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Supporting decision making in urban wastewater systems using a knowledge-based approach

ENVIRONMENTAL MODELLING & SOFTWARE 2011年第5期26卷 562-572页

作者： Aulinas, Montse Carlos Nieves, Juan Cortes, Ulises Poch, Mane Univ Girona Lab Chem & Environm Engn LEQUIA E-17003 Girona Spain Tech Univ Catalonia KEMLG Software Dept LSI E-08034 Barcelona Spain Univ Girona ICRA E-17003 Girona Spain

The use of knowledge-based systems has been shown to be a suitable approach to support decision making in environmental systems. Capturing and managing the huge quantity of data/information that has to be considered is an intrinsic factor that makes environmental systems a sophisticated domain. Organizing this data in a naive way can impact the efficacy of any knowledge-based system. Another intrinsic factor is the variety of data sources, which can result in inconsistent, uncertain or incomplete knowledge bases when different data sources are considered. Accordingly, two central issues of a successful knowledge-based system are the organization of its knowledge base and the expressiveness of its specification language. In this paper, we introduce a stratified framework for structuring any environmental knowledge base. We will argue that a declarative specification language, such as answer set programming, is expressive enough to capture environmental knowledge bases that are inconsistent, uncertain and incomplete. We also present an automata-based approach to manage actions in knowledge-based systems. By solving a use case, specifically the diagnosis of the safety of a particular industrial wastewater discharge in an urban wastewater system, we illustrate how to represent relevant abstractions to model related complex processes. We show that by using them it is also possible to automate the diagnosis process (in the present case, for example, to diagnose problems at a wastewater treatment plant and afterward in the river) and hence support the decision-making task. (c) 2010 Elsevier Ltd. All rights reserved.

关键词： Urban Wastewater System Industrial wastewater discharges Decision-support systems Knowledge management answer set programming

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Rapid Publication On equivalence of infinitary formulas under the stable model semantics

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THEORY AND PRACTICE OF LOGIC programming 2015年第1期15卷 18-34页

作者： Harrison, Amelia Lifschitz, Vladimir Truszczynski, Miroslaw Univ Texas Austin Dept Comp Sci Austin TX 78712 USA Univ Kentucky Dept Comp Sci Lexington KY 40506 USA

Propositional formulas that are equivalent in intuitionistic logic, or in its extension known as the logic of here-and-there, have the same stable models. We extend this theorem to propositional formulas with infinitely long conjunctions and disjunctions and show how to apply this generalization to proving properties of aggregates in answer set programming.

关键词： answer set programming semantics of aggregates intuitionistic logic strong equivalence

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Assessing Student programming Process Using Automated Reasoning 53

Assessing Student Programming Process Using Automated Reason...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Acuña, Ruben Bansal, Ajay School of Computing and Augmented Intelligence Arizona State Univerity MesaAZ United States

ISBN: (纸本)9798350336429

This Innovative Practice Full Paper presents an automated reasoning approach to the assessment of a student's programming process as captured by an autograder. When students are assessed in computing courses, they are typically assessed on their completed work, such as a programming assignment. Although such an assessment measures a student's general ability, it provides only an indirect measure of the effectiveness of their programming (or problem-solving) process. Without the ability to examine the process by which a student completes an assignment, an instructor may have difficulty teaching problem-solving skills since they cannot measure how the student's skill develops. Some computing courses make use of an autograder which automatically assesses student homework. The trace captured by an autograder is a unique source of information about a student's problem-solving ability, which can be analyzed to measure properties of that learner's process. This provides a way to assess the process-based aspect of student learning, enabling instructors to not only teach problem-solving skills but to determine how well students acquire these skills. This enables teachers to evaluate the methodology they employ and to select instructional material based on student need. Automatically analyzing the trace captured by an autograder has two major challenges: the scale of the data makes traditional approaches computationally expensive, and the definition of a problem-solving process is often expressed in a commonsense way based on an expert's intuition. These challenges can be addressed by using an answer set programming (ASP) approach. ASP is a declarative language that is intended for solving computationally hard problems, and through various extensions, supports human-style commonsense reasoning. Assessing a student is computationally hard due to the combinatorial nature of the different ways an assignment may be solved. In this paper, we discuss the design of an ASP-based syst

关键词： answer set programming automated assessment computing education process assessment

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Towards a Logic programming Tool for Cancer Data Analysis

Towards a Logic Programming Tool for Cancer Data Analysis

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Fundamenta Informaticae

作者： Tarzariol, Alice Zanazzo, Eugenia Dovier, Agostino Policriti, Alberto Dipartimento di Scienze Matematiche Informatiche e Fisiche Università Degli Studi di Udine Italy

