Pub Date : 2018-11-09DOI: 10.4230/OASIcs.ICLP.2018.16
Emily Leblanc
The study of actual causation concerns reasoning about events that have been instrumental in bringing about a particular outcome. Although the subject has long been studied in a number of fields including artificial intelligence, existing approaches have not yet reached the point where their results can be directly applied to explain causation in certain advanced scenarios, such as pin-pointing causes and responsibilities for the behavior of a complex cyber-physical system. We believe that this is due, at least in part, to a lack of distinction between the laws that govern individual states of the world and events whose occurrence cause state to evolve. In this paper, we present a novel approach to reasoning about actual causation that leverages techniques from Reasoning about Actions and Change to identify detailed causal explanations for how an outcome of interest came to be. We also present an implementation of the approach that leverages Answer Set Programming.
{"title":"Explaining Actual Causation via Reasoning About Actions and Change","authors":"Emily Leblanc","doi":"10.4230/OASIcs.ICLP.2018.16","DOIUrl":"https://doi.org/10.4230/OASIcs.ICLP.2018.16","url":null,"abstract":"The study of actual causation concerns reasoning about events that have been instrumental in bringing about a particular outcome. Although the subject has long been studied in a number of fields including artificial intelligence, existing approaches have not yet reached the point where their results can be directly applied to explain causation in certain advanced scenarios, such as pin-pointing causes and responsibilities for the behavior of a complex cyber-physical system. We believe that this is due, at least in part, to a lack of distinction between the laws that govern individual states of the world and events whose occurrence cause state to evolve. In this paper, we present a novel approach to reasoning about actual causation that leverages techniques from Reasoning about Actions and Change to identify detailed causal explanations for how an outcome of interest came to be. We also present an implementation of the approach that leverages Answer Set Programming.","PeriodicalId":271041,"journal":{"name":"International Conference on Logic Programming","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125451848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-07-30DOI: 10.4230/OASIcs.ICLP.2018.20
Richard Comploi-Taupe
The grounding bottleneck is an important open issue in Answer Set Programming. Lazy grounding addresses it by interleaving grounding and search. The performance of current lazy-grounding solvers is not yet comparable to that of ground-and-solve systems, however. The aim of this thesis is to extend prior work on lazy grounding by novel heuristics and other techniques like non-ground conflict learning in order to speed up solving. Parts of expected results will be beneficial for ground-and-solve systems as well.
{"title":"Speeding up Lazy-Grounding Answer Set Solving","authors":"Richard Comploi-Taupe","doi":"10.4230/OASIcs.ICLP.2018.20","DOIUrl":"https://doi.org/10.4230/OASIcs.ICLP.2018.20","url":null,"abstract":"The grounding bottleneck is an important open issue in Answer Set Programming. Lazy grounding addresses it by interleaving grounding and search. The performance of current lazy-grounding solvers is not yet comparable to that of ground-and-solve systems, however. The aim of this thesis is to extend prior work on lazy grounding by novel heuristics and other techniques like non-ground conflict learning in order to speed up solving. Parts of expected results will be beneficial for ground-and-solve systems as well.","PeriodicalId":271041,"journal":{"name":"International Conference on Logic Programming","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116516956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-07-01DOI: 10.4230/OASIcs.ICLP.2018.6
Joana Côrte-Real, Anton Dries, I. Dutra, Ricardo Rocha
Many real-world phenomena exhibit both relational structure and uncertainty. Probabilistic Inductive Logic Programming (PILP) uses Inductive Logic Programming (ILP) extended with probabilistic facts to produce meaningful and interpretable models for real-world phenomena. This merge between First Order Logic (FOL) theories and uncertainty makes PILP a very adequate tool for knowledge representation and extraction. However, this flexibility is coupled with a problem (inherited from ILP) of exponential search space growth and so, often, only a subset of all possible models is explored due to limited resources. Furthermore, the probabilistic evaluation of FOL theories, coming from the underlying probabilistic logic language and its solver, is also computationally demanding. This work introduces a prediction-based pruning strategy, which can reduce the search space based on the probabilistic evaluation of models, and a safe pruning criterion, which guarantees that the optimal model is not pruned away, as well as two alternative more aggressive criteria that do not provide this guarantee. Experiments performed using three benchmarks from different areas show that prediction pruning is effective in (i) maintaining predictive accuracy for all criteria and experimental settings; (ii) reducing the execution time when using some of the more aggressive criteria, compared to using no pruning; and (iii) selecting better candidate models in limited resource settings, also when compared to using no pruning. 2012 ACM Subject Classification Computing methodologies → Probabilistic reasoning
