The Operating Room Scheduling (ORS) problem is the task of assigning patients to operating rooms (ORs), taking into account different specialties, lengths, and priority scores of each planned surgery, OR session durat...
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The Operating Room Scheduling (ORS) problem is the task of assigning patients to operating rooms (ORs), taking into account different specialties, lengths, and priority scores of each planned surgery, OR session durations, and the availability of beds for the entire length of stay (LOS) both in the Intensive Care Unit (ICU) and in the wards. A proper solution to the ORS problem is of primary importance for the healthcare service quality and the satisfaction of patients in hospital environments. In this paper we first present a solution to the problem based on Answer Set programming (ASP). The solution is tested on benchmarks with realistic sizes and parameters, on three scenarios for the target length on 5-day scheduling, common in small-medium-sized hospitals, and results show that ASP is a suitable solving methodology for the ORS problem in such setting. Then, we also performed a scalability analysis on the schedule length up to 15 days, which still shows the suitability of our solution also on longer plan horizons. Moreover, we also present an ASP solution for the rescheduling problem, that is, when the offline schedule cannot be completed for some reason. Finally, we introduce a web framework for managing ORS problems via ASP that allows a user to insert the main parameters of the problem, solve a specific instance, and show results graphically in real time.
Bounded treewidth is one of the most cited combinatorial invariants in the literature. It was also applied for solving several counting problems efficiently. A canonical counting problem is #Sat, which asks to count t...
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Bounded treewidth is one of the most cited combinatorial invariants in the literature. It was also applied for solving several counting problems efficiently. A canonical counting problem is #Sat, which asks to count the satisfying assignments of a Boolean formula. Recent work shows that benchmarking instances for #Sat often have reasonably small treewidth. This paper deals with counting problems for instances of small treewidth. We introduce a general framework to solve counting questions based on state-of-the-art database management systems (DBMSs). Our framework takes explicitly advantage of small treewidth by solving instances using dynamic programming (DP) on tree decompositions (TD). Therefore, we implement the concept of DP into a DBMS (PostgreSQL), since DP algorithms are already often given in terms of table manipulations in theory. This allows for elegant specifications of DP algorithms and the use of SQL to manipulate records and tables, which gives us a natural approach to bring DP algorithms into practice. To the best of our knowledge, we present the first approach to employ a DBMS for algorithms on TDs. A key advantage of our approach is that DBMSs naturally allow for dealing with huge tables with a limited amount of main memory (RAM).
An interesting feature that traditional approaches to inductive logicprogramming are missing is the ability to treat noisy and non-logical data. Neural-symbolic approaches to inductive logicprogramming have been rec...
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ISBN:
(纸本)9783031157073;9783031157066
An interesting feature that traditional approaches to inductive logicprogramming are missing is the ability to treat noisy and non-logical data. Neural-symbolic approaches to inductive logicprogramming have been recently proposed to combine the advantages of inductive logicprogramming, in terms of interpretability and generalization capability, with the characteristic capacity of deep learning to treat noisy and nonlogical data. This paper concisely surveys and briefly compares three promising neural-symbolic approaches to inductive logicprogramming that have been proposed in the last five years. The considered approaches use Datalog dialects to represent background knowledge, and they are capable of producing reusable logical rules from noisy and non-logical data. Therefore, they provide an effective means to combine logical reasoning with state-of-the-art machine learning.
Modal types-types that are derived from proof systems of modal logic-have been studied as theoretical foundations of metaprogramming, where program code is manipulated as first-class values. In modal type systems, mod...
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ISBN:
(数字)9783031300448
ISBN:
(纸本)9783031300431;9783031300448
Modal types-types that are derived from proof systems of modal logic-have been studied as theoretical foundations of metaprogramming, where program code is manipulated as first-class values. In modal type systems, modality corresponds to a type constructor for code types and controls free variables and their types in code values. Nanevski et al. have proposed contextual modal type theory, which has modal types with fine-grained information on free variables: modal types are explicitly indexed by contexts-the types of all free variables in code values. This paper presents lambda(for all(sic)), a novel extension of contextual modal type theory with parametric polymorphism over contexts. Such an extension has been studied in the literature but, unlike earlier proposals, lambda(for all(sic)) is more general in that it allows multiple occurrence of context variables in a single context. We formalize lambda(for all(sic)) with its type system and operational semantics given by beta-reduction and prove its basic properties including subject reduction, strong normalization, and confluence. Moreover, to demonstrate the expressive power of polymorphic contexts, we show a type-preserving embedding from a two-level fragment of Davies' lambda((sic)), which is based on linear-time temporal logic, to lambda(for all(sic)).
