Answer Set Programming
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Answer Set Programming
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Author : Vladimir Lifschitz
language : en
Publisher: Springer Nature
Release Date : 2019-08-29
Answer Set Programming written by Vladimir Lifschitz and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-29 with Computers categories.
Answer set programming (ASP) is a programming methodology oriented towards combinatorial search problems. In such a problem, the goal is to find a solution among a large but finite number of possibilities. The idea of ASP came from research on artificial intelligence and computational logic. ASP is a form of declarative programming: an ASP program describes what is counted as a solution to the problem, but does not specify an algorithm for solving it. Search is performed by sophisticated software systems called answer set solvers. Combinatorial search problems often arise in science and technology, and ASP has found applications in diverse areas—in historical linguistic, in bioinformatics, in robotics, in space exploration, in oil and gas industry, and many others. The importance of this programming method was recognized by the Association for the Advancement of Artificial Intelligence in 2016, when AI Magazine published a special issue on answer set programming. The book introduces the reader to the theory and practice of ASP. It describes the input language of the answer set solver CLINGO, which was designed at the University of Potsdam in Germany and is used today by ASP programmers in many countries. It includes numerous examples of ASP programs and present the mathematical theory that ASP is based on. There are many exercises with complete solutions.
Answer Set Solving In Practice
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Author : Martin Liu
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Answer Set Solving In Practice written by Martin Liu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Computers categories.
Answer Set Programming (ASP) is a declarative problem solving approach, initially tailored to modeling problems in the area of Knowledge Representation and Reasoning (KRR). More recently, its attractive combination of a rich yet simple modeling language with high-performance solving capacities has sparked interest in many other areas even beyond KRR. This book presents a practical introduction to ASP, aiming at using ASP languages and systems for solving application problems. Starting from the essential formal foundations, it introduces ASP's solving technology, modeling language and methodology, while illustrating the overall solving process by practical examples. Table of Contents: List of Figures / List of Tables / Motivation / Introduction / Basic modeling / Grounding / Characterizations / Solving / Systems / Advanced modeling / Conclusions
Answer Set Programming
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Author : Vladimir Lifschitz
language : en
Publisher:
Release Date : 2019
Answer Set Programming written by Vladimir Lifschitz and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Artificial intelligence categories.
Answer set programming (ASP) is a programming methodology oriented towards combinatorial search problems. In such a problem, the goal is to find a solution among a large but finite number of possibilities. The idea of ASP came from research on artificial intelligence and computational logic. ASP is a form of declarative programming: an ASP program describes what is counted as a solution to the problem, but does not specify an algorithm for solving it. Search is performed by sophisticated software systems called answer set solvers. Combinatorial search problems often arise in science and technology, and ASP has found applications in diverse areas?in historical linguistic, in bioinformatics, in robotics, in space exploration, in oil and gas industry, and many others. The importance of this programming method was recognized by the Association for the Advancement of Artificial Intelligence in 2016, when AI Magazine published a special issue on answer set programming. The book will introduce the reader to the theory and practice of ASP. It will describe the input language of the answer set solver CLINGO, which was designed at the University of Potsdam in Germany and is used today by ASP programmers in many countries. It will include numerous examples of ASP programs and present the mathematical theory that ASP is based on. There will be many exercises with complete solutions.
Answer Set Solving In Practice
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Author : Martin Gebser
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2013
Answer Set Solving In Practice written by Martin Gebser and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Computers categories.
Answer Set Programming (ASP) is a declarative problem solving approach, initially tailored to modelling problems in the area of Knowledge Representation and Reasoning (KRR). This book presents a practical introduction to ASP. It introduces ASP's solving technology, modelling language and methodology, while illustrating the overall solving process with practical examples.
Sat Based Answer Set Programming
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Author : Yuliya Lierler
language : en
Publisher:
Release Date : 2010
Sat Based Answer Set Programming written by Yuliya Lierler and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.