The main goal of this work is to propose a tool-chain capable of analyzing a data collection of temporally qualified (genetic) mutation profiles, i.e., a collection of DNA-sequences (genes) that present variations with respect to their 'healthy' versions. We implemented a system consisting of a front-end, a reasoning core, and a post-processor: the first transforms the input data retrieved from medical databases into a set of logical facts, while the last displays the computation results as graphs. Concerning the reasoning core, we employed the answer set programming paradigm, which is capable of deducing complex information from data. However, since the system is modular, this component can be replaced by any logic programming tool for different kinds of data analysis. Indeed, we tested the use of a probabilistic inductive logic programming core. © 2020 - IOS Press and the authors. All rights reserved.

关键词： Inductive logic programming (ILP) Computation theory Computer circuits Data handling DNA sequences Genetic programming Information analysis Medical computing answer set programming Complex information Data collection Front end Input datas Medical database Post processor Probabilistic inductive logic programming

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Rough set reasoning using answer set programs

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2021年 130卷 126-149页

作者： Doherty, Patrick Szalas, Andrzej Jinan Univ Sch Intelligent Syst & Engn Zhuhai Campus Zhuhai Peoples R China Linkoping Univ Dept Comp & Informat Sci SE-58183 Linkoping Sweden Univ Warsaw Inst Informat Banacha 2 PL-02097 Warsaw Poland

Reasoning about uncertainty is one of the main cornerstones of Knowledge Representation. Formal representations of uncertainty are numerous and highly varied due to different types of uncertainty intended to be modeled such as vagueness, imprecision and incompleteness. There is a rich body of theoretical results that has been generated for many of these approaches. It is often the case though, that pragmatic tools for reasoning with uncertainty lag behind this rich body of theoretical results. Rough set theory is one such approach for modeling incompleteness and imprecision based on indiscernibility and its generalizations. In this paper, we provide a pragmatic tool for constructively reasoning with generalized rough set approximations that is based on the use of answer set programming (Asp). We provide an interpretation of answer sets as (generalized) approximations of crisp sets (when possible) and show how to use Asp solvers as a tool for reasoning about (generalized) rough set approximations situated in realistic knowledge bases. The paper includes generic Asp templates for doing this and also provides a case study showing how these techniques can be used to generate reducts for incomplete information systems. Complete, ready to run clingo Asp code is provided in the Appendix, for all programs considered. These can be executed for validation purposes in the clingo Asp solver. (C) 2020 The Author(s). Published by Elsevier Inc.

关键词： Rough sets Approximate reasoning answer set programming Knowledge representation

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Adjudication of coreference annotations via answer set optimisation

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JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2018年第4期30卷 525-546页

作者： Schueller, Peter Tech Univ Wien Inst Log & Computat Vienna Austria Marmara Univ Fac Engn Istanbul Turkey

We describe the first automatic approach for merging coreference annotations obtained from multiple annotators into a single gold standard. This merging is subject to certain linguistic hard constraints and optimisation criteria that prefer solutions with minimal divergence from annotators. The representation involves an equivalence relation over a large number of elements. We use answer set programming to describe two representations of the problem and four objective functions suitable for different data-sets. We provide two structurally different real-world benchmark data-sets based on the METU-Sabanci Turkish Treebank and we report our experiences in using the Gringo, Clasp and Wasp tools for computing optimal adjudication results on these data-sets.

关键词： Coreference resolution adjudication answer set programming

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How to Build Your Own ASP-based System?!

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THEORY AND PRACTICE OF LOGIC programming 2023年第1期23卷 299-361页

作者： Kaminski, Roland Romero, Javier Schaub, Torsten Wanko, Philipp Univ Potsdam Potsdam Germany

