Time最新文献

{"title":"On Restricted Disjunctive Temporal Problems: Faster Algorithms and Tractability Frontier","authors":"Carlo Comin, Romeo Rizzi","doi":"10.4230/LIPIcs.TIME.2018.10","DOIUrl":"https://doi.org/10.4230/LIPIcs.TIME.2018.10","url":null,"abstract":"In 2005 Kumar studied the Restricted Disjunctive Temporal Problem (RDTP), a restricted but very expressive class of disjunctive temporal problems (DTPs). It was shown that that RDTPs are solvable in deterministic strongly-polynomial time by reducing them to the Connected Row-Convex (CRC) constraints problem; plus, Kumar devised a randomized algorithm whose expected running time is less than that of the deterministic one. Instead, the most general form of DTPs allows for multi-variable disjunctions of many interval constraints and it is NP-complete. \u0000This work offers a deeper comprehension on the tractability of RDTPs, leading to an elementary deterministic strongly-polynomial time algorithm for them, significantly improving the asymptotic running times of both the deterministic and randomized algorithms of Kumar. The result is obtained by reducing RDTPs to the Single-Source Shortest-Paths (SSSP) and the 2-SAT problem (jointly), instead of reducing to CRCs. In passing, we obtain a faster (quadratic-time) algorithm for RDTPs having only Type-1 and Type-2 constraints (and no Type-3 constraint). As a second main contribution, we study the tractability frontier of solving RDTPs by considering Hyper Temporal Networks (HTNs), a strict generalization of STNs grounded on hypergraphs: on one side, we prove that solving temporal problems having only Type-2 constraints and either only multi-tail or only multi-head hyperarc constraints lies in both NP and co-NP and it admits deterministic pseudo-polynomial time algorithms; on the other side, solving problems with Type-3 constraints and either only multi-tail or only multi-head hyperarc constraints turns strongly NP-complete.","PeriodicalId":75226,"journal":{"name":"Time","volume":"1 1","pages":"10:1-10:20"},"PeriodicalIF":0.0,"publicationDate":"2018-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47180339","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}

{"title":"Predicting the Evolution of Communities with Online Inductive Logic Programming","authors":"G. Athanasopoulos, G. Paliouras, D. Vogiatzis, Grigorios Tzortzis, Nikos Katzouris","doi":"10.4230/LIPIcs.TIME.2018.4","DOIUrl":"https://doi.org/10.4230/LIPIcs.TIME.2018.4","url":null,"abstract":"In the recent years research on dynamic social network has increased, which is also due to the availability of data sets from streaming media. Modeling a network's dynamic behaviour can be performed at the level of communities, which represent their mesoscale structure. Communities arise as a result of user to user interaction. In the current work we aim to predict the evolution of communities, i.e. to predict their future form. While this problem has been studied in the past as a supervised learning problem with a variety of classifiers, the problem is that the \"knowledge\" of a classifier is opaque and consequently incomprehensible to a human. Thus we have employed first order logic, and in particular the event calculus to represent the communities and their evolution. We addressed the problem of predicting the evolution as an online Inductive Logic Programming problem (ILP), where the issue is to learn first order logical clauses that associate evolutionary events, and particular Growth, Shrinkage, Continuation and Dissolution to lower level events. The lower level events are features that represent the structural and temporal characteristics of communities. Experiments have been performed on a real life data set form the Mathematics StackExchange forum, with the OLED framework for ILP. In doing so we have produced clauses that model both short term and long term correlations.","PeriodicalId":75226,"journal":{"name":"Time","volume":"110 1","pages":"4:1-4:20"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79250864","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}

{"title":"Deciding the Consistency of Branching Time Interval Networks","authors":"M. Gavanelli, A. Passantino, G. Sciavicco","doi":"10.4230/LIPIcs.TIME.2018.12","DOIUrl":"https://doi.org/10.4230/LIPIcs.TIME.2018.12","url":null,"abstract":"Allen’s Interval Algebra (IA) is one of the most prominent formalisms in the area of qualitative temporal reasoning; however, its applications are naturally restricted to linear flows of time. When dealing with nonlinear time, Allen’s algebra can be extended in several ways, and, as suggested by Ragni and Wölfl [20], a possible solution consists in defining the Branching Algebra (BA) as a set of 19 basic relations (13 basic linear relations plus 6 new basic nonlinear ones) in such a way that each basic relation between two intervals is completely defined by the relative position of the endpoints on a tree-like partial order. While the problem of deciding the consistency of a network of IA-constraints is well-studied, and every subset of the IA has been classified with respect to the tractability of its consistency problem, the fragments of the BA have received less attention. In this paper, we first define the notion of convex BA-relation, and, then, we prove that the consistency of a network of convex BA-relations can be decided via path consistency, and is therefore a polynomial problem. This is the first non-trivial tractable fragment of the BA; given the clear parallel with the linear case, our contribution poses the bases for a deeper study of fragments of BA towards their complete classification. 2012 ACM Subject Classification Theory of computation→ Constraint and logic programming","PeriodicalId":75226,"journal":{"name":"Time","volume":"104 1","pages":"12:1-12:15"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89002329","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}

