We present a new approach to enhancing answer set programming (ASP) with Constraint Processing techniques which allows for solving interesting Constraint Satisfaction Problems in ASP. We show how constraints on finite...
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We present a new approach to enhancing answer set programming (ASP) with Constraint Processing techniques which allows for solving interesting Constraint Satisfaction Problems in ASP. We show how constraints on finite domains can be decomposed into logic programs such that unit-propagation achieves arc, bound or range consistency. Experiments with our encodings demonstrate their computational impact.
The repeated execution of reasoning tasks is desirable in many applicative scenarios, such as stream reasoning and event processing. When using answer set programming in such contexts, one can avoid the iterative gene...
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The repeated execution of reasoning tasks is desirable in many applicative scenarios, such as stream reasoning and event processing. When using answer set programming in such contexts, one can avoid the iterative generation of ground programs thus achieving a significant payoff in terms of computing time. However, this may require some additional amount of memory and/or the manual addition of operational directives in the declarative knowledge base at hand. We introduce a new strategy for generating series of monotonically growing propositional programs. The proposedovergrounded programs with tailoring(OPTs) can be updated and reused in combination with consecutive inputs. With respect to earlier approaches, ourtailored simplificationtechnique reduces the size of instantiated programs. A maintained OPT slowly grows in size from an iteration to another while the update cost decreases, especially in later iterations. In this paper we formally introduce tailored embeddings, a family of equivalence-preserving ground programs which are at the theoretical basis of OPTs and we describe their properties. We then illustrate an OPT update algorithm and report about our implementation and its performance.
Management of chronic diseases, such as heart failure, is a major public health problem. A standard approach to managing chronic diseases by medical community is to have a committee of experts develop guidelines that ...
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Management of chronic diseases, such as heart failure, is a major public health problem. A standard approach to managing chronic diseases by medical community is to have a committee of experts develop guidelines that all physicians should follow. Due to their complexity, these guidelines are difficult to implement and are adopted slowly by the medical community at large. We have developed a physician advisory system that codes the entire set of clinical practice guidelines for managing heart failure using answer set programming. In this paper, we show how abductive reasoning can be deployed to find missing symptoms and conditions that the patient must exhibit in order for a treatment prescribed by a physician to work effectively. Thus, if a physician does not make an appropriate recommendation or makes a non-adherent recommendation, our system will advise the physician about symptoms and conditions that must be in effect for that recommendation to apply. It is under consideration for acceptance in TPLP.
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.
Several extensions of the stable model semantics are available to describe "intensional" functions-functions that can be described in terms of other functions and predicates by logic programs. Such functions...
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Several extensions of the stable model semantics are available to describe "intensional" functions-functions that can be described in terms of other functions and predicates by logic programs. Such functions are useful for expressing inertia and default behaviors of systems, and can be exploited for alleviating the grounding bottleneck involving functional fluents. However, the extensions were defined in different ways under different intuitions. In this paper we provide several reformulations of the extensions, and note that they are in fact closely related to each other and coincide on large syntactic classes of logic programs.
Acting under several jurisdictions at the same time is becoming the norm rather than the exception, certainly for companies but also (sometimes without knowing) for individuals. In these circumstances disparities amon...
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ISBN:
(纸本)9781614993599;9781614993582
Acting under several jurisdictions at the same time is becoming the norm rather than the exception, certainly for companies but also (sometimes without knowing) for individuals. In these circumstances disparities among the different laws are inevitable. Here, we present a mathematical and a computational model of interacting legal specifications, along with a mechanism to find conflicts between them. We illustrate the approach by a case study using European Privacy law.
HEX programs extend ASP with external atoms implemented in C++ or Python. DLVHEX is a solver for HEX that permits cyclic reasoning over external atoms and external value invention.
HEX programs extend ASP with external atoms implemented in C++ or Python. DLVHEX is a solver for HEX that permits cyclic reasoning over external atoms and external value invention.
Search/optimization problems are plentiful in scientific and engineering domains. Artificial intelligence has long contributed to the development of search algorithms and declarative programming languages geared towar...
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Search/optimization problems are plentiful in scientific and engineering domains. Artificial intelligence has long contributed to the development of search algorithms and declarative programming languages geared toward solving and modeling search/optimization problems. Automated reasoning and knowledge representation are the subfields of AI that are particularly vested in these developments. Many popular automated reasoning paradigms provide users with languages supporting optimization statements: answer set programming or MaxSAT or min-one, to name a few. These paradigms vary significantly in their languages and in the ways they express quality conditions on computed solutions. Here we propose a unifying framework of so-called weight systems that eliminates syntactic distinctions between paradigms and allows us to see essential similarities and differences between optimization statements provided by paradigms. This unifying outlook has significant simplifying and explanatory potential in the studies of optimization and modularity in automated reasoning and knowledge representation. It also supplies researchers with a convenient tool for proving the formal properties of distinct frameworks;bridging these frameworks;and facilitating the development of translational solvers.
The representation of a dynamic problem in ASP usually boils down to using copies of variables and constraints, one for each time stamp, no matter whether it is directly encoded or via an action or temporal language. ...
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ISBN:
(纸本)9783031215407;9783031215414
The representation of a dynamic problem in ASP usually boils down to using copies of variables and constraints, one for each time stamp, no matter whether it is directly encoded or via an action or temporal language. The multiplication of variables and constraints is commonly done during grounding and the solver is completely ignorant about the temporal relationship among the different instances. On the other hand, a key factor in the performance of today's ASP solvers is conflict-driven constraint learning. Our question is now whether a constraint learned for particular time steps can be generalized and reused at other time stamps, and ultimately whether this enhances the overall solver performance on dynamic problems. Knowing full well the domain of time, we study conditions under which learned dynamic constraints can be generalized and propose a simple translation of the original logic program such that, for the translated programs, all learned constraints can be generalized to other time points. Last but not least, we empirically evaluate the impact of adding the generalized constraints to an ASP solver.
We present a new approach based on spatial reasoning in answer set programming (ASP), and a prototype software tool, for automatically evaluating construction safety compliance of real-world Building Information Model...
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ISBN:
(纸本)9781450388214
We present a new approach based on spatial reasoning in answer set programming (ASP), and a prototype software tool, for automatically evaluating construction safety compliance of real-world Building Information Models (BIM) that have both a geometric component and temporal component in the form of a construction plan and schedule (4D BIM). In the 4D BIM domain, geometries of building objects are large and complex making it highly impractical to represent geometries as ASP facts, unoptimised spatial reasoning can be prohibitively slow, and rounding errors in floating point arithmetic often result in logical contradictions. Our novel framework addresses these challenges by integrating a specialised geometry database, built-in spatial optimisations, and support for real arithmetic solving. We empirically evaluate our prototype software tool on two large 4D BIM models from real buildings to demonstrate the practicality and scalability of our new framework to real-world workplace hazard prevention tasks in construction safety-in-design analysis.
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