Answer Set programming (ASP) is a declarative logic formalism that allows to encode computational problems via logic programs. Despite the declarative nature of the formalism, some advanced expertise is required, in g...
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Answer Set programming (ASP) is a declarative logic formalism that allows to encode computational problems via logic programs. Despite the declarative nature of the formalism, some advanced expertise is required, in general, for designing an ASP encoding that can be efficiently evaluated by an actual ASP system. A common way for trying to reduce the burden of manually tweaking an ASP program consists in automatically rewriting the input encoding according to suitable techniques, for producing alternative, yet semantically equivalent, ASP programs. However, rewriting does not always grant benefits in terms of performance;hence, proper means are needed for predicting their effects withthis respect. In this paper we describe an approach based on Machine Learning (ML) to automatically decide whether to rewrite. In particular, given an ASP program and a set of input facts, our approach chooses whether and how to rewrite input rules based on a set of features measuring their structural properties and domain information. To this end, a Multilayer Perceptrons model has then been trained to guide the ASP grounder I -DLV on rewriting input rules. We report and discuss the results of an experimental evaluation over a prototypical implementation.
Aggregates are among the most frequently used linguistic extensions of answer set programming. the result of an aggregation may introduce new constants during the instantiation of the input program, a feature known as...
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Aggregates are among the most frequently used linguistic extensions of answer set programming. the result of an aggregation may introduce new constants during the instantiation of the input program, a feature known as value invention. When the aggregation involves literals whose truth value is undefined at instantiation time, modern grounders introduce several instances of the aggregate, one for each possible interpretation of the undefined literals. this paper introduces new data structures and techniques to handle such cases, and more in general aggregations on the same aggregate set identified in the ground program in input. the proposed solution reduces the memory footprint of the solver without sacrificing efficiency. On the contrary, the performance of the solver may improve thanks to the addition of some simple entailed clauses which are not easily discovered otherwise, and since redundant computation is avoided during propagation. Empirical evidence of the potential impact of the proposed solution is given.
the problem we want to solve is how to generate all theorems of a given size in the implicational fragment of propositional intuitionistic linear logic. We start by filtering for linearity the proof terms associated b...
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the problem we want to solve is how to generate all theorems of a given size in the implicational fragment of propositional intuitionistic linear logic. We start by filtering for linearity the proof terms associated by our Prolog-based theorem prover for Implicational Intuitionistic logic. this works, but using for each formula a PSPACE-complete algorithm limits it to very small formulas. We take a few walks back and forth over the bridge between proof terms and theorems, provided by the Curry-Howard isomorphism, and derive step-by-step an efficient algorithm requiring a low polynomial effort per generated theorem. the resulting Prolog program runs in O(N) space for terms of size N and generates in a few hours 7,566,084,686 theorems in the implicational fragment of Linear Intuitionistic logic together withtheir proof terms in normal form. As applications, we generate datasets for correctness and scalability testing of linear logictheorem provers and training data for neural networks working on theorem proving challenges. the results in the paper, organized as a literate Prolog program, are fully replicable.
Decision support systems play an important role in medical fields as they can augment clinicians to deal more efficiently and effectively with complex decision-making processes. In the diagnosis of headache disorders,...
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Decision support systems play an important role in medical fields as they can augment clinicians to deal more efficiently and effectively with complex decision-making processes. In the diagnosis of headache disorders, however, existing approaches and tools are still not optimal. On the one hand, to support the diagnosis of this complex and vast spectrum of disorders, the international Headache Society released in 1988 the international Classification of Headache Disorders (ICHD), now in its 3rd edition: a 200 pages document classifying more than 300 different kinds of headaches, where each is identified via a collection of specific nontrivial diagnostic criteria. On the other hand, the high number of headache disorders and their complex criteria make the medical history process inaccurate and not exhaustive both for clinicians and existing automatic tools. To fill this gap, we present head-asp, a novel decision support system for the diagnosis of headache disorders. through a REST Web Service, head-asp implements a dynamic questionnaire that complies withICHD-3by exploiting two logical modules to reach a complete diagnosis while trying to minimize the total number of questions being posed to patients. Finally, head-asp is freely available on-line and it is receiving very positive feedback from the group of neurologists that is testing it.
Repeated executions of reasoning tasks for varying inputs are necessary in many applicative settings, such as stream reasoning. In this context, we propose an incremental grounding approach for the answer set semantic...
