This paper introduces GLINTS, a graphical tool for exploring variant narrowing computations in Maude. The most recent version of Maude, version 2.7.1, provides quite sophisticated unification features, including order...
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This paper introduces GLINTS, a graphical tool for exploring variant narrowing computations in Maude. The most recent version of Maude, version 2.7.1, provides quite sophisticated unification features, including order-sorted equational unification for convergent theories modulo axioms such as associativity, commutativity, and identity. This novel equational unification relies on built-in generation of the set of variants of a term t, i.e., the canonical form of t sigma for a computed substitution sigma. Variant generation relies on a novel narrowing strategy called folding variant narrowing that opens up new applications in formal reasoning, theorem proving, testing, protocol analysis, and model checking, especially when the theory satisfies the finite variant property, i.e., there is a finite number of most general variants for every term in the theory. However, variant narrowing computations can be extremely involved and are simply presented in text format by Maude, often being too heavy to be debugged or even understood. The GLINTS system provides support for (i) determining whether a given theory satisfies the finite variant property, (ii) thoroughly exploring variant narrowing computations, (iii) automatic checking of node embedding and closedness modulo axioms, and (iv) querying and inspecting selected parts of the variant trees.
In complex reasoning tasks, as expressible by Answer Set programming (ASP), problems often permit for multiple solutions. In dynamic environments, where knowledge is continuously changing, the question arises how a gi...
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In complex reasoning tasks, as expressible by Answer Set programming (ASP), problems often permit for multiple solutions. In dynamic environments, where knowledge is continuously changing, the question arises how a given model can be incrementally adjusted relative to new and outdated information. This paper introduces Ticker, a prototypical engine for well-defined logical reasoning over streaming data. Ticker builds on a practical fragment of the recent rule-based language LARS, which extends ASP for streams by providing flexible expiration control and temporal modalities. We discuss Ticker's reasoning strategies: first, the repeated one-shot solving mode calls Clingo on an ASP encoding. We show how this translation can be incrementally updated when new data is streaming in or time passes by. Based on this, we build on Doyle's classic justification-based truth-maintenance system to update models of non-stratified programs. Finally, we empirically compare the obtained evaluation mechanisms.
This paper introduces a novel monotonic modal logic, able to characterise reflexive autoepistemic reasoning of the nonmonotonic variant of modal logic SW5: we add a second new modal operator into the original language...
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ISBN:
(纸本)9783319616605;9783319616599
This paper introduces a novel monotonic modal logic, able to characterise reflexive autoepistemic reasoning of the nonmonotonic variant of modal logic SW5: we add a second new modal operator into the original language of SW5, and show that the resulting formalism called MRAE* is strong enough to capture the minimal model notion underlying some major forms of nonmonotonic logic among which are autoepistemic logic, default logic, and nonmonotonic logicprogramming. The paper ends with a discussion of a general strategy, naturally embedding several nonmonotonic logics of similar kinds.
Answer Set programming (ASP) is a prominent knowledge representation language with roots in logicprogramming and non-monotonic reasoning. Biennial competitions are organized in order to furnish challenging benchmark ...
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ISBN:
(纸本)9783319616605;9783319616599
Answer Set programming (ASP) is a prominent knowledge representation language with roots in logicprogramming and non-monotonic reasoning. Biennial competitions are organized in order to furnish challenging benchmark collections and assess the advancement of the state of the art in ASP solving. In this paper, we report about the design of the Seventh ASP Competition, which is jointly organized by the University of Calabria (Italy), the University of Genova (Italy), and the University of Potsdam (Germany), in affiliation with the 14th International Conference on logicprogramming and Non-Monotonic Reasoning (LPNMR 2017). A novel feature of this competition edition is the re-introduction of a Model&Solve track, complementing the usual System track with problem domains where participants need to provide dedicated encodings and solving means.
Constraint answer set programming (CASP) is an extension of answer set programming that allows for numerical constraints to be added in the rules. PDDL+ is an extension of the PDDL standard language of automated plann...
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Constraint answer set programming (CASP) is an extension of answer set programming that allows for numerical constraints to be added in the rules. PDDL+ is an extension of the PDDL standard language of automated planning for modeling mixed discrete-continuous dynamics. In this paper, we present CASP solutions for dealing with PDDL+ problems, i.e., encoding from PDDL+ to CASP, and extensions to the algorithm of the ezcsp CASP solver in order to solve CASP programs arising from PDDL+ domains. An experimental analysis, performed on well-known linear and non-linear variants of PDDL+ domains, involving various configurations of the ezcsp solver, other CASP solvers, and PDDL+ planners, shows the viability of our solution.