{"title":"Improving Candidate Quality of Probabilistic Logic Models","authors":"Joana Côrte-Real, Anton Dries, I. Dutra, Ricardo Rocha","doi":"10.4230/OASIcs.ICLP.2018.6","DOIUrl":"https://doi.org/10.4230/OASIcs.ICLP.2018.6","url":null,"abstract":"Many real-world phenomena exhibit both relational structure and uncertainty. Probabilistic Inductive Logic Programming (PILP) uses Inductive Logic Programming (ILP) extended with probabilistic facts to produce meaningful and interpretable models for real-world phenomena. This merge between First Order Logic (FOL) theories and uncertainty makes PILP a very adequate tool for knowledge representation and extraction. However, this flexibility is coupled with a problem (inherited from ILP) of exponential search space growth and so, often, only a subset of all possible models is explored due to limited resources. Furthermore, the probabilistic evaluation of FOL theories, coming from the underlying probabilistic logic language and its solver, is also computationally demanding. This work introduces a prediction-based pruning strategy, which can reduce the search space based on the probabilistic evaluation of models, and a safe pruning criterion, which guarantees that the optimal model is not pruned away, as well as two alternative more aggressive criteria that do not provide this guarantee. Experiments performed using three benchmarks from different areas show that prediction pruning is effective in (i) maintaining predictive accuracy for all criteria and experimental settings; (ii) reducing the execution time when using some of the more aggressive criteria, compared to using no pruning; and (iii) selecting better candidate models in limited resource settings, also when compared to using no pruning. 2012 ACM Subject Classification Computing methodologies → Probabilistic reasoning","PeriodicalId":271041,"journal":{"name":"International Conference on Logic Programming","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123359945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-04DOI: 10.4230/OASIcs.ICLP.2018.1
P. Kahl, A. Leclerc
An epistemic logic program is a set of rules written in the language of Epistemic Specifications, an extension of the language of answer set programming that provides for more powerful introspective reasoning through the use of modal operators K and M. We propose adding a new construct to Epistemic Specifications called a world view constraint that provides a universal device for expressing global constraints in the various versions of the language. We further propose the use of subjective literals (literals preceded by K or M) in rule heads as syntactic sugar for world view constraints. Additionally, we provide an algorithm for finding the world views of such programs.
{"title":"Epistemic Logic Programs with World View Constraints","authors":"P. Kahl, A. Leclerc","doi":"10.4230/OASIcs.ICLP.2018.1","DOIUrl":"https://doi.org/10.4230/OASIcs.ICLP.2018.1","url":null,"abstract":"An epistemic logic program is a set of rules written in the language of Epistemic Specifications, an extension of the language of answer set programming that provides for more powerful introspective reasoning through the use of modal operators K and M. We propose adding a new construct to Epistemic Specifications called a world view constraint that provides a universal device for expressing global constraints in the various versions of the language. We further propose the use of subjective literals (literals preceded by K or M) in rule heads as syntactic sugar for world view constraints. Additionally, we provide an algorithm for finding the world views of such programs.","PeriodicalId":271041,"journal":{"name":"International Conference on Logic Programming","volume":"289 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134145446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-02DOI: 10.4230/OASIcs.ICLP.2018.12
Benjamin Wu, A. Russo, Mark Law, Katsumi Inoue
One of the most difficult problems in Artificial Intelligence is related to acquiring commonsense knowledge -- to create a collection of facts and information that an ordinary person should know. In this work, we present a system that, from a limited background knowledge, is able to learn to form simple concepts through interactive dialogue with a user. We approach the problem using a syntactic parser, along with a mechanism to check for synonymy, to translate sentences into a logical formulas represented in Event Calculus using Answer Set Programming (ASP). Reasoning and learning tasks are then automatically generated for the translated text, with learning being initiated through question and answering. The system is capable of learning with no contextual knowledge prior to the dialogue. The system has been evaluated on stories inspired by the Facebook's bAbI's question-answering tasks, and through appropriate question and answering is able to respond accurately to these dialogues.