Probabilistic logicprogramming is an effective formalism for encoding problems characterized by uncertainty. Some of these problems may require the optimization of probability values subject to constraints among prob...
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Probabilistic logicprogramming is an effective formalism for encoding problems characterized by uncertainty. Some of these problems may require the optimization of probability values subject to constraints among probability distributions of random variables. Here, we introduce a new class of probabilistic logic programs, namely probabilistic optimizable logic programs, and we provide an effective algorithm to find the best assignment to probabilities of random variables, such that a set of constraints is satisfied and an objective function is optimized.
Probabilistic logicprogramming is a major part of statistical relational artificial intelligence, where approaches from logic and probability are brought together to reason about and learn from relational domains in ...
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Probabilistic logicprogramming is a major part of statistical relational artificial intelligence, where approaches from logic and probability are brought together to reason about and learn from relational domains in a setting of uncertainty. However, the behaviour of statistical relational representations across variable domain sizes is complex, and scaling inference and learning to large domains remains a significant challenge. In recent years, connections have emerged between domain size dependence, lifted inference and learning from sampled subpopulations. The asymptotic behaviour of statistical relational representations has come under scrutiny, and projectivity was investigated as the strongest form of domain size dependence, in which query marginals are completely independent of the domain size. In this contribution we show that every probabilistic logic program under the distribution semantics is asymptotically equivalent to an acyclic probabilistic logic program consisting only of determinate clauses over probabilistic facts. We conclude that every probabilistic logic program inducing a projective family of distributions is in fact everywhere equivalent to a program from this fragment, and we investigate the consequences for the projective families of distributions expressible by probabilistic logic programs.
In the theory of answer set programming, two groups of rules are called strongly equivalent if, informally speaking, they have the same meaning in any context. The relationship between strong equivalence and the propo...
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In the theory of answer set programming, two groups of rules are called strongly equivalent if, informally speaking, they have the same meaning in any context. The relationship between strong equivalence and the propositional logic of here-and-there allows us to establish strong equivalence by deriving rules of each group from rules of the other. In the process, rules are rewritten as propositional formulas. We extend this method of proving strong equivalence to an answer set programming language that includes operations on integers. The formula representing a rule in this language is a first-order formula that may contain comparison symbols among its predicate constants, and symbols for arithmetic operations among its function constants. The paper is under consideration for acceptance in TPLP.
With the rapid transition to distance learning, automatic grading software becomes more important to both teachers and students. We study the problem of automatically grading the regular expressions submitted by stude...
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ISBN:
(数字)9783031300448
ISBN:
(纸本)9783031300431;9783031300448
With the rapid transition to distance learning, automatic grading software becomes more important to both teachers and students. We study the problem of automatically grading the regular expressions submitted by students in courses related to automata and formal language theory. In order to utilize the semantic information of the regular expression, we define a declarative logic that can be described by regular language and at the same time has natural language characteristics, and use it for the following tasks: 1) to assign partial grades for incorrect regular expressions and 2) to provide helpful feedback to students to make them understand the reason for the grades and a way to revise the incorrect regular expressions into correct ones. We categorize the cases when students' incorrect submissions deserve partial grades and suggest how to assign appropriate grades for each of the cases. In order to optimize the runtime complexity of the algorithm, two heuristics based on automata theory are proposed and evaluated on the dataset collected from undergraduate students. In addition, we suggest Regex2NL which translates regular expressions to natural language descriptions to give insight to students so that they can understand how the regular expressions work.
Epistemic logic Programs (ELPs), extend Answer Set programming (ASP) with epistemic operators. The semantics of such programs is provided in terms of world views, which are sets of belief sets. Different semantic appr...
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ISBN:
(纸本)9783031157073;9783031157066
Epistemic logic Programs (ELPs), extend Answer Set programming (ASP) with epistemic operators. The semantics of such programs is provided in terms of world views, which are sets of belief sets. Different semantic approaches propose different characterizations of world views. Recent work has introduced semantic properties that should be met by any semantics for ELPs, like the Epistemic Splitting Property, that, if satisfied, allows to modularly compute world views in a bottom-up fashion, analogously to 'traditional' ASP. We analyze the possibility to change the perspective, shifting from a bottom-up to a top-down approach to splitting. Our new definition: (i) copes with concerns regarding unfoundedness of world views and subjective constraint monotonicity;(ii) is provably applicable to many of the existing semantics;(iii) operates similarly to "traditional" ASP;(iv) provably coincides with the bottom-up notion of splitting at least on the class of Epistemically Stratified Programs (which are, intuitively, those where the use of epistemic operators is stratified).
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