Answer Set programming (ASP) is a framework in artificial intelligence and knowledge representation for declarative modeling and problem solving. Modern ASP solvers focus on the computation or enumeration of answer se...
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ISBN:
(纸本)9781577358763
Answer Set programming (ASP) is a framework in artificial intelligence and knowledge representation for declarative modeling and problem solving. Modern ASP solvers focus on the computation or enumeration of answer sets. However, a variety of probabilistic applications in reasoning or logic programming require counting answer sets. While counting can be done by enumeration, simple enumeration becomes immediately infeasible if the number of solutions is high. On the other hand, approaches to exact counting are of high worst-case complexity. In fact, in propositional model counting, exact counting becomes impractical. In this work, we present a scalable approach to approximate counting for ASP. Our approach is based on systematically adding parity (XOR) constraints to ASP programs, which divide the search space. We prove that adding random XOR constraints partitions the answer sets of an ASP program. In practice, we use a Gaussian elimination-based approach by lifting ideas from SAT to ASP and integrate it into a state of the art ASP solver, which we call ApproxASP. Finally, our experimental evaluation shows the scalability of our approach over existing ASP systems.
In the context of social choice theory, we develop a tableau-based calculus for reasoning about voting rules. This calculus can be used to obtain structured explanations for why a given set of axioms justifies a given...
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ISBN:
(纸本)9781577358763
In the context of social choice theory, we develop a tableau-based calculus for reasoning about voting rules. This calculus can be used to obtain structured explanations for why a given set of axioms justifies a given election outcome for a given profile of voter preferences. We then show how to operationalise this calculus, using a combination of SAT solving and Answer Set programming, to arrive at a flexible framework for presenting human-readable justifications to users.
The aim of this study is to examine the effective communication process during shift changes in a transitional care unit;communication should be clear, concise and relevant. The caregivers should be able to understand...
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ISBN:
(纸本)9783031164743;9783031164736
The aim of this study is to examine the effective communication process during shift changes in a transitional care unit;communication should be clear, concise and relevant. The caregivers should be able to understand their mates needs and wants, and they should understand what is happening. Indeed, it will be study if there is inconsistency in the communication process during shift change, since there are some factors that could be affecting the consistency, such as inadequate time for communication, lack of awareness about the needs of other team members and unclear expectations from managers, a process carried out by interviewing employees who have experience in transitional care. The interviewees are asked about their past experiences and what they think are the most important points for effective communication. No doubt this article will discuss the communication challenges and opportunities that arise when a caregiver transfers care from one shift to the next, with effective communication being the key. On the other hand, in this work is also set a logic programming based framework that nurses may use to optimize their insight into how caregivers may make their communication more effective by understanding the needs of both parties involved in the shift change process.
State-of-the-art solvers for constrained Horn clauses (CHC) are successfully used to generate reachability facts from symbolic encodings of programs. In this paper, we present a new application to test-case generation...
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ISBN:
(纸本)9783030995270;9783030995263
State-of-the-art solvers for constrained Horn clauses (CHC) are successfully used to generate reachability facts from symbolic encodings of programs. In this paper, we present a new application to test-case generation: if a block of code is provably unreachable, no test case can be generated allowing to explore other blocks of code. Our new approach uses CHC to incrementally construct different program unrollings and extract test cases from models of satisfiable formulas. At the same time, a CHC solver keeps track of CHCs that represent unreachable blocks of code which makes the unrolling process more efficient. In practice, this lets our approach to terminate early while guaranteeing maximal coverage. Our implementation called HORNTINUUM exhibits promising performance: it generates high coverage in the majority of cases and spends less time on average than state-of-the-art.
logic programming emerged in the 1970s from debates concerning procedural versus declarative representations of knowledge in artificial intelligence. In those days, declarative representations were associated mainly w...
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ISBN:
(纸本)9783642405648
logic programming emerged in the 1970s from debates concerning procedural versus declarative representations of knowledge in artificial intelligence. In those days, declarative representations were associated mainly with bottom-up proof procedures, such as hyper-resolution. The development of logic programming showed that procedural representations could be obtained by applying top-down proof procedures, such as linear resolution, to declarative representations in logical form. In recent years, logic programming has become more purely declarative, with the development of answer set programming, tabling and the revival of data-log. These recent developments invite comparison with earlier attempts to reconcile procedural and declarative representations of knowledge, and raise the question whether anything has been lost.
Answer Set programming (ASP) is a logic programming based, truly declarative formalism for general purpose problem solving. Its declarative nature allows users to solve problems by defining what the solutions are inst...
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Answer Set programming (ASP) is a logic programming based, truly declarative formalism for general purpose problem solving. Its declarative nature allows users to solve problems by defining what the solutions are instead of how to find them. Complete lack of an imperative component in ASP makes creation of end user applications or integration with other systems demanding. External tools that can process and interpret the output of ASP solvers are needed. To address this issue in the case of simple applications with an input - output interaction loop we introduce a framework for iterative logic applications. Such applications consist of a core logic program that is used to evaluate user actions w.r.t. their current state and to derive a new state of the application. We take care to define the framework in a way that allows it to be used also with other formalism, especially SAT solvers. We also present a web based implementation of such framework for ASP.