{"title":"Learning Commonsense Knowledge Through Interactive Dialogue","authors":"Benjamin Wu, A. Russo, Mark Law, Katsumi Inoue","doi":"10.4230/OASIcs.ICLP.2018.12","DOIUrl":"https://doi.org/10.4230/OASIcs.ICLP.2018.12","url":null,"abstract":"One of the most difficult problems in Artificial Intelligence is related to acquiring commonsense knowledge -- to create a collection of facts and information that an ordinary person should know. In this work, we present a system that, from a limited background knowledge, is able to learn to form simple concepts through interactive dialogue with a user. We approach the problem using a syntactic parser, along with a mechanism to check for synonymy, to translate sentences into a logical formulas represented in Event Calculus using Answer Set Programming (ASP). Reasoning and learning tasks are then automatically generated for the translated text, with learning being initiated through question and answering. The system is capable of learning with no contextual knowledge prior to the dialogue. The system has been evaluated on stories inspired by the Facebook's bAbI's question-answering tasks, and through appropriate question and answering is able to respond accurately to these dialogues.","PeriodicalId":271041,"journal":{"name":"International Conference on Logic Programming","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124470899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-02-01DOI: 10.4230/OASIcs.ICLP.2017.8
Rolf Schwitter
In this paper we showed how we can automatically learn the structure and parameters of probabilistic effect axioms for the Simple Event Calculus (SEC) from positive and negative example interpretations stated as short dialogue sequences in natural language. We used the cplint framework for this task that provides libraries for structure and parameter learning and for answering queries with exact and inexact inference. The example dialogues that are used for learning the structure of the probabilistic logic program are parsed into dependency structures and then further translated into the Event Calculus notation with the help of a simple ontology. The novelty of our approach is that we can not only process uncertainty in event recognition but also learn the structure of effect axioms and combine these two sources of uncertainty to successfully answer queries under this probabilistic setting. Interestingly, our extension of the logic-based version of the SEC is completely elaboration-tolerant in the sense that the probabilistic version fully includes the logic-based version. This makes it possible to use the probabilistic version of the SEC in the traditional way as well as when we have to deal with uncertainty in the observed world. In the future, we would like to extend the probabilistic version of the SEC to deal -- among other things -- with concurrent actions and continuous change.
{"title":"Learning Effect Axioms via Probabilistic Logic Programming","authors":"Rolf Schwitter","doi":"10.4230/OASIcs.ICLP.2017.8","DOIUrl":"https://doi.org/10.4230/OASIcs.ICLP.2017.8","url":null,"abstract":"In this paper we showed how we can automatically learn the structure and parameters of probabilistic effect axioms for the Simple Event Calculus (SEC) from positive and negative example interpretations stated as short dialogue sequences in natural language. We used the cplint framework for this task that provides \u0000libraries for structure and parameter learning and for answering queries with exact and inexact inference. The example dialogues that are used for learning the structure of the probabilistic logic program are parsed into dependency structures and then further translated into the Event Calculus notation with the help of a simple ontology. The novelty of our approach is that we can not only process uncertainty in event recognition but also learn the structure of effect axioms and combine these two sources of uncertainty to successfully answer queries under this probabilistic setting. Interestingly, our extension of the logic-based version of the SEC is completely elaboration-tolerant in the sense that the probabilistic version fully includes the logic-based version. This makes it possible to use the probabilistic version of the SEC in the traditional way as well as when we have to deal with uncertainty in the observed world. In the future, we would like to extend the probabilistic version of the SEC to deal -- among other things -- with concurrent actions and continuous change.","PeriodicalId":271041,"journal":{"name":"International Conference on Logic Programming","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129723443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-09-26DOI: 10.4230/OASIcs.ICLP.2016.16
Michell Guzmán, F. Valencia