{"title":"An Empirical Study on Bidirectional Recurrent Neural Networks for Human Motion Recognition","authors":"Pattreeya Tanisaro, G. Heidemann","doi":"10.4230/LIPIcs.TIME.2018.21","DOIUrl":"https://doi.org/10.4230/LIPIcs.TIME.2018.21","url":null,"abstract":"The deep recurrent neural networks (RNNs) and their associated gated neurons, such as Long Short–Term Memory (LSTM) have demonstrated a continued and growing success rates with researches in various sequential data processing applications, especially when applied to speech recognition and language modeling. Despite this, amongst current researches, there are limited studies on the deep RNNs architectures and their effects being applied to other application domains. In this paper, we evaluated the different strategies available to construct bidirectional recurrent neural networks (BRNNs) applying Gated Recurrent Units (GRUs), as well as investigating a reservoir computing RNNs, i.e., Echo state networks (ESN) and a few other conventional machine learning techniques for skeleton-based human motion recognition. The evaluation of tasks focuses on the generalization of different approaches by employing arbitrary untrained viewpoints, combined together with previously unseen subjects. Moreover, we extended the test by lowering the subsampling frame rates to examine the robustness of the algorithms being employed against the varying of movement speed. 2012 ACM Subject Classification Mathematics of computing → Time series analysis","PeriodicalId":75226,"journal":{"name":"Time","volume":"61 1","pages":"21:1-21:19"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86869449","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}

{"title":"GSM+T: A Timed Artifact-Centric Process Model","authors":"Julius Köpke, Johann Eder, Jianwen Su","doi":"10.4230/LIPIcs.TIME.2018.18","DOIUrl":"https://doi.org/10.4230/LIPIcs.TIME.2018.18","url":null,"abstract":"We introduce an extension to the declarative and artifact-centric Guard Stage Milestone (GSM) process modeling language to represent temporal aspects (duration, deadlines, lower- and upper-bound constraints), define the correctness of executions of GSM processes with respect to temporal constraints, check controllability of processes, compute execution plans respecting temporal constraints, and provide a translation method allowing to execute controllable GSM+T processes on standard GSM Engines.","PeriodicalId":75226,"journal":{"name":"Time","volume":"68 1","pages":"18:1-18:15"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81314407","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}

{"title":"Learning Qualitative Constraint Networks","authors":"Malek Mouhoub, H. Marri, Eisa A. Alanazi","doi":"10.4230/LIPIcs.TIME.2018.19","DOIUrl":"https://doi.org/10.4230/LIPIcs.TIME.2018.19","url":null,"abstract":"Temporal and spatial reasoning is a fundamental task in artificial intelligence and its related areas including scheduling, planning and Geographic Information Systems (GIS). In these applications, we often deal with incomplete and qualitative information. In this regard, the symbolic representation of time and space using Qualitative Constraint Networks (QCNs) is therefore substantial. We propose a new algorithm for learning a QCN from a non expert. The learning process includes different cases where querying the user is an essential task. Here, membership queries are asked in order to elicit temporal or spatial relationships between pairs of temporal or spatial entities. During this acquisition process, constraint propagation through Path Consistency (PC) is performed in order to reduce the number of membership queries needed to reach the target QCN. We use the learning theory machinery to prove some limits on learning path consistent QCNs from queries. The time performances of our algorithm have been experimentally evaluated using different scenarios.","PeriodicalId":75226,"journal":{"name":"Time","volume":"11 1","pages":"19:1-19:13"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80453428","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}

{"title":"Sound-and-Complete Algorithms for Checking the Dynamic Controllability of Conditional Simple Temporal Networks with Uncertainty","authors":"Luke Hunsberger, Roberto Posenato","doi":"10.4230/LIPIcs.TIME.2018.14","DOIUrl":"https://doi.org/10.4230/LIPIcs.TIME.2018.14","url":null,"abstract":"A Conditional Simple Temporal Network with Uncertainty (CSTNU) is a data structure for representing and reasoning about time. CSTNUs incorporate observation time-points from Conditional Simple Temporal Networks (CSTNs) and contingent links from Simple Temporal Networks with Uncertainty (STNUs). A CSTNU is dynamically controllable (DC) if there exists a strategy for executing its time-points that guarantees the satisfaction of all relevant constraints no matter how the uncertainty associated with its observation time-points and contingent links is resolved in real time. This paper presents the first sound-and-complete DC-checking algorithms for CSTNUs that are based on the propagation of labeled constraints and demonstrates their practicality. 2012 ACM Subject Classification G.2.2 Graph Theory, I.2.8 Problem Solving, Control Methods, and Search","PeriodicalId":75226,"journal":{"name":"Time","volume":"55 1","pages":"14:1-14:17"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83109547","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}

