has been used to define a goal-oriented proof-search procedure for IPL. In this paper we present the calculus for LC and we sketch its soundness and completeness
Reasoning on Information Term Semantics with ASP for Constructive ELꓕ
No full textConstructive description logics represent different re-interpretations of description logics (DLs) under constructive semantics. Constructive description logics have been mostly studied for their formal properties, while limited practical approaches have been shown for their use in Knowledge Representation languages and tools (which, on the other hand, constitute the distinctive applications of description logics). To address this aspect, we recently studied the relation of constructive DLs based on Information Term semantics with Answer Set semantics in the context of the positive logic EL. In this paper we continue this study in the direction of more expressive DLs by considering the introduction of negative information, leading to a constructive interpretation for the DL EL⊥. We show that formal results linking the constructive semantics to answer set semantics can be extended to the case of negative information in EL⊥
A narrative review of consolidation strategies for young and fit patients with newly-diagnosed primary central nervous system lymphoma
No full textIntroduction: The modern treatment of patients with primary central nervous system lymphoma (PCNSL) consists of two phases: induction, currently represented by a high-dose-methotrexate-based polychemotherapy, and consolidation. The optimal consolidation therapy has not been defined yet, but several strategies, such as whole-brain radiotherapy (WBRT), high-dose chemotherapy supported by autologous stem cell transplantation (HDC/ASCT) or nonmyeloablative chemotherapy, have been addressed in important randomized trials. Areas covered: This review provides an overview of the current role of consolidation strategies in young and fit patients with newly diagnosed PCNSL. Publications in English language, peer-reviewed, from high-quality international journals, edited from 2003 to 2021 were identified on PubMed. Expert opinion: Consolidation treatment significantly improved outcomes of PCNSL. Radiotherapy had represented for years the only choice in the consolidation therapy, but large randomized trials have demonstrated that HDC/ASCT is equally effective and associated with lower neurotoxicity risk in patients younger than 65–70 years. Encouraging results have been obtained using reduced-dose WBRT, while a recent randomized trial failed to demonstrate that consolidation with nonmyeloablative chemotherapy is more effective than HDC/ASCT in PCNSL patients. A personalized consolidation treatment, driven also by a response prediction model based on radiological and molecular details, may improve the management of PCNSL patients
MutantChick: Type-Preserving Mutation Analysis for Coq
No full textWe present MutantChick, a mutation analysis tool for Coq to be used in combination with QuickChick to evaluate the fault detection capability of propertybased testing in a proof assistant. Mutation analysis of Coq theories is implemented via metaprogramming with MetaCoq and it is by construction type-preserving
Structural Positional Encoding for knowledge integration in transformer-based medical process monitoring
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