Epistemic, mobile and spatial behaviour are common place in today’s distributed systems. The intrinsic epistemic nature of these systems arises from the interactions of the elements taking part of them. Most people are familiar with digital systems where users share their beliefs, opinions and even intentional lies (hoaxes). Models of those systems must take into account the interactions with others as well as the distributed quality these systems present. Spatial and mobile behaviour are exhibited by applications and data moving across (possibly nested) spaces defined by, for example, friend circles, groups, and shared folders. We therefore believe that a solid understanding of the notion of space and spatial mobility as well as the flow of epistemic information is relevant in many models of today’s distributed systems.Constraint systems (cs’s) provide the basic domains and opera- tions for the semantic foundations of the family of formal declarative models from concurrency theory known as concurrent constraint programming (ccp). Spatial constraint systems (scs’s) are algebraic structures that extend cs’s for reasoning about basic spatial and epistemic behaviour such as belief and extrusion. Both spatial and epistemic assertions can be viewed as specific modalities. Other modalities can be used for assertions about time, knowledge and even the analysis of groups among other concepts used in the specification and verification of concurrent systems.In this thesis we study the expressiveness of spatial constraint systems in the broader perspective of modal and epistemic behaviour. We shall show that spatial constraint systems are sufficiently robust to capture inverse modalities and to derive new results for modal logics. We shall show that we can use scs’s to express a fundamental epistemic behaviour such as knowledge. Finally we shall give an algebraic characterization of the notion of distributed information by means of constructors over scs’s.
{"title":"On the Expressiveness of Spatial Constraint Systems","authors":"Michell Guzmán, F. Valencia","doi":"10.4230/OASIcs.ICLP.2016.16","DOIUrl":"https://doi.org/10.4230/OASIcs.ICLP.2016.16","url":null,"abstract":"Epistemic, mobile and spatial behaviour are common place in today’s distributed systems. The intrinsic epistemic nature of these systems arises from the interactions of the elements taking part of them. Most people are familiar with digital systems where users share their beliefs, opinions and even intentional lies (hoaxes). Models of those systems must take into account the interactions with others as well as the distributed quality these systems present. Spatial and mobile behaviour are exhibited by applications and data moving across (possibly nested) spaces defined by, for example, friend circles, groups, and shared folders. We therefore believe that a solid understanding of the notion of space and spatial mobility as well as the flow of epistemic information is relevant in many models of today’s distributed systems.Constraint systems (cs’s) provide the basic domains and opera- tions for the semantic foundations of the family of formal declarative models from concurrency theory known as concurrent constraint programming (ccp). Spatial constraint systems (scs’s) are algebraic structures that extend cs’s for reasoning about basic spatial and epistemic behaviour such as belief and extrusion. Both spatial and epistemic assertions can be viewed as specific modalities. Other modalities can be used for assertions about time, knowledge and even the analysis of groups among other concepts used in the specification and verification of concurrent systems.In this thesis we study the expressiveness of spatial constraint systems in the broader perspective of modal and epistemic behaviour. We shall show that spatial constraint systems are sufficiently robust to capture inverse modalities and to derive new results for modal logics. We shall show that we can use scs’s to express a fundamental epistemic behaviour such as knowledge. Finally we shall give an algebraic characterization of the notion of distributed information by means of constructors over scs’s.","PeriodicalId":271041,"journal":{"name":"International Conference on Logic Programming","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123244529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-11-01DOI: 10.4230/OASIcs.ICLP.2016.20
Alexander Vandenbroucke
The basics of logic programming, as embodied by Prolog, are generally well-known in the programming language community. However, more advanced techniques, such as tabling, answer subsumption and probabilistic logic programming fail to attract the attention of a larger audience. The cause for the community's seemingly limited interest lies with the presentation of these features: the literature frequently focuses on implementations and examples that do little to aid the understanding of non-experts in the field. The key point is that many of these advanced logic programming features can be characterised in more generally known, more accessible terms. In my research I try to reconcile these advanced concepts from logic programming (Tabling, Answer subsumption and probabilistic programming) with concepts from functional programming (effects, monads and applicative functors).