{"title":"A Stream Reasoning System for Maritime Monitoring","authors":"Georgios M. Santipantakis, Akrivi Vlachou, C. Doulkeridis, A. Artikis, Ioannis Kontopoulos, G. Vouros","doi":"10.4230/LIPIcs.TIME.2018.20","DOIUrl":"https://doi.org/10.4230/LIPIcs.TIME.2018.20","url":null,"abstract":"We present a stream reasoning system for monitoring vessel activity in large geographical areas. The system ingests a compressed vessel position stream, and performs online spatio-temporal link discovery to calculate proximity relations between vessels, and topological relations between vessel and static areas. Capitalizing on the discovered relations, a complex activity recognition engine, based on the Event Calculus, performs continuous pattern matching to detect various types of dangerous, suspicious and potentially illegal vessel activity. We evaluate the performance of the system by means of real datasets including kinematic messages from vessels, and demonstrate the effects of the highly efficient spatio-temporal link discovery on performance.","PeriodicalId":75226,"journal":{"name":"Time","volume":"65 1","pages":"20:1-20:17"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75098405","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}

{"title":"Faster Dynamic Controllability Checking for Simple Temporal Networks with Uncertainty","authors":"Massimo Cairo, Luke Hunsberger, Romeo Rizzi","doi":"10.4230/LIPIcs.TIME.2018.8","DOIUrl":"https://doi.org/10.4230/LIPIcs.TIME.2018.8","url":null,"abstract":"Simple Temporal Networks (STNs) are a well-studied model for representing and reasoning about time. An STN comprises a set of real-valued variables called time-points, together with a set of binary constraints, each of the form Y ≤ X + w. The problem of finding a feasible schedule (i.e., an assignment of real numbers to time-points such that all of the constraints are satisfied) is equivalent to the Single Source Shortest Path problem (SSSP) in the STN graph. Simple Temporal Networks with Uncertainty (STNUs) augment STNs to include contingent links that can be used, for example, to represent actions with uncertain durations. The duration of a contingent link is not controlled by the planner, but is instead controlled by a (possibly adversarial) environment. Each contingent link has the form, 〈A, `, u, C〉, where 0 < ` ≤ u <∞. Once the planner executes the activation time-point A, the environment must execute the contingent time-point C at some time A+ ∆, where ∆ ∈ [`, u]. Crucially, the planner does not know the value of ∆ in advance, but only discovers it when C executes. An STNU is dynamically controllable (DC) if there is a strategy that the planner can use to execute all of the non-contingent time-points, such that all of the constraints are guaranteed to be satisfied no matter which durations the environment chooses for the contingent links. The strategy can be dynamic in that it can react in real time to the contingent durations it observes. Recently, an upper bound of O(N3) was given for the DC-checking problem for STNUs, where N is the number of time-points. This paper introduces a new algorithm, called the RUL− algorithm, for solving the DCchecking problem for STNUs that improves on the O(N3) bound. The worst-case complexity of the RUL− algorithm is O(MN +K2N +KN logN), where N is the number of time-points, M is the number of constraints, and K is the number of contingent time-points. If M is O(N2), then the complexity reduces to O(N3); however, in sparse graphs the complexity can be much less. For example, if M is O(N logN), and K is O( √ N), then the complexity of the RUL− algorithm reduces to O(N2 logN). The RUL− algorithm begins by using the Bellman-Ford algorithm to compute a potential function. It then performs at most 2K rounds of computations, interleaving novel applications of Dijkstra’s algorithm to (1) generate new edges and (2) update the potential function in response to those new edges. The constraint-propagation/edge-generation rules used by the RUL− algorithm are distinguished from related work in two ways. First, they only generate unlabeled edges. Second, their applicability conditions are more restrictive. As a result, the RUL− algorithm requires only O(K) rounds of Dijkstra’s algorithm, instead of the O(N) rounds required by other approaches. The paper proves that the RUL− algorithm is sound and complete for the DC-checking problem for STNUs. 2012 ACM Subject Classification Networks → Network Algorithms 23:2 Faster DC ","PeriodicalId":75226,"journal":{"name":"Time","volume":"35 1","pages":"8:1-8:16"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78362108","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}

{"title":"Model Checking Strategic Ability - Why, What, and Especially: How? (Invited Paper)","authors":"W. Jamroga","doi":"10.4230/LIPIcs.TIME.2018.3","DOIUrl":"https://doi.org/10.4230/LIPIcs.TIME.2018.3","url":null,"abstract":"Automated verification of discrete-state systems has been a hot topic in computer science for over 35 years. Model checking of temporal and strategic properties is one of the most prominent and most successful approaches here. In this talk, I present a brief introduction to the topic, and mention some relevant properties that one might like to verify this way. Then, I describe some recent results on approximate model checking and model reductions, which can be applied to facilitate verification of notoriously hard cases. 2012 ACM Subject Classification Computing methodologies → Multi-agent systems","PeriodicalId":75226,"journal":{"name":"Time","volume":"7 1","pages":"3:1-3:10"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81021877","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}