This paper presents the first automatic method for learning and revising dynamic temporal theories in the full-fledged Discrete Event Calculus (DEC), where fluents may be temporarily released from the law of inertia a...
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ISBN:
(数字)9783030974541
ISBN:
(纸本)9783030974541;9783030974534
This paper presents the first automatic method for learning and revising dynamic temporal theories in the full-fledged Discrete Event Calculus (DEC), where fluents may be temporarily released from the law of inertia and subject to qualitative or quantitative domain laws. This is done by proposing a reformulation of the DEC, called the eXploratory Event Calculus (XEC), which can be more efficiently handled by state-of-the-art answerset solvers, and which supports a range of different logical semantics and policy options for resolving conflicts relating to the truth value or release status of fluents. The paper shows how XEC outperforms DEC on standard reasoning benchmarks, and how it can be used with an ILP system XHAIL to provide the first proof-of-principle demonstration of theory learning and revision in the full-featured DEC.
Modern product design and manufacturing process are highly integrated and exposed to frequent changes. This has made information reuse play an increasingly important role in improving the efficiency of the product dev...
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ISBN:
(纸本)9783642152450
Modern product design and manufacturing process are highly integrated and exposed to frequent changes. This has made information reuse play an increasingly important role in improving the efficiency of the product development process. Mechanical Assembly Sequence Planning (MASP) is a key issue in the manufacturing of a product. Known methods for MASP are not satisfactory from the aspect of information reuse. This paper proposes an answer set programming (ASP) based solution to MASP, where information reuse is enhanced by dividing an ASP program into EDB (extensional database) and IDB (intensional database) such that IDB can be shared by all the assemblies with the same number of parts. Compared with other approaches for MASP, this is a great advantage. Experiments are conducted to show the applicability and performance of our method by using different answerset solvers.
Conventional processor architectures are restricted in exploiting instruction level parallelism (ILP) due to the relatively low number of programmer-visible registers. Therefore, more recent processor architectures ex...
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Conventional processor architectures are restricted in exploiting instruction level parallelism (ILP) due to the relatively low number of programmer-visible registers. Therefore, more recent processor architectures expose their datapaths so that the compiler (1) can schedule parallel instructions to different processing units and (2) can make effective use of local storage of the processing units. Among these architectures, the Synchronous Control Asynchronous Dataflow (SCAD) architecture is a new exposed datapath architecture whose processing units are equipped with first-in first-out (FIFO) buffers at their input and output ports. In contrast to register-based machines, the optimal code generation for SCAD is still a matter of research. In particular, SAT and SMT solvers were used to generate optimal resource constrained and optimal time constrained schedules for SCAD, respectively. As answer set programming (ASP) offers better flexibility in handling such scheduling problems, we focus in this paper on using an answerset solver for both resource and time constrained optimal SCAD code generation. As a major benefit of using ASP, we are able to generate all optimal schedules for a given program which allows one to study their properties. Furthermore, the experimental results of this paper demonstrate that the answerset solver can compete with SAT solvers and outperforms SMT solvers. This paper is under consideration for acceptance in TPLP.
answer set programming is a well-understood and established problem-solving and knowledge representation paradigm. It has become more prominent amongst a wider audience due to its multiple applications in science and ...
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answer set programming is a well-understood and established problem-solving and knowledge representation paradigm. It has become more prominent amongst a wider audience due to its multiple applications in science and industry. The constant development of advanced programming and modeling techniques extends the toolset for developers and users regularly. This paper compiles and demonstrates different techniques to reuse logic program parts (multi-shot) by solving the arcade game snake. This game is particularly interesting because a victory can be assured by solving the NP-hard problem of Hamiltonian Cycles. We will demonstrate five hands-on implementations in clingo and compare their performance in an empirical evaluation. In addition, our implementation utilizes clingraph to generate a simple yet informative image representation of the game's progress.
The module theorem by Janhunen et al. demonstrates how to provide a modular structure in answer set programming, where each module has a well-defined input/output interface which can be used to establish the compositi...
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The module theorem by Janhunen et al. demonstrates how to provide a modular structure in answer set programming, where each module has a well-defined input/output interface which can be used to establish the compositionality of answersets. The theorem is useful in the analysis of answerset programs, and is a basis of incremental grounding and reactive answer set programming. We extend the module theorem to the general theory of stable models by Ferraris et al. The generalization applies to non-ground logic programs allowing useful constructs in answer set programming, such as choice rules, the count aggregate, and nested expressions. Our extension is based on relating the module theorem to the symmetric splitting theorem by Ferraris et al. Based on this result, we reformulate and extend the theory of incremental answerset computation to a more general class of programs.
Over the years the Artificial Intelligence (AI) community has produced several datasets which have given the machine learning algorithms the opportunity to learn various skills across various domains. However, a subcl...