answer set programming, or ASP for short, has become a popular and sophisticated approach to declarative problem solving. Its popularity is due to its attractive modeling-grounding-solving workflow that provides an easy approach to problem solving, even for laypersons outside computer science. However, in contrast to ASP's ease of use, the high degree of sophistication of the underlying technology makes it even hard for ASP experts to put ideas into practice whenever this involves modifying ASP's machinery. For addressing this issue, this tutorial aims at enabling users to build their own ASP-based systems. More precisely, we show how the ASP system clingo can be used for extending ASP and for implementing customized special-purpose systems. To this end, we propose two alternatives. We begin with a traditional AI technique and show how metaprogramming can be used for extending ASP. This is a rather light approach that relies on clingo's reification feature to use ASP itself for expressing new functionalities. The second part of this tutorial uses traditional programming (in Python) for manipulating clingo via its application programming interface. This approach allows for changing and controlling the entire model-ground-solve workflow of ASP. Central to this is clingo's new Application class that allows us to draw on clingo's infrastructure by customizing processes similar to the one in clingo. For instance, we may apply manipulations to programs' abstract syntax trees, control various forms of multi-shot solving, and set up theory propagators for foreign inferences. A cross-sectional structure, spanning meta as well as application programming, is clingo's intermediate format, aspif, that specifies the interface among the underlying grounder and solver. We illustrate the aforementioned concepts and techniques throughout this tutorial by means of examples and several nontrivial case studies. In particular, we show how clingo can be extended by difference constraints

关键词： answer set programming answer set Systems Meta programming answer set Solving modulo Theories

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A synthesis of automated planning and reinforcement learning for efficient, robust decision-making

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ARTIFICIAL INTELLIGENCE 2016年第0期241卷 103-130页

作者： Leonetti, Matteo Iocchi, Luca Stone, Peter Univ Texas Austin Dept Comp Sci 2317 SpeedwayStop D9500 Austin TX 78712 USA Sapienza Univ Rome Dept Comp Control & Management Engn Via Ariosto 25 I-00185 Rome Italy Univ Leeds Sch Comp Leeds LS2 9JT W Yorkshire England

Automated planning and reinforcement learning are characterized by complementary views on decision making: the former relies on previous knowledge and computation, while the latter on interaction with the world, and experience. Planning allows robots to carry out different tasks in the same domain, without the need to acquire knowledge about each one of them, but relies strongly on the accuracy of the model. Reinforcement learning, on the other hand, does not require previous knowledge, and allows robots to robustly adapt to the environment, but often necessitates an infeasible amount of experience. We present Domain Approximation for Reinforcement LearniNG (DARLING), a method that takes advantage of planning to constrain the behavior of the agent to reasonable choices, and of reinforcement learning to adapt to the environment, and increase the reliability of the decision making process. We demonstrate the effectiveness of the proposed method on a service robot, carrying out a variety of tasks in an office building. We find that when the robot makes decisions by planning alone on a given model it often fails, and when it makes decisions by reinforcement learning alone it often cannot complete its tasks in a reasonable amount of time. When employing DARLING, even when seeded with the same model that was used for planning alone, however, the robot can quickly learn a behavior to carry out all the tasks, improves over time, and adapts to, the environment as it changes. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Automated planning Reinforcement learning Autonomous robot Robot learning answer set programming

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Reconciling Description Logics and Rules

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JOURNAL OF THE ACM 2010年第5期57卷 30-30.62页

作者： Motik, Boris Rosati, Riccardo Univ Oxford Comp Lab Oxford OX1 3QD England Univ Roma La Sapienza Dipartimento Informat & Sistemist I-00185 Rome Italy

Description logics (DLs) and rules are formalisms that emphasize different aspects of knowledge representation: whereas DLs are focused on specifying and reasoning about conceptual knowledge, rules are focused on nonmonotonic inference. Many applications, however, require features of both DLs and rules. Developing a formalism that integrates DLs and rules would be a natural outcome of a large body of research in knowledge representation and reasoning of the last two decades;however, achieving this goal is very challenging and the approaches proposed thus far have not fully reached it. In this paper, we present a hybrid formalism of MKNF+ knowledge bases, which integrates DLs and rules in a coherent semantic framework. Achieving seamless integration is nontrivial, since DLs use an open-world assumption, while the rules are based on a closed-world assumption. We overcome this discrepancy by basing the semantics of our formalism on the logic of minimal knowledge and negation as failure (MKNF) by Lifschitz. We present several algorithms for reasoning with MKNF+ knowledge bases, each suitable to different kinds of rules, and establish tight complexity bounds.

关键词： Theory Description logics answer set programming combined complexity data complexity

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External Behavior of a Logic Program and Verification of Refactoring

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THEORY AND PRACTICE OF LOGIC programming 2023年第4期23卷 933-947页

作者： Fandinno, Jorge Hansen, Zachary Lierler, Yuliya Lifschitz, Vladimir Temple, Nathan Univ Nebraska Omaha NE 68182 USA Univ Texas Austin Austin TX USA

Refactoring is modifying a program without changing its external behavior. In this paper, we make the concept of external behavior precise for a simple answer set programming language. Then we describe a proof assista... 详细信息

关键词： answer set programming software verification proof assistant automated reasoning

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