answer set programming (ASP) has been applied fruitfully to a wide range of areas in AI and in other fields, both in academia and in industry, thanks to the expressive representation languages of ASP and the continuou...
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answer set programming (ASP) has been applied fruitfully to a wide range of areas in AI and in other fields, both in academia and in industry, thanks to the expressive representation languages of ASP and the continuous improvement of ASP solvers. We present some of these ASP applications, in particular, in knowledge representation and reasoning, robotics, bioinformatics, and computational biology as well as some industrial applications. We discuss the challenges addressed by ASP in these applications and emphasize the strengths of ASP as a useful Al paradigm.
Epistemic negation not along with default negation (sic) plays a key role in knowledge representation and nonmonotonic reasoning. However, the existing epistemic approaches such as those by Gelfond [13,15,14], Truszcz...
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Epistemic negation not along with default negation (sic) plays a key role in knowledge representation and nonmonotonic reasoning. However, the existing epistemic approaches such as those by Gelfond [13,15,14], Truszczynski [33] and Kahl et al. [18] behave not satisfactorily in that they suffer from the problems of unintended world views due to recursion through the epistemic modal operator K or M (KF and MF are shorthands for (sic)not F and not(sic)F, respectively). In this paper we present a new approach to handling epistemic negation which is free of unintended world views and thus offers a solution to the long-standing problem of epistemic specifications which were introduced by Gelfond [13] over two decades ago. We consider general logic programs consisting of rules of the form H <- B, where H and B are arbitrary first-order formulas possibly containing epistemic negation, and define a general epistemic answerset semantics for general logic programs by introducing a novel program transformation and a new definition of world views in which we apply epistemic negation to minimize the knowledge in world views. The general epistemic semantics is applicable to extend any existing answerset semantics, such as those defined in [26,27,32,1,8,12,29], with epistemic negation. For illustration, we extend FLP answerset semantics of Faber et al. [8] for general logic programs with epistemic negation, leading to epistemic FLP semantics. We also extend the more restrictive well justified FLP semantics of Shen et al. [29], which is free of circularity for default negation, to an epistemic well justified semantics. We consider the computational complexity of epistemic FLP semantics and show that for a propositional program II with epistemic negation, deciding whether II has epistemic FLP answersets is Sigma(p)(3)-complete and deciding whether a propositional formula F is true in II under epistemic FLP semantics is Sigma(p)(4)-complete in general, but has lower complexity for
Notwithstanding the advancement of service computing in recent years, service composition is still main issue in this field. In this paper, the authors present an integrated framework for semantic service composition ...
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Notwithstanding the advancement of service computing in recent years, service composition is still main issue in this field. In this paper, the authors present an integrated framework for semantic service composition using answer set programming. Unlike the AI planning approaches of top-down workflow with nested composition and combining composition procedure into service discovery, this proposed framework integrates designed workflow with nested composition. In addition, the planning is based on interface variables with validation through pre and post conditions. Moreover, a unified implementation of service discovery, selection, composition and validation is achieved by answer set programming. Finally, the framework performance is demonstrated by a travel booking example on QWSDataset.
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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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 programmers. In fact, processing invalid data is a waste of resources at best, and a drama at worst if the problem remains unnoticed and wrong results are used for business. answer set programming (ASP) is not an exception, but the quest for better and better performance resulted in systems that essentially do not validate data. Even under the simplistic assumption that input/output data are eventually validated by external tools, invalid data may appear in other portions of the program, and go undetected until some other module of the designed software suddenly breaks. This paper formalizes the problem of data validation for ASP programs, introduces a language to specify data validation, and presents valasp, a tool to inject data validation in ordinary programs. The proposed approach promotes fail-fast techniques at coding time without imposing any lag on the deployed system if data are pretended to be valid. Validation can be specified in terms of statements using YAML, ASP and Python. Additionally, the proposed approach opens the possibility to use ASP for validating data of imperative programming languages.
Clinical Guidelines (CGs) provide general evidence-based recommendations and physicians often have to resort also to their Basic Medical Knowledge (BMK) to cope with specific patients. In this paper, we explore the in...
