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Journal articles on the topic 'Temporal constraints'

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Published: 4 June 2021
Last updated: 25 May 2024

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1

Nair, Manjusha, Jinesh Manchan Kannimoola, Bharat Jayaraman, Bipin Nair, and Shyam Diwakar. "Temporal constrained objects for modelling neuronal dynamics." PeerJ Computer Science 4 (July 23, 2018): e159. http://dx.doi.org/10.7717/peerj-cs.159.

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Background Several new programming languages and technologies have emerged in the past few decades in order to ease the task of modelling complex systems. Modelling the dynamics of complex systems requires various levels of abstractions and reductive measures in representing the underlying behaviour. This also often requires making a trade-off between how realistic a model should be in order to address the scientific questions of interest and the computational tractability of the model. Methods In this paper, we propose a novel programming paradigm, called temporal constrained objects, which facilitates a principled approach to modelling complex dynamical systems. Temporal constrained objects are an extension of constrained objects with a focus on the analysis and prediction of the dynamic behaviour of a system. The structural aspects of a neuronal system are represented using objects, as in object-oriented languages, while the dynamic behaviour of neurons and synapses are modelled using declarative temporal constraints. Computation in this paradigm is a process of constraint satisfaction within a time-based simulation. Results We identified the feasibility and practicality in automatically mapping different kinds of neuron and synapse models to the constraints of temporal constrained objects. Simple neuronal networks were modelled by composing circuit components, implicitly satisfying the internal constraints of each component and interface constraints of the composition. Simulations show that temporal constrained objects provide significant conciseness in the formulation of these models. The underlying computational engine employed here automatically finds the solutions to the problems stated, reducing the code for modelling and simulation control. All examples reported in this paper have been programmed and successfully tested using the prototype language called TCOB. The code along with the programming environment are available at http://github.com/compneuro/TCOB_Neuron. Discussion Temporal constrained objects provide powerful capabilities for modelling the structural and dynamic aspects of neural systems. Capabilities of the constraint programming paradigm, such as declarative specification, the ability to express partial information and non-directionality, and capabilities of the object-oriented paradigm especially aggregation and inheritance, make this paradigm the right candidate for complex systems and computational modelling studies. With the advent of multi-core parallel computer architectures and techniques or parallel constraint-solving, the paradigm of temporal constrained objects lends itself to highly efficient execution which is necessary for modelling and simulation of large brain circuits.
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2

Msaaf, Mohammed, and Fouad Belmajdoub. "Diagnosis of Discrete Event Systems under Temporal Constraints Using Neural Network." International Journal of Engineering Research in Africa 49 (June 2020): 198–205. http://dx.doi.org/10.4028/www.scientific.net/jera.49.198.

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The good functioning of a discrete event system is related to how much the temporal constraints are respected. This paper gives a new approach, based on a statistical model and neural network, that allows the verification of temporal constraints in DES. We will perform an online temporal constraint checking which can detect in real time any abnormal functioning related to the violation of a temporal constraint. In the first phase, the construction of temporal constraints from statistical model is shown and after that neural networks are involved in dealing with the online temporal constraint checking.
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Chu, Wesley W., and Patrick H. Ngai. "Embedding temporal constraint propagation in machine sequencing for job shop scheduling." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 7, no. 1 (February 1993): 37–52. http://dx.doi.org/10.1017/s0890060400000056.

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In this paper, we show how a temporal constraint propagation technique can be embedded in the machine sequencing approach for solving the job shop scheduling problem. The temporal