Datalog, a bottom-up declarative logic programming language, has a wide variety of uses for deduction, modeling, and data analysis, across application domains. Datalog can be efficiently implemented using relational a...
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ISBN:
(纸本)9781665475075
Datalog, a bottom-up declarative logic programming language, has a wide variety of uses for deduction, modeling, and data analysis, across application domains. Datalog can be efficiently implemented using relational algebra primitives such as join, projection and union. While there exist several multi-threaded and multi-core implementations of Datalog, targeting CPU-based systems, our work makes an inroad towards developing a Datalog implementation for GPUs. We demonstrate the feasibility of a high-performance relational algebra backend for a subset of Datalog applications that can effectively leverage the parallelism of GPUs using cuDF. cuDF is a library from the Rapids suite that uses the NVIDIA CUDA programming model for GPU parallelism. It provides similar functionalities to Pandas, a popular data analysis engine. In this paper, we analyze and evaluate the performance of cuDF versus Pandas for two graph-mining problems implemented in Datalog, (1) triangle counting and (2) transitive-closure computation.
This report presents an elementary theory of unification for positive conjunctive queries. A positive conjunctive query is a formula constructed from propositional constants, equations and atoms using the conjunction ...
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The paper studies defeasible reasoning in rule-based systems, in particular about legal norms and contracts. We identify rule modifiers that specify how rules interact and how they can be overridden. We then define ru...
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In this paper, we apply incremental answer set solving to product configuration. Incremental answer set solving is a stepwise incremental approach to Answer Set programming (ASP). We demonstrate how to use this techni...
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If there is a format that is easy for both humans and AI (computers) to understand and infer, and also if the humans let the AI learn various morals, ethics and values in the above format, these data may be operated a...
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ISBN:
(纸本)9781665480468
If there is a format that is easy for both humans and AI (computers) to understand and infer, and also if the humans let the AI learn various morals, ethics and values in the above format, these data may be operated automatically by the AI in a way that the humans concerned are able to check for mistakes / lacks of thoughts in the automatic judgments by the AI, avoiding arbitrary thoughts / judgments by both the providers(humans) of such data and the *** the other hand, morals, etc. are diverse depending on the country, history, culture, social position, and/or individual. So in order to accurately express such data for reducing interpretation mistakes, lacks of required conditions of thought-rules and so on, we need to allow, as such data, expressions almost in (or from completely including all) the natural languages used in each cultural *** this paper, we (1) propose a format based on natural language that we called as "situation-specific-value-order-formula" as a starting point for an expression format that satisfy the above needs, (2) explain its components: situations / viewpoints and items ( positive / 0 / negative ) ordered by the value all based on natural language, (3) explain related things including the specific l ogical i nference f eature o f t he n atural language based programming where variables are embedded in the natural *** conclude that our proposed format is feasible and practical to some extent for the above Purpose and need.
The development of complex software requires tools promoting fail-fast approaches, so that bugs and unexpected behavior can be quickly identified and fixed. Tools for data validation may save the day of computer progr...
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Modern scientific software stacks have become extremely complex, using many programming models and libraries to exploit a growing variety of GPUs and accelerators. Package managers can mitigate this complexity using d...
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Modern scientific software stacks have become extremely complex, using many programming models and libraries to exploit a growing variety of GPUs and accelerators. Package managers can mitigate this complexity using dependency solvers, but they are reaching their limits. Finding compatible dependency versions is NP-complete, and modeling the semantics of package compatibility modulo build-time options, GPU runtimes, flags, and other parameters is extremely difficult. Within this enormous configuration space, defining a "good" configuration is *** tackle this problem using Answer Set programming (ASP), a declarative model for combinatorial search problems. We show, using the Spack package manager, that ASP programs can concisely express the compatibility rules of HPC software stacks and provide strong quality-of-solution guarantees. Using ASP, we can mix new builds with preinstalled binaries, and solver performance is acceptable even when considering tens of thousands of packages.
The decoupling between the representation of a certain problem, i.e., its knowledge model, and the reasoning side is one of main strong points of model-based Artificial Intelligence (AI). This allows, e.g. to focus on...
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Justification theory is a unifying semantic framework. While it has its roots in non-monotonic logics, it can be applied to various areas in computer science, especially in explainable reasoning;its most central conce...
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Justification theory is a unifying semantic framework. While it has its roots in non-monotonic logics, it can be applied to various areas in computer science, especially in explainable reasoning;its most central concept is a justification: an explanation why a property holds (or does not hold) in a model. In this paper, we continue the study of justification theory by means of three major contributions. The first is studying the relation between justification theory and game theory. We show that justification frameworks can be seen as a special type of games. The established connection provides the theoretical foundations for our next two contributions. The second contribution is studying under which condition two different dialects of justification theory (graphs as explanations vs trees as explanations) coincide. The third contribution is establishing a precise criterion of when a semantics induced by justification theory yields consistent results. In the past proving that such semantics were consistent took cumbersome and elaborate proofs. We show that these criteria are indeed satisfied for all common semantics of logic programming.
Multi-agent pathfinding is the problem of finding collision-free paths for a set of agents. Solving this problem optimally is computationally hard, therefore many techniques based on reductions to other formalisms wer...
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ISBN:
(纸本)9781450392136
Multi-agent pathfinding is the problem of finding collision-free paths for a set of agents. Solving this problem optimally is computationally hard, therefore many techniques based on reductions to other formalisms were developed. In comparison to search-based techniques, the reduction-based techniques fall behind on large maps even for a small number of agents. To combat this phenomenon, we propose several strategies for pruning vertices off large instances that will most likely not be used by agents. First, we introduce these strategies conceptually and prove which of them maintain completeness and optimality. Eventually, we conduct an exhaustive evaluation and show that graph pruning strategies make reduction-based solvers comparable to search-based techniques on large maps while maintaining their advantage on small dense maps.
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