FO(.)(IDP3) extends first-order logic with inductive definitions, partial functions, types and aggregates. Its model generator IDP3 first grounds the theory and then uses search to find the models. The grounder uses L...
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FO(.)(IDP3) extends first-order logic with inductive definitions, partial functions, types and aggregates. Its model generator IDP3 first grounds the theory and then uses search to find the models. The grounder uses Lifted Unit Propagation (LUP) to reduce the size of the groundings of problem specifications in IDP3. LUP is in general very effective, but performs poorly on definitions of predicates whose two-valued interpretation can be computed from data in the input structure. To solve this problem, a preprocessing step is introduced that converts such definitions to Prolog code and uses XSB Prolog to compute their interpretation. The interpretation of these predicates is then added to the input structure, their definitions are removed from the theory and further processing is done by the standard IDP3 system. Experimental results show the effectiveness of our method.
The paper provides a framework for the verification of business processes, based on an extension of answer set programming (ASP) with temporal logic and constraints. The framework allows to capture expressive fluent a...
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The paper provides a framework for the verification of business processes, based on an extension of answer set programming (ASP) with temporal logic and constraints. The framework allows to capture expressive fluent annotations as well as data awareness in a uniform way. It allows for a declarative specification of a business process but also for encoding processes specified in conventional workflow languages. Verification of temporal properties of a business process, including verification of compliance to business rules, is performed by bounded model checking techniques in Answer Set programming, extended with constraint solving for dealing with conditions on numeric data.
Proposing relevant perturbations to biological signaling networks is central to many problems in biology and medicine because it allows for enabling or disabling certain biological outcomes. In contrast to quantitativ...
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Proposing relevant perturbations to biological signaling networks is central to many problems in biology and medicine because it allows for enabling or disabling certain biological outcomes. In contrast to quantitative methods that permit fine-grained (kinetic) analysis, qualitative approaches allow for addressing large-scale networks. This is accomplished by more abstract representations such as logical networks. We elaborate upon such a qualitative approach aiming at the computation of minimal interventions in logical signaling networks relying on Kleene's three-valued logic and fixpoint semantics. We address this problem within answer set programming and show that it greatly outperforms previous work using dedicated algorithms.
A probabilistic model allows us to reason about the world and make statistically optimal decisions using Bayesian decision theory. However, in practice the intractability of the decision problem forces us to adopt sim...
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A probabilistic model allows us to reason about the world and make statistically optimal decisions using Bayesian decision theory. However, in practice the intractability of the decision problem forces us to adopt simplistic loss functions such as the 0/1 loss or Hamming loss and as result we make poor decisions through MAP estimates or through low-order marginal statistics. In this work we investigate optimal decision making for more realistic loss functions. Specifically we consider the popular intersection-over-union (IoU) score used in image segmentation benchmarks and show that it results in a hard combinatorial decision problem. To make this problem tractable we propose a statistical approximation to the objective function, as well as an approximate algorithm based on parametric linear programming. We apply the algorithm on three benchmark datasets and obtain improved intersection-over-union scores compared to maximum-posterior-marginal decisions. Our work points out the difficulties of using realistic loss functions with probabilistic computer vision models.
There is an increasing interest in using logicprogramming to specify and implement distributed algorithms, including a variety of network applications. These are applications where data and computation are distribute...
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There is an increasing interest in using logicprogramming to specify and implement distributed algorithms, including a variety of network applications. These are applications where data and computation are distributed among several devices and where, in principle, all the devices can exchange data and share the computational results of the group. In this paper we propose a declarative approach to distributed computing whereby distributed algorithms and communication models can be (i) specified as action theories of fluents and actions;(ii) executed as collections of distributed state machines, where devices are abstracted as (input/output) automata that can exchange messages;and (iii) analysed using existing results on connecting causal theories and Answer Set programming. Results on the application of our approach to different classes of network protocols are also presented.
Maher (2012) introduced an approach for relative expressiveness of defeasible logics, and two notions of relative expressiveness were investigated. Using the first of these definitions of relative expressiveness, we s...
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Maher (2012) introduced an approach for relative expressiveness of defeasible logics, and two notions of relative expressiveness were investigated. Using the first of these definitions of relative expressiveness, we show that all the defeasible logics in the DL framework are equally expressive under this formulation of relative expressiveness. The second formulation of relative expressiveness is stronger than the first. However, we show that logics incorporating individual defeat are equally expressive as the corresponding logics with team defeat. Thus the only differences in expressiveness of logics in DL arise from differences in how ambiguity is handled. This completes the study of relative expressiveness in DL begun in Maher (2012).