{"title":"The Functional Perspective on Advanced Logic Programming","authors":"Alexander Vandenbroucke","doi":"10.4230/OASIcs.ICLP.2016.20","DOIUrl":"https://doi.org/10.4230/OASIcs.ICLP.2016.20","url":null,"abstract":"The basics of logic programming, as embodied by Prolog, are generally well-known in the programming language community. However, more advanced techniques, such as tabling, answer subsumption and probabilistic logic programming fail to attract the attention of a larger audience. The cause for the community's seemingly limited interest lies with the presentation of these features: the literature frequently focuses on implementations and examples that do little to aid the understanding of non-experts in the field. The key point is that many of these advanced logic programming features can be characterised in more generally known, more accessible terms. In my research I try to reconcile these advanced concepts from logic programming (Tabling, Answer subsumption and probabilistic programming) with concepts from functional programming (effects, monads and applicative functors).","PeriodicalId":271041,"journal":{"name":"International Conference on Logic Programming","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131060515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-11-01DOI: 10.4230/OASIcs.ICLP.2016.5
J. Bomanson, M. Gebser, T. Janhunen
Constraints on Pseudo-Boolean (PB) expressions can be translated into Conjunctive Normal Form (CNF) using several known translations. In Answer-Set Programming (ASP), analogous expressions appear in weight rules and optimization statements. Previously, we have translated weight rules into normal rules, using normalizations designed in accord with existing CNF encodings. In this work, we rededicate such designs to rewrite optimization statements in ASP. In this context, a rewrite of an optimization statement is a replacement accompanied by a set of normal rules that together replicate the original meaning. The goal is partially the same as in translating PB constraints or weight rules: to introduce new meaningful auxiliary atoms that may help a solver in the search for (optimal) solutions. In addition to adapting previous translations, we present selective rewriting techniques in order to meet the above goal while using only a limited amount of new rules and atoms. We experimentally evaluate these methods in preprocessing ASP optimization statements and then searching for optimal answer sets. The results exhibit significant advances in terms of numbers of optimally solved instances, reductions in search conflicts, and shortened computation times. By appropriate choices of rewriting techniques, improvements are made on instances involving both small and large weights. In particular, we show that selective rewriting is paramount on benchmarks involving large weights.
{"title":"Rewriting Optimization Statements in Answer-Set Programs","authors":"J. Bomanson, M. Gebser, T. Janhunen","doi":"10.4230/OASIcs.ICLP.2016.5","DOIUrl":"https://doi.org/10.4230/OASIcs.ICLP.2016.5","url":null,"abstract":"Constraints on Pseudo-Boolean (PB) expressions can be translated into Conjunctive Normal Form (CNF) using several known translations. In Answer-Set Programming (ASP), analogous expressions appear in weight rules and optimization statements. Previously, we have translated weight rules into normal rules, using normalizations designed in accord with existing CNF encodings. In this work, we rededicate such designs to rewrite optimization statements in ASP. In this context, a rewrite of an optimization statement is a replacement accompanied by a set of normal rules that together replicate the original meaning. The goal is partially the same as in translating PB constraints or weight rules: to introduce new meaningful auxiliary atoms that may help a solver in the search for (optimal) solutions. In addition to adapting previous translations, we present selective rewriting techniques in order to meet the above goal while using only a limited amount of new rules and atoms. We experimentally evaluate these methods in preprocessing ASP optimization statements and then searching for optimal answer sets. The results exhibit significant advances in terms of numbers of optimally solved instances, reductions in search conflicts, and shortened computation times. By appropriate choices of rewriting techniques, improvements are made on instances involving both small and large weights. In particular, we show that selective rewriting is paramount on benchmarks involving large weights.","PeriodicalId":271041,"journal":{"name":"International Conference on Logic Programming","volume":"156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122942721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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