Generative Datalog is an extension of Datalog that incorporates constructs for referencing parameterized probability distributions. This augmentation transforms the evaluation of a Generative Datalog program into a st...
详细信息
ISBN:
(纸本)9783031436185;9783031436192
Generative Datalog is an extension of Datalog that incorporates constructs for referencing parameterized probability distributions. This augmentation transforms the evaluation of a Generative Datalog program into a stochastic process, resulting in a declarative formalism suitable for modeling and analyzing other stochastic processes. This work provides an introduction to Generative Datalog through the lens of answer set programming (ASP), demonstrating how Generative Datalog can explain the output of ASP systems that include @-terms referencing probability distributions. From a theoretical point of view, extending the semantics of Generative Datalog to stable negation proved to be challenging due to the richness of ASP relative to Datalog in terms of linguistic constructs. On a more pragmatic side, the connection between the two formalisms lays the foundation for implementing Generative Datalog atop efficient ASP systems, making it a practical solution for real-world applications.
The Hamiltonian cycle reconfiguration problem is defined as determining, for a given Hamiltonian cycle problem and two among its feasible solutions, whether one is reachable from another via a sequence of feasible sol...
详细信息
ISBN:
(纸本)9783031436185;9783031436192
The Hamiltonian cycle reconfiguration problem is defined as determining, for a given Hamiltonian cycle problem and two among its feasible solutions, whether one is reachable from another via a sequence of feasible solutions subject to certain transition constraints. We develop an approach to solving the Hamiltonian cycle reconfiguration problem based on answer set programming (ASP). Our approach relies on a high-level ASP encoding and delegates both the grounding and solving tasks to an ASP-based solver. To show the effectiveness of our approach, we conduct experiments on the benchmark set of Flinders Hamiltonian Cycle Project.
In this paper we develop a state transition function for partially observable multi-agent epistemic domains and implement it using answer set programming (ASP). The transition function computes the next state upon an ...
详细信息
ISBN:
(纸本)9783031157073;9783031157066
In this paper we develop a state transition function for partially observable multi-agent epistemic domains and implement it using answer set programming (ASP). The transition function computes the next state upon an occurrence of a single action. Thus it can be used as a module in epistemic planners. Our transition function incorporates ontic, sensing and announcement actions and allows for arbitrary nested belief formulae and general common knowledge. A novel feature of our model is that upon an action occurrence, an observing agent corrects his (possibly wrong) initial beliefs about action precondition and his observability. By examples, we show that this step is necessary for robust state transition. We establish some properties of our state transition function regarding its soundness in updating beliefs of agents consistent with their observability.
Declarative business process discovery aims at identifying sets of constraints, from a given formal language, that characterise a workflow by using pre-recorded activity logs. Since the provided logs represent a fract...
详细信息
ISBN:
(纸本)9783031157073;9783031157066
Declarative business process discovery aims at identifying sets of constraints, from a given formal language, that characterise a workflow by using pre-recorded activity logs. Since the provided logs represent a fraction of all the consistent evolution of a process, and the fact that many sets of constraints covering those examples can be selected, empirical criteria should be employed to identify the "best" candidates. In our work we frame the process discovery as an optimisation problem, where we want to identify optimal sets of constraints according to preference criteria. Declarative constraints for processes are usually characterised via temporal logics, so different solutions can be semantically equivalent. For this reason, it is difficult to use an arbitrary finite domain constraints solvers for the optimisation. The use of answer set programming enables the combination of deduction rules within the optimisation algorithm, in order to take into account not only the user preferences but also the implicit semantics of the formal language. In this paper we show how we encoded the process discovery problem using the ASPrin framework for qualitative and quantitative optimisation in ASP, and the results of our experiments.
We present a general approach to planning with incomplete information in answer set programming (ASP). More precisely, we consider the problems of conformant and conditional planning with sensing actions and assumptio...
详细信息
We present a general approach to planning with incomplete information in answer set programming (ASP). More precisely, we consider the problems of conformant and conditional planning with sensing actions and assumptions. We represent planning problems using a simple formalism where logic programs describe the transition function between states, the initial states and the goal states. For solving planning problems, we use Quantified answer set programming (QASP), an extension of ASP with existential and universal quantifiers over atoms that is analogous to Quantified Boolean Formulas (QBFs). We define the language of quantified logic programs and use it to represent the solutions different variants of conformant and conditional planning. On the practical side, we present a translation-based QASP solver that converts quantified logic programs into QBFs and then executes a QBF solver, and we evaluate experimentally the approach on conformant and conditional planning benchmarks.
