Complex Event Recognition (CER) systems detect event occurrences in streaming input using predefined event patterns. Techniques that learn event patterns from data are highly desirable in CER. Since such patterns are ...
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Answer Set programming is an automated reasoning technology that has become a prime candidate for solving knowledge-intense search and optimization problems. One of the main reasons of its success is the availability ...
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Answer Set programming is an automated reasoning technology that has become a prime candidate for solving knowledge-intense search and optimization problems. One of the main reasons of its success is the availability of highly effective solvers that can go toe-to-toe with Satisfiability Solvers while dealing with a high-level human understandable language. Epistemic logic programs are an extension of Answer Set programming with subjective literals that allow to succinctly represent several problems that cannot be represented using the standard language of Answer Set programming. eclingo is a solver developed to solve problems described in the language of Epistemic logic Programs. This research aims to enhance the efficiency of such solver. The focus of the research will be aimed at the use of the metaprogramming capabilities of Answer Set programming solver clingo. This will allow us to enhance the solver with new inference rules expressed in the Answer Set programming language. This will reduce the search space and, in principle, improve solver performance.
Predictive maintenance plays a key role in the core business of the industry due to its potential in reducing unexpected machine downtime and related cost. To avoid such issues, it is crucial to devise artificial inte...
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It is now widely acknowledged that working in marketable banking (MB) can be a major source of stress. Meeting overly ambitious commercial targets or adapting to changes in the industry can often result in stressful s...
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Machine learning (ML) models are ubiquitous: we encounter them when using a search engine, behind online text translation, etc. However, these models have to be used with care, as they are susceptible to social biases...
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ISBN:
(纸本)9781450392471
Machine learning (ML) models are ubiquitous: we encounter them when using a search engine, behind online text translation, etc. However, these models have to be used with care, as they are susceptible to social biases. Further, most ML models are inherently opaque, another obstacle to understand and verify *** concerned with meaningful explanations, this work is putting forward two research paths: constructing counterfactual explanations with prior knowledge, and reasoning over explanations and time. Prior knowledge has the potential to significantly increase explanation quality, whereas time dimensions are necessary to track changes in ML models and explanations. The proposal builds on (constraint) logic programming and meta-reasoning. While situated in the computer sciences, it strives to reflect the interdisciplinary character of the field of eXplainable Artificial Intelligence.
Extensions of Answer Set programming with language constructs from temporal logics, such as temporal equilibrium logic over finite traces (TELƒ), provide an expressive computational framework for modeling dynamic appl...
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Extensions of Answer Set programming with language constructs from temporal logics, such as temporal equilibrium logic over finite traces (TELƒ), provide an expressive computational framework for modeling dynamic applications. In this paper, we study the so-called past-present syntactic subclass, which consists of a set of logic programming rules whose body references to the past and head to the present. Such restriction ensures that the past remains independent of the future, which is the case in most dynamic domains. We extend the definitions of completion and loop formulas to the case of past-present formulas, which allows capturing the temporal stable models of a set of past-present temporal programs by means of an LTLƒexpression. 2023 Copyright for this paper by its authors.
Artificial society is a discipline to study mechanisms of social system and phenomena which the mechanisms make. Emergence is global phenomena occurred by local mechanisms, such as, by collective behavior of autonomou...
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Artificial society is a discipline to study mechanisms of social system and phenomena which the mechanisms make. Emergence is global phenomena occurred by local mechanisms, such as, by collective behavior of autonomous agents. Understanding of emergence phenomena is a challenging subject. In this paper we use the framework of inductive logic programming (ILP) for artificial society and emergence behavior study. ILP is a branch of machine learning based on logic programming and inductive inference. We investigate the possibility of ILP in artificial society study. ILP and logic programming technique are applied to representation of an artificial society model, called Sugarscape, and to rule learning for agent behavior. Although classical ILP algorithms target classification problems, the proposed algorithm grows behavior rule for an evaluation measurement. Phenomena which this paper treate is limited but we showed that ILP technique can be applied to study in the field of artificial society.
作者：
Lima, RuiFdez-Riverola, FlorentinoCapita, AntonioBorges, IsabelVicente, HenriqueNeves, JoseCESPU
Inst Politecn Saude Norte Famalicao Portugal Univ Vigo
ESEI Escuela Super Ingn Informat Dept Informat Campus Univ As Lagoas Orense Spain SERGAS UVIGO
SING Res Grp Galicia Sur Hlth Res Inst IIS Galicia Sur Pontevedra Spain Univ Inst Hlth Sci IUCS
CESPU Dept Sci IINFACTS Inst Res & Adv Training Hlth Sci & Techn P-4585116 Gandra Crl Portugal CEGOT
Ctr Invest Desenvolvimento & Inovacao Inst Estudos Super Fafe CIDI IESF Porto Portugal REMIT
Porto Portugal Univ Evora
REQUIMTE LAQV Dept Quim & Bioquim Escola Ciencias & Tecnol Evora Portugal Univ Minho
Ctr Algoritmi Braga Portugal
Job satisfaction is an important factor in hospitality industry, figuratively, its chimera. A concept has not a single cause;rather, it is the product of elements such as conditions and relationships that determine th...
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ISBN:
(纸本)9789811697012;9789811697005
Job satisfaction is an important factor in hospitality industry, figuratively, its chimera. A concept has not a single cause;rather, it is the product of elements such as conditions and relationships that determine the workplace, the organizational system of employment, and social, cultural, and economic uncertainties. For example, and as its antonym, Maslach defined burnout "as a psychological syndrome involving emotional exhaustion, depersonalization, and a diminished sense of personal accomplishment that occurred among various professionals who work with other people in challenging situations". This multidimensional approach describes the plight of a three-dimensional concept that is derived empirically, has a similarity with stress, adds an important social dimension to emotional issues such as exhaustion, depersonalization, and challenging situations, i.e., there is the perception that the entropic state that each kitchen staff team member conveys is relevant for an appropriate personnel management of the kitchen staff team, and mirrors the social dimension that is object of study in this work.
The rapid growth of the Web and the information overload problem demand the development of practical information extraction (IE) solutions for web content processing. Ontology Population (OP) concerns both the extract...
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The rapid growth of the Web and the information overload problem demand the development of practical information extraction (IE) solutions for web content processing. Ontology Population (OP) concerns both the extraction and classification of instances of the concepts and relations defined by an ontology. Developing IE rules for OP is an intensive and time-consuming process. Thus, an automated mechanism, based on machine-learning techniques, able to convert textual data from web pages into ontology instances may be a crucial path. This paper presents an inductive logic programming-based method that automatic induces symbolic extraction rules, which are used for populating a domain ontology with instances of entity classes. This method uses domain-independent linguistic patterns for retrieving candidate instances from web pages, and a WordNet semantic similarity measure as background knowledge to be used as input by a generic inductive logic programming system. Experiments were conducted concerning both the instance classification problem and a comparison with other popular machine learning algorithms, with encouraging results.
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