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Clinical Guidelines (CGs) provide general evidence-based recommendations and physicians often have to resort also to their Basic Medical Knowledge (BMK) to cope with specific patients. In this paper, we explore the interplay between CGs and BMK from the viewpoint of a-posteriori conformance analysis, intended as the adherence of a specific execution log to both the CG and the BMK. In this paper, we consider also the temporal dimension: the guideline may include temporal constraints for the execution of actions, and its adaptation to a specific patient and context may add or modify conditions and temporal constraints for actions. We propose an approach for analyzing execution traces in answer set programming with respect to a guideline and BMK, pointing out discrepancies - including temporal discrepancies - with respect to the different knowledge sources, and providing explanations regarding how the applications of the CG and the BMK have interacted, especially in case strictly adhering to both is not possible.
Spatial puzzles composed of rigid objects, flexible strings and holes offer interesting challenges for reasoning about spatial entities that are common in the human daily-life's activities. This motivates the use ...
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Spatial puzzles composed of rigid objects, flexible strings and holes offer interesting challenges for reasoning about spatial entities that are common in the human daily-life's activities. This motivates the use of spatial puzzles as domains of study in this work. The goal of this paper is to investigate the automated solution of this kind of problems by extending an algorithm that combines answer set programming (ASP) with Markov Decision Process (MDP) and Reinforcement Learning (RL), called oASP(MDP). This method is capable of constructing the set of domain states online, i.e., while the agent interacts with a changing environment. The aim of the extension proposed in this work is to add heuristics as a mechanism to accelerate the learning process, resulting in the main contribution of this paper: the Heuristic oASP(MDP) (HoASP(MDP)) algorithm. Experiments were performed on deterministic, non-deterministic and non-stationary versions of the puzzles. Results show that the proposed approach can considerably accelerate the learning process, outperforming other state-of-the-art methods.
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...
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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.
We investigate the usage of rule dependency graphs and their colorings for characterizing and computing answersets of logic programs. This approach provides us with insights into the interplay between rules when indu...
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We investigate the usage of rule dependency graphs and their colorings for characterizing and computing answersets of logic programs. This approach provides us with insights into the interplay between rules when inducing answersets. We start with different characterizations of answersets in terms of totally colored dependency graphs that differ ill graph-theoretical aspects. We then develop a series of operational characterizations of answersets in terms of operators on partial colorings. In analogy to the notion of a derivation in proof theory, our operational characterizations are expressed as (non-deterministically formed) sequences of colorings, turning an uncolored graph into a totally colored one. In this way, we obtain an operational framework in which different combinations of operators result in different formal properties. Among others, we identify the basic strategy employed by the noMoRe system and justify its algorithmic approach. Furthermore, we distinguish operations corresponding to Fitting's operator as well as to well-founded semantics.
This article studies a semantics of multiple logic programs, and synthesizes a program having such a collective semantics. More precisely, the following two problems are considered: given two logic programs P-1 and P-...
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This article studies a semantics of multiple logic programs, and synthesizes a program having such a collective semantics. More precisely, the following two problems are considered: given two logic programs P-1 and P-2, which have the collections of answersets AS(P-1) and AS(P-2), respectively;(i) find a program Q which has the set of answersets such that AS(Q) = AS(P-1). AS(P2);(ii) find a program R which has the set of answersets such that AS(R) = AS(P1) boolean AND AS(P2). A program Q satisfying the condition (i) is called generous coordination of P-1 and P-2;and R satisfying (ii) is called rigorous coordination of P-1 and P-2. Generous coordination retains all of the answersets of each program, but permits the introduction of additional answersets of the other program. By contrast, rigorous coordination forces each program to give up some answersets, but the result remains within the original answersets for each program. Coordination provides a program that reflects the meaning of two or more programs. We provide methods for constructing these two types of coordination and address its application to logic-based multi-agent systems.
answer set programming is a declarative problem-solving paradigm that rests upon a work flow involving modeling, grounding, and solving. While the former is described by Gebser and Schaub (2016), we focus here on key ...
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answer set programming is a declarative problem-solving paradigm that rests upon a work flow involving modeling, grounding, and solving. While the former is described by Gebser and Schaub (2016), we focus here on key issues in grounding, or how to systematically replace object variables by ground terms in an effective way, and solving, or how to compute the answersets, of a propositional logic program obtained by grounding.
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