Answer set programming (ASP) is a declarative programming paradigm oriented towards difficult combinatorial search problems. Syntactically, ASP programs look like Prolog programs, but solutions are represented in ASP by sets of atoms, and not by substitutions, as in Prolog. Answer set systems, such as Smodels, Smodelscc, and DLV, compute answer sets of a given program in the sense of the answer set (stable model) semantics. This is different from the functionality of Prolog systems, which determine when a given query is true relative to a given logic program. ASP has been applied to many areas of science and technology, from the design of a decision support system for the Space Shuttle to graph-theoretic problems arising in zoology and linguistics. The "native" answer set systems mentioned above are based on specialized search procedures. Usually these procedures are described fairly informally with the use of pseudocode. We propose an alternative approach to describing algorithms of answer set solvers. In this approach we specify what "states of computation" are, and which transitions between states are allowed. In this way, we define a directed graph such that every execution of a procedure corresponds to a path in this graph. This allows us to model algorithms of answer set solvers by a mathematically simple and elegant object, graph, rather than a collection of pseudocode statements. We use this abstract framework to describe and prove the correctness of the answer set solver Smodels, and also of Smodelscc, which enhances the former using learning and backjumping techniques. Answer sets of a tight program can be found by running a SAT solver on the program's completion, because for such a program answer sets are in a one-to-one correspondence with models of completion. SAT is one of the most widely studied problems in computational logic, and many efficient SAT procedures were developed over the last decade. Using SAT solvers for computing answer sets allows us to take advantage of the advances in the SAT area. For a nontight program it is still the case that each answer set corresponds to a model of program's completion but not vice versa. We show how to modify the search method typically used in SAT solvers to allow testing models of completion and employ learning to utilize testing information to guide the search. We develop a new SAT-based answer set solver, called Cmodels, based on this idea. We develop an abstract graph based framework for describing SAT-based answer set solvers and use it to represent the Cmodels algorithm and to demonstrate its correctness. Such representations allow us to better understand similarities and differences between native and SAT-based answer set solvers. We formally compare the Smodels algorithm with a variant of the Cmodels algorithm without learning. Abstract frameworks for describing native and SAT-based answer set solvers facilitate the development of new systems. We propose and implement the answer set solver called SUP that can be seen as a combination of computational ideas behind Cmodels and Smodels. Like Cmodels, solver SUP operates by computing a sequence of models of completion of the given program, but it does not form the completion. Instead, SUP runs the Atleast algorithm, one of the main building blocks of the Smodels procedure. Both systems Cmodels and SUP, developed in this dissertation, proved to be competitive answer set programming systems.
Theory And Applications Of Answer Set Programming
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Author : Esra Erdem
language : en
Publisher:
Release Date : 2002
Theory And Applications Of Answer Set Programming written by Esra Erdem and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Computer science categories.
Answer set programming (ASP) is a new form of declarative logic programming. ASP interprets a logic program as a constraint on sets of literals, just as a propositional formula can be viewed as a constraint on assignments of truth values to atoms. The concept of an answer set was originally proposed as a semantics of negation as failure in Prolog. Instead of traditional Prolog systems, ASP uses answer set solvers. The input of Prolog consists of a logic program and a query, and Prolog computes answer substitutions; the input of an answer set solver is a logic program, and the solver computes the program's answer sets. The idea of ASP is to represent a given computational problem as a logic program whose answer sets correspond to solutions, and to use an answer set solver to find an answer set. We have investigated the application of ASP to several combinatorial search problems, including planning, wire routing, and phylogeny reconstruction. Planning is the problem of finding a sequence of actions that leads to a given goal. Wire routing is the problem of determining the physical locations of all wires interconnecting the circuit components on a chip. Phylogeny reconstruction is the problem of constructing and labeling an evolutionary tree for a set of taxa (taxonomic units), which describes the evolution of the taxa in that set from their most recent common ancestor. In our work on phylogeny reconstruction, we have generated several conjectures about the evolutionary history of Indo-European languages. The work on the use of ASP for planning has led us to the investigation of some theoretical questions related to answer sets. One is the problem of equivalent transformations of logic programs: under what conditions can we replace a program by an equivalent program that can be processed by an answer set solver more efficiently? Another problem is related to completion--a process that can translate a logic program into a set of formulas of classical logic. In some cases, the interpretations satisfying the completion of a program are also the answer sets for that program. In such cases, we can use propositional solvers--systems that compute a model of a given set of clauses--to find the program's answer sets. For some problems, propositional solvers are more efficient than answer set solvers. Therefore, we have investigated under what conditions we can use propositional solvers to find the program's answer sets.