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Repeated executions of reasoning tasks for varying inputs are necessary in many applicative settings, such as stream reasoning. In this context, we propose an incremental grounding approach for the answer set semantics. We focus on the possibility of generating incrementally larger ground logic programs equivalent to a given non-ground one;so called overgrounded programs can be reused in combination with deliberately many different sets of inputs. Updating overgrounded programs requires a small effort, thus making the instantiation of logic programs considerably faster when grounding is repeated on a series of inputs similar to each other. Notably, the proposed approach works "under the hood", relieving designers of logic programs from controlling technical aspects of grounding engines and answer set systems. In this work we present the theoretical basis of the proposed incremental grounding technique, we illustrate the consequent repeated evaluation strategy and report about our experiments.
the flexibility of Programmable logic Devices (PLDs) and their supporting CAD (Computer Aided Design) tools make PLDs ideal for digital systems design courses. this paper outlines a computer design-based course and su...
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ISBN:
(纸本)9781424402564
the flexibility of Programmable logic Devices (PLDs) and their supporting CAD (Computer Aided Design) tools make PLDs ideal for digital systems design courses. this paper outlines a computer design-based course and supporting laboratory targeted for students with intermediate-level digital design skills. the unifying theme of this course is the design, implementation, and programming of a relatively complex microcontroller (MCU). this course emphasizes the development and subsequent integration of basic individual computer building blocks using a Hardware Description Language (HDL). the major topics in the course include Finite State Machine (FSM) design, computer system architecture, and assembly language programming. Placing emphasis on a hierarchical design approach and ensuring continuity in the course topics reduces potential difficulties in presenting this scope of topics.
Qualification has been recently introduced as a generalization Of uncertainty in the field of logicprogramming. In this paper we investigate a more expressive language for First-Order Functional logicprogramming wit...
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ISBN:
(纸本)9783642028458
Qualification has been recently introduced as a generalization Of uncertainty in the field of logicprogramming. In this paper we investigate a more expressive language for First-Order Functional logicprogramming with Constraints and Qualification. We present a Rewriting logic which characterizes the intended semantics of programs, and a prototype implementation based on a semantically correct, program transformation. Potential applications of the resulting language include flexible information retrieval. As a concrete illustration, we show how to write program rules to compute qualified answers for user queries concerning the books available in a given library.
In this paper we propose an extension of Answer Set programming (ASP) [1], and in particular, of its most general logical counterpart, Quantified Equilibrium logic (QEL) [2], to deal with partial functions. Although t...
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ISBN:
(纸本)9783540899815
In this paper we propose an extension of Answer Set programming (ASP) [1], and in particular, of its most general logical counterpart, Quantified Equilibrium logic (QEL) [2], to deal with partial functions. Although the treatment of equality in QEL can be established in different ways, we first analyse the choice of decidable equality with complete functions and Herbrand models, recently proposed in the literature [3]. We argue that this choice yields some counterintuitive effects from a logicprogramming and knowledge representation point of view. We then propose a variant called QEL=(F) where the set of functions is partitioned into partial and Herbrand functions (we also call constructors). In the rest of the paper, we show a direct connection to Scott's logic of Existence [4] and present a practical application, proposing an extension of normal logic programs to deal with partial functions and equality, so that they can be translated into function-free normal programs, being possible in this way to compute their answer sets with any standard ASP solver.
Recently, enabling modularity aspects in Answer Set programming (ASP) has gained increasing interest to ease the composition of program parts to an overall program. In this paper, we focus on modular nonmonotonic logi...
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ISBN:
(纸本)9783642028458
Recently, enabling modularity aspects in Answer Set programming (ASP) has gained increasing interest to ease the composition of program parts to an overall program. In this paper, we focus on modular nonmonotonic logic programs (MLP) under the answer set semantics, whose modules may have contextually dependent input provided by other modules. Moreover, (Mutually) recursive module calls are allowed. We define a model-theoretic semantics for this extended setting, show that many desired properties of ordinary logicprogramming generalize to our modular ASP, and determine the computational complexity of the new formalism. We investigate the relationship of modular programs to disjunctive logic programs with well-defined input/output interface (DLP-functions) and show that they can be embedded into MLPs.
Withthe retention life of data increasing, many organizations are trying to determine the best long-term storage strategy for the future. To do this, organizations must analyze the reliability, security and speed of ...
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