Relational data modeling can often be restrictive as it provides no direct facility for modeling polymorphic types, reified relations, multi-valued attributes, and other common high-level structures in data. This crea...
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Relational data modeling can often be restrictive as it provides no direct facility for modeling polymorphic types, reified relations, multi-valued attributes, and other common high-level structures in data. This creates many challenges in data modeling and engineering tasks, and has led to the rise of more flexible NoSQL databases, such as graph and document databases. In the absence of structured schemas, however, we can neither express nor validate the intention of data models, making long-term maintenance of databases substantially more difficult. To resolve this dilemma, we argue that, parallel to the role of classical predicate logic for relational algebra, contemporary foundations of mathematics rooted in type theory can guide us in the development of powerful new high-level data models and query languages. To this end, we introduce a new polymorphic entity-relation-attribute (PERA) data model, grounded in type-theoretic principles and accessible through classical conceptual modeling, with a near-natural query language: TypeQL. We illustrate the syntax of TypeQL as well as its denotation in the PERA model, formalize our model as an algebraic theory with dependent types, and describe its stratified semantics.
Multi-label classification (MC) is a standard machine learning problem in which a data point can be associated with a set of classes. A more challenging scenario is given by hierarchical multi-label classification (HM...
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Multi-label classification (MC) is a standard machine learning problem in which a data point can be associated with a set of classes. A more challenging scenario is given by hierarchical multi-label classification (HMC) problems, in which every prediction must satisfy a given set of hard constraints expressing subclass relationships between classes. In this article, we propose C-HMCNN(h), a novel approach for solving HMC problems, which, given a network h for the underlying MC problem, exploits the hierarchy information in order to produce predictions coherent with the constraints and to improve performance. Furthermore, we extend the logic used to express HMC constraints in order to be able to specify more complex relations among the classes and propose a new model CCN(h), which extends C-HMCNN(h) and is again able to satisfy and exploit the constraints to improve performance. We conduct an extensive experimental analysis showing the superior performance of both C-HMCNN(h) and CCN(h) when compared to state-of-the-art models in both the HMC and the general MC setting with hard logical constraints.
This study focuses on the development of an analytical model to predict the long-term productivity of channel-fractured shale gas/oil wells. The accuracy was verified by comparing productivity calculated by the propos...
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This study focuses on the development of an analytical model to predict the long-term productivity of channel-fractured shale gas/oil wells. The accuracy was verified by comparing productivity calculated by the proposed model with numerical results. Sensitivity analysis was conducted to analyze significant parameters on the performance of channel fracturing. Field application of the model was conducted using production data obtained from an Eagle Ford Formation dry gas well, which was completed using channel fracturing. The procedure for estimating reservoir and stimulation parameters from production data was provided. The results indicated that the equivalent fracture width obtained from our model is consistent with the inversion of cubic law. Comparison with numerical simulations demonstrated that the proposed model might under- or overestimate well productivity, with mean absolute percentage error (MAPE) values of less than 8%. Sensitivity analysis indicated that, with the increase of fracture width, fracture half-length, and matrix permeability, the productivity of channel-fractured wells increases disproportionately. In addition, well productivity will increase as the ratio of the pillar radius to the length of channel fracture decreases, provided that the proppant pillars are stable and the fracture width is held constant. Under the conditions of smaller fracture width and larger matrix permeability, the effect of using channel fracturing to increase well productivity is more significant. However, as the fracture width becomes large, the benefits of channel fracturing will diminish. The case study indicated that the shale gas productivity estimated by the proposed model matches well with field data, with MAPE and R-2 of 12.90% and 0.93, respectively. The proposed model provides a basis for optimizing the design of channel fracturing.
This article explores the entanglement of logic and computing by focusing on the activity of writing. Although mathematical logic is sometimes cast as the immaterial spirit of the computer's material body, the stu...
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This article explores the entanglement of logic and computing by focusing on the activity of writing. Although mathematical logic is sometimes cast as the immaterial spirit of the computer's material body, the study of logic also takes place in the physical world through the manipulation of symbols on paper. Already in the 19th century, mathematical logic was understood to be related to mechanization, though not as the science behind an as-yet-uninvented technology. Rather, symbolic notations were seen as tools that opened possibilities but required new kinds of work. Turning to early electronic computing in the 1950s, I observe that researchers similarly relied on novel inscriptive techniques to mitigate labor. Finally, considering Charles Hamblin's Reverse Polish Notation, I show how logic was a source of notational invention, emerging as a practical resource for the work of writing programs independently of its role as a plausible theoretical foundation for computer science.
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