Both hybrid automata and action languages are formalisms for describing the evolution of dynamic systems. This paper establishes a formal relationship between them. We show how to succinctly represent hybrid automata ...
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Both hybrid automata and action languages are formalisms for describing the evolution of dynamic systems. This paper establishes a formal relationship between them. We show how to succinctly represent hybrid automata in an action language which in turn is defined as a high-level notation for answer set programming modulo theories-an extension of answer set programs to the firstorder level similar to the way satisfiability modulo theories (SMT) extends propositional satisfiability (SAT). We first show how to represent linear hybrid automata with convex invariants by an action language modulo theories. A further translation into SMT allows for computing them using SMT solvers that support arithmetic over reals. Next, we extend the representation to the general class of non-linear hybrid automata allowing even non-convex invariants. We represent them by an action language modulo ordinary differential equations, which can be compiled into satisfiability modulo ordinary differential equations. We present a prototype system CPLUS2ASPMT based on these translations, which allows for a succinct representation of hybrid transition systems that can be computed effectively by the state-of-the-art SMT solver dReal.
Among the myriad of desirable properties discussed in the context of forgetting in Answer Set programming, strong persistence naturally captures its essence. Recently, it has been shown that it is not always possible ...
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Among the myriad of desirable properties discussed in the context of forgetting in Answer Set programming, strong persistence naturally captures its essence. Recently, it has been shown that it is not always possible to forget a set of atoms from a program while obeying this property, and a precise criterion regarding what can be forgotten has been presented, accompanied by a class of forgetting operators that return the correct result when forgetting is possible. However, it is an open question what to do when we have to forget a set of atoms, but cannot without violating this property. In this paper, we address this issue and investigate three natural alternatives to forget when forgetting without violating strong persistence is not possible, which turn out to correspond to the different possible relaxations of the characterization of strong persistence. Additionally, we discuss their preferable usage, shed light on the relation between forgetting and notions of relativized equivalence established earlier in the context of Answer Set programming, and present a detailed study on their computational complexity.
This paper introduces GLINTS, a graphical tool for exploring variant narrowing computations in Maude. The most recent version of Maude, version 2.7.1, provides quite sophisticated unification features, including order...
详细信息
This paper introduces GLINTS, a graphical tool for exploring variant narrowing computations in Maude. The most recent version of Maude, version 2.7.1, provides quite sophisticated unification features, including order-sorted equational unification for convergent theories modulo axioms such as associativity, commutativity, and identity. This novel equational unification relies on built-in generation of the set of variants of a term t, i.e., the canonical form of t sigma for a computed substitution sigma. Variant generation relies on a novel narrowing strategy called folding variant narrowing that opens up new applications in formal reasoning, theorem proving, testing, protocol analysis, and model checking, especially when the theory satisfies the finite variant property, i.e., there is a finite number of most general variants for every term in the theory. However, variant narrowing computations can be extremely involved and are simply presented in text format by Maude, often being too heavy to be debugged or even understood. The GLINTS system provides support for (i) determining whether a given theory satisfies the finite variant property, (ii) thoroughly exploring variant narrowing computations, (iii) automatic checking of node embedding and closedness modulo axioms, and (iv) querying and inspecting selected parts of the variant trees.
A concentrator is a circuit with N inputs and M <= N outputs that can route any given subset of K <= M valid inputs to K of its M outputs. Concentrator circuits are important building blocks of many parallel alg...
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ISBN:
(纸本)9783319616605;9783319616599
A concentrator is a circuit with N inputs and M <= N outputs that can route any given subset of K <= M valid inputs to K of its M outputs. Concentrator circuits are important building blocks of many parallel algorithms. The design of optimal concentrator circuits is however a challenging task that has already been considered in many research papers. In this paper, we show how answer set programming can be used to automatically generate concentrator circuits of provably optimal size.
The paper presents some applications in planning and multi-agent systems of answer set programming. It highlights the benefits of answer set programming based techniques in these applications. It also describes a clas...
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ISBN:
(纸本)9783319616605;9783319616599
The paper presents some applications in planning and multi-agent systems of answer set programming. It highlights the benefits of answer set programming based techniques in these applications. It also describes a class of multi-agent planning problems that is challenging to answer set programming.
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