Weight constraint and aggregate programs are among the most widely used logic programs with constraints. In this paper, we relate the semantics of these two classes of programs, namely, the stable model semantics for ...
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Weight constraint and aggregate programs are among the most widely used logic programs with constraints. In this paper, we relate the semantics of these two classes of programs, namely, the stable model semantics for weight constraint programs and the answer set semantics based on conditional satisfaction for aggregate programs. Both classes of programs are instances of logic programs with constraints, and in particular, the answer set semantics for aggregate programs can be applied to weight constraint programs. We show that the two semantics are closely related. First, we show that for a broad class of weight constraint programs, called strongly satisfiable programs, the two semantics coincide. When they disagree, a stable model admitted by the stable model semantics may be circularly justified. We show that the gap between the two semantics can be closed by transforming a weight constraint program to a strongly satisfiable one so that no circular models may be generated under the current implementation of the stable model semantics. We further demonstrate the close relationship between the two semantics by formulating a transformation from weight constraint programs to logic programs with nested expressions, which preserves the answer set semantics. Our study on the semantics leads to an investigation of a methodological issue, namely, the possibility of compact representation of aggregate programs by weight constraint programs. We show that almost all standard aggregates can be encoded by weight constraints compactly. This makes it possible to compute the answer sets of aggregate programs using the answer set programming solvers for weight constraint programs. This approach is compared experimentally with the ones where aggregates are handled more explicitly, which show that the weight constraint encoding of aggregates enables a competitive approach to answer set computation for aggregate programs.
In this paper we take on Stuart C. Shapiro's challenge of solving the Jobs Puzzle automatically and do this via controlled natural language processing. Instead of encoding the puzzle in a formal language that migh...
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In this paper we take on Stuart C. Shapiro's challenge of solving the Jobs Puzzle automatically and do this via controlled natural language processing. Instead of encoding the puzzle in a formal language that might be difficult to use and understand, we employ a controlled natural language as a high-level specification language that adheres closely to the original notation of the puzzle and allows us to reconstruct the puzzle in a machine-processable way and add missing and implicit information to the problem description. We show how the resulting specification can be translated into an answer set program and be processed by a state-of-the-art answer set solver to find the solutions to the puzzle.
Dealing with domains involving substantial quantitative information in Answer Set programming (ASP) often results in cumbersome and inefficient encodings. Hybrid "CASP" languages combining ASP and Constraint...
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Dealing with domains involving substantial quantitative information in Answer Set programming (ASP) often results in cumbersome and inefficient encodings. Hybrid "CASP" languages combining ASP and Constraint programming aim to overcome this limitation, but also impose inconvenient constraints - first and foremost that quantitative information must be encoded by means of total functions. This goes against central knowledge representation principles that contribute to the power of ASP, and makes the formalization of certain domains difficult. ASP{f} is being developed with the ultimate goal of providing scientists and practitioners with an alternative to CASP languages that allows for the efficient representation of qualitative and quantitative information in ASP without restricting one's ability to deal with incompleteness or uncertainty. In this paper we present the latest outcome of such research: versions of the language and of the supporting system that allow for practical, industrial-size use and scalability. The applicability of ASP{f} is demonstrated by a case study on an actual industrial application.
We study the problem of finding optimal plans for multiple teams of robots through a mediator, where each team is given a task to complete in its workspace on its own and where teams are allowed to transfer robots bet...
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We study the problem of finding optimal plans for multiple teams of robots through a mediator, where each team is given a task to complete in its workspace on its own and where teams are allowed to transfer robots between each other, subject to the following constraints: 1) teams (and the mediator) do not know about each other's workspace or tasks (e.g., for privacy purposes);2) every team can lend or borrow robots, but not both (e.g., transportation/calibration of robots between/for different workspaces is usually costly). We present a mathematical definition of this problem and analyze its computational complexity. We introduce a novel, logic-based method to solve this problem, utilizing action languages and answer set programming for representation, and the state-of-the-art ASP solvers for reasoning. We show the applicability and usefulness of our approach by experiments on various scenarios of responsive and energy-efficient cognitive factories.
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