Modal logic S5 has attracted significant attention and has led to several practical applications, owing to its simplified approach to dealing with nesting modal operators. Efficient implementations for evaluating sati...
详细信息
Modal logic S5 has attracted significant attention and has led to several practical applications, owing to its simplified approach to dealing with nesting modal operators. Efficient implementations for evaluating satisfiability of S5 formulas commonly rely on Skolemisation to convert them into propositional logic formulas, essentially by introducing copies of propositional atoms for each set of interpretations (possible worlds). This approach is simple, but often results into large formulas that are too difficult to process, and therefore more parsimonious constructions are required. In this work, we propose to use answer set programming for implementing such constructions, and in particular for identifying the propositional atoms that are relevant in every world by means of a reachability relation. The proposed encodings are designed to take advantage of other properties such as entailment relations of subformulas rooted by modal operators. An empirical assessment of the proposed encodings shows that the reachability relation is very effective and leads to comparable performance to a state-of-the-art S5 solver based on SAT, while entailment relations are possibly too expensive to reason about and may result in overhead.
Declarative methods such as answer set programming show potential in cutting down development costs in commercial videogames and real-time applications in general. Many shortcomings, however, prevent their adoption, s...
详细信息
ISBN:
(数字)9781665459891
ISBN:
(纸本)9781665459891
Declarative methods such as answer set programming show potential in cutting down development costs in commercial videogames and real-time applications in general. Many shortcomings, however, prevent their adoption, such as performance and integration gaps. In this work we illustrate our ThinkEngine, a framework in which a tight integration of declarative formalisms within the typical game development workflow is made possible in the context of the Unity game engine. ThinkEngine allows to wire declarative AI modules to the game logic and to move the computational load of reasoning tasks outside the main game loop using an hybrid deliberative/reactive architecture. In this paper, we illustrate the architecture of the ThinkEngine and its role both at design and run-time. Then we show how to program declarative modules in a proof-of-concept game, and report about performance and related work.
作者：
Hashimoto, RyuOzaki, TomonobuNihon Univ
Grad Sch Integrated Basic Sci Setagaya Ward 3-25-40 Sakurajosui Tokyo 1568550 Japan Nihon Univ
Dept Informat Sci Setagaya Ward 3-25-40 Sakurajosui Tokyo 1568550 Japan
The preference criteria for real estate rental properties strongly depend on the form of use and purposes. If it is possible to acquire the preference criteria on each purpose of use in a human-understandable form, th...
详细信息
ISBN:
(纸本)9781665475327
The preference criteria for real estate rental properties strongly depend on the form of use and purposes. If it is possible to acquire the preference criteria on each purpose of use in a human-understandable form, they can be useful information for each of user, lenders and developers of rental properties. In this research, using the framework of preference and classification learning on answer set programming (ASP), we attempt to acquire preference criteria from floor plans of rental properties. Specifically, from a ranking of rental properties on intended purpose, inductive preference learning of ASP is applied to extract weak constraints corresponding to ranking functions, and classification learning is performed to obtain rules representing distinguish features between high and low ranked properties. The usefulness of our ASP-based analysis is evaluated by examining the results of the learnings for three real rankings of nine rental properties.
answer set programming (ASP) is a well-established declarative AI formalism for knowledge representation and reasoning. ASP systems were successfully applied to both industrial and academic problems. Nonetheless, thei...
详细信息
ISBN:
(纸本)9783031157073;9783031157066
answer set programming (ASP) is a well-established declarative AI formalism for knowledge representation and reasoning. ASP systems were successfully applied to both industrial and academic problems. Nonetheless, their performance can be improved by embedding domain-specific heuristics into their solving process. However, the development of domain-specific heuristics often requires both a deep knowledge of the domain at hand and a good understanding of the fundamental working principles of the ASP solvers. In this paper, we investigate the use of deep learning techniques to automatically generate domain-specific heuristics for ASP solvers targeting the well-known graph coloring problem. Empirical results show that the idea is promising: the performance of the ASP solver WASP can be improved.
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...
详细信息
ISBN:
(纸本)9781665454445
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 daunting. We 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.
暂无评论