Although substantial progress has been made in the automation of many areas of systems biology, from data processing and model building to experimentation, comparatively little work has been done on integrated systems...
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ISBN:
(纸本)9783319234014;9783319234007
Although substantial progress has been made in the automation of many areas of systems biology, from data processing and model building to experimentation, comparatively little work has been done on integrated systems that combine all of these aspects. this paper presents an active learning system, "Huginn", that integrates experiment design and model revision in order to automate scientific reasoning about Metabolic Network Models. We have validated our approach in a simulated environment using substantial test cases derived from a state-of-the-art model of yeast metabolism. We demonstrate that Huginn can not only improve metabolic models, but that it is able to both solve a wider range of biochemical problems than previous methods, and to utilise a wider range of experiment types. Also, we show how design of extended crucial experiments can be automated using Abductive logicprogramming for the first time.
We present a computing model based on the DNA strand displacement technique which performs Bayesian inference. the model will take single stranded DNA as input data, representing the presence or absence of a specific ...
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Multi-context systems are a declarative formalism for interlinking knowledge-based systems (contexts) that interact via (possibly nonmonotonic) bridge rules. Interlinking knowledge provides ample opportunity for unexp...
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We present a program logic for reasoning about resource consumption of programs written in Grail, an abstract fragment of the Java Virtual Machine Language. Serving as the target logic of a certifying compiler, the lo...
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In answer-set programming (ASP), the solutions of a problem are encoded in dedicated models, called answer sets, of a logical theory. these answer sets are computed from the program that represents the theory by means...
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Representing and reasoning spatial and temporal information is a key research issue in Computer Science and Artificial Intelligence. In this paper, we introduce tools that produce three novel encodings which translate...
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ISBN:
(纸本)9781479902279
Representing and reasoning spatial and temporal information is a key research issue in Computer Science and Artificial Intelligence. In this paper, we introduce tools that produce three novel encodings which translate problems in qualitative spatial and temporal reasoning into logic programs for answer set programming solvers. Each encoding reflects a different type of modeling abstraction. We evaluate our approach with two of the most well known qualitative spatial and temporal reasoning formalisms, the Interval Algebra and Region Connection Calculus. Our results show some surprising findings, including the strong performance of the solver for disjunctive logic programs over the non-disjunctive ones on our benchmark problems.
In contemporary societies higher education must shape individuals able to solve problems in a workable and simpler manner and, therefore, a multidisciplinary view of the problems, with insights in disciplines like psy...
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ISBN:
(纸本)9783319401652;9783319401645
In contemporary societies higher education must shape individuals able to solve problems in a workable and simpler manner and, therefore, a multidisciplinary view of the problems, with insights in disciplines like psychology, mathematics or computer science becomes mandatory. Undeniably, the great challenge for teachers is to provide a comprehensive training in General Chemistry with high standards of quality, and aiming not only at the promotion of the student's academic success, but also at the understanding of the competences/skills required to their future doings. thus, this work will be focused on the development of an intelligent system to assess the Quality-of-General-Chemistry-Learning, based on factors related with subject, teachers and students.
this paper describes a number of hyperresolution-based decision procedures for a subfragment of the guarded fragment. We first present a polynomial space decision procedure of optimal worst-case space and time complex...
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While remarkable recent developments in deep neural networks have significantly contributed to advancing the state-of-the-art in Computer Vision (CV), several studies have also shown their limitations and defects. In ...
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ISBN:
(纸本)9783031711664;9783031711671
While remarkable recent developments in deep neural networks have significantly contributed to advancing the state-of-the-art in Computer Vision (CV), several studies have also shown their limitations and defects. In particular, CV models often make systematic errors on important subsets of data called slices, which are groups of data sharing a set of attributes. the slice discovery problem involves detecting semantically meaningful slices on which the model performs poorly, called rare slices. We propose a modular Neurosymbolic AI approach whose distinct advantage is the extraction of human-readable logical rules that describe rare slices, and thus enhances explainability of CV models. To this end, we present a methodology to induce rare slice occurrences in a model. Experiments on datasets from our data generator leveraging on Super-CLEVR show that the approach can correctly identify rare slices and produce logical rules describing them. the rules can be fruitfully used to generate new training data to mend model behavior or may be integrated into the model to enhance its inference capabilities. (the code for reproducing our experiments is available as an online repository: https://***/kbs/nesy- ai/ilp4sd).
Communication in multiagent systems (MASs) is usually governed by agent communication languages (ACLs) and communication protocols carrying a clear cut semantics. With an increasing degree of openness, however, the ne...
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Communication in multiagent systems (MASs) is usually governed by agent communication languages (ACLs) and communication protocols carrying a clear cut semantics. With an increasing degree of openness, however, the need arises for more flexible models of communication that can handle the uncertainty associated withthe fact that adherence to a supposedly agreed specification of possible conversations cannot be ensured on the side of other agents. As one example for such a model, interaction frames follow an empirical semantics view of communication, where meaning is defined in terms of expected consequences, and allow for a combination of existing expectations with empirical observation of how communication is used in practice. In this paper, we use methods from the fields of case-based reasoning, inductive logicprogramming and cluster analysis to devise a formal scheme for the acquisition and adaptation of interaction frames from actual conversations, enabling agents to autonomously (i.e. independent of users and system designers) create and maintain a concise model of the different classes of conversation in a MAS on the basis of an initial set of ACL and protocol specifications. this resembles the first rigorous attempt to solve this problem that is decisive for building truly autonomous agents. Copyright 2005 ACM.
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