Knowledge Representation Reasoning And The Design Of Intelligent Agents
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Author : Michael Gelfond
language : en
Publisher: Cambridge University Press
Release Date : 2014-03-10
Knowledge Representation Reasoning And The Design Of Intelligent Agents written by Michael Gelfond and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-03-10 with Computers categories.
This in-depth introduction for students and researchers shows how to use ASP for intelligent tasks, including answering queries, planning, and diagnostics.
Answer Set Programming For Continuous Domains A Fuzzy Logic Approach
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Author : Jeroen Janssen
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-04-26
Answer Set Programming For Continuous Domains A Fuzzy Logic Approach written by Jeroen Janssen and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-04-26 with Computers categories.
Answer set programming (ASP) is a declarative language tailored towards solving combinatorial optimization problems. It has been successfully applied to e.g. planning problems, configuration and verification of software, diagnosis and database repairs. However, ASP is not directly suitable for modeling problems with continuous domains. Such problems occur naturally in diverse fields such as the design of gas and electricity networks, computer vision and investment portfolios. To overcome this problem we study FASP, a combination of ASP with fuzzy logic -- a class of manyvalued logics that can handle continuity. We specifically focus on the following issues: 1. An important question when modeling continuous optimization problems is how we should handle overconstrained problems, i.e. problems that have no solutions. In many cases we can opt to accept an imperfect solution, i.e. a solution that does not satisfy all the stated rules (constraints). However, this leads to the question: what imperfect solutions should we choose? We investigate this question and improve upon the state-of-the-art by proposing an approach based on aggregation functions. 2. Users of a programming language often want a rich language that is easy to model in. However, implementers and theoreticians prefer a small language that is easy to implement and reason about. We create a bridge between these two desires by proposing a small core language for FASP and by showing that this language is capable of expressing many of its common extensions such as constraints, monotonically decreasing functions, aggregators, S-implicators and classical negation. 3. A well-known technique for solving ASP consists of translating a program P to a propositional theory whose models exactly correspond to the answer sets of P. We show how this technique can be generalized to FASP, paving the way to implement efficient fuzzy answer set solvers that can take advantage of existing fuzzy reasoners.
Reasoning Web Semantic Technologies For Information Systems
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Author : Sergio Tessaris
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-08-17
Reasoning Web Semantic Technologies For Information Systems written by Sergio Tessaris and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-08-17 with Computers categories.
This book contains a collection of revised tutorial papers based on lectures given by researchers at the 5th International Summer School on the Reasoning Web. It introduces semantic web methods and research issues with a particular emphasis on reasoning.
Graph Based Answer Set Programming Solver Systems
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Author : Fang Li
language : en
Publisher:
Release Date : 2022
Graph Based Answer Set Programming Solver Systems written by Fang Li and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with Declarative programming categories.
Answer set programming (ASP) is a popular nonmonotonic-logic based paradigm for knowledge representation and solving combinatorial problems. Computing the answer set of an ASP program is NP-hard in general, and researchers have been investing significant effort to speed it up. The majority of current ASP solvers employ SAT solver-like technology to find these answer sets. As a result, justification for why a literal is in the answer set is hard to produce. There are dependency graph based approaches to find answer sets, but due to the representational limitations of dependency graphs, such approaches are limited. This thesis proposes a novel dependency graph-based approach for finding answer sets in which conjunction of goals is explicitly represented as a node which allows arbitrary answer set programs to be uniformly represented. Our representation preserves causal relationships allowing for justification for each literal in the answer set to be elegantly found. We explore multiple approaches for executing answer set programs based on this graph representation. These approaches can be broadly classified as bottom-up or top-down. The bottom-up approach finds models by assigning truth values following a topological order, while the top-down approach generates models starting from the constraints imposed by the answer set program. We also demonstrate other applications of our graph representation-based implementation, namely, to answer set program execution visualization, finding defeaters for answer set program queries, implementing alternative semantics of normal logic programs, and finding the set of relevant consistent concepts in the development of automated socialbots.