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Over the years the Artificial Intelligence (AI) community has produced several datasets which have given the machine learning algorithms the opportunity to learn various skills across various domains. However, a subclass of these machine learning algorithms that aimed at learning logic programs, namely the Inductive Logic programming algorithms, have often failed at the task due to the vastness of these datasets. This has impacted the usability of knowledge representation and reasoning techniques in the development of AI systems. In this research, we try to address this scalability issue for the algorithms that learn answerset programs. We present a sound and complete algorithm which takes the input in a slightly different manner and performs an efficient and more user controlled search for a solution. We show via experiments that our algorithm can learn from two popular datasets from machine learning community, namely bAbl (a question answering dataset) and MNIST (a dataset for handwritten digit recognition), whicYh to the best of our knowledge was not previously possible. The system is publicly available at https://***/KdWAcV.
answer set programming (ASP) is a powerful form of declarative programming used in areas such as planning or reasoning. ASP solvers enforce stable model semantics, which rule out solutions representing certain kinds o...
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answer set programming (ASP) is a powerful form of declarative programming used in areas such as planning or reasoning. ASP solvers enforce stable model semantics, which rule out solutions representing certain kinds of circular reasoning. Unfortunately, current ASP solvers are incapable of solving problems involving cyclic dependencies between multiple integer or continuous quantities effectively. In this paper, we generalize the notion of stable models to bound founded variables with arbitrary domains, where bounds on such variables need to be justified by some rule in the program in order for the model to be stable. We show how to handle significantly more general rule forms where bound founded variables can act as head or body variables, and where head and body variables can be related via complex constraints subject to certain monotonicity requirements. We describe a new unfounded set detection algorithm which allows us to enforce this generalization of the stable model semantics. We also show how these unfounded sets can be explained in order to allow effective conflict-directed clause learning. The new solver merges the best features of CP, SAT and ASP solvers and allows new types of problems to be solved very efficiently.
The goal of this article is to foster modular program development in answer set programming using a Gaifman-Shapiro-style module architecture. More specifically, a method for verifying the equivalence of logic program...
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The goal of this article is to foster modular program development in answer set programming using a Gaifman-Shapiro-style module architecture. More specifically, a method for verifying the equivalence of logic program modules is devised and proved correct. The idea is to adapt a translation-based verification technique, which was originally devised for complete programs only, for program modules. In addition, optimization strategies are addressed in order to exploit the modular structure of programs in verification tasks. A number of experiments on verification strategies are also conducted using lpeq which implements the verification method for the smodels system. The preliminary experimental results reported in this article suggest that the modularization of equivalence verification leads to potential time savings especially if the modules involved share a common context.
The DLVHEX system implements the hex-semantics, which integrates answer set programming (ASP) with arbitrary external sources. Since its first release ten years ago, significant advancements were achieved. Most import...
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The DLVHEX system implements the hex-semantics, which integrates answer set programming (ASP) with arbitrary external sources. Since its first release ten years ago, significant advancements were achieved. Most importantly, the exploitation of properties of external sources led to efficiency improvements and flexibility enhancements of the language, and technical improvements on the system side increased user's convenience. In this paper, we present the current status of the system and point out the most important recent enhancements over early versions. While existing literature focuses on theoretical aspects and specific components, a bird's eye view of the overall system is missing. In order to promote the system for real-world applications, we further present applications which were already successfully realized on top of DLVHEX.
Causal discovery algorithms can induce some of the causal relations from the data, commonly in the form of a causal network such as a causal Bayesian network. Arguably however, all such algorithms lack far behind what...
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ISBN:
(纸本)9781450342322
Causal discovery algorithms can induce some of the causal relations from the data, commonly in the form of a causal network such as a causal Bayesian network. Arguably however, all such algorithms lack far behind what is necessary for a true business application. We develop an initial version of a new, general causal discovery algorithm called ETIO with many features suitable for business applications. These include (a) ability to accept prior causal knowledge (e.g., taking senior driving courses improves driving skills), (b) admitting the presence of latent confounding factors, (c) admitting the possibility of (a certain type of) selection bias in the data (e.g., clients sampled mostly from a given region), (d) ability to analyze data with missing-by-design (i.e., not planned to measure) values (e.g., if two companies merge and their databases measure different attributes), and (e) ability to analyze data from different interventions (e.g., prior and posterior to an advertisement campaign). ETIO is an instance of the logical approach to integrative causal discovery that has been relatively recently introduced and enables the solution of complex reverse-engineering problems in causal discovery. ETIO is compared against the state-of-the-art and is shown to be more effective in terms of speed, with only a slight degradation in terms of learning accuracy, while incorporating all the features above. The code is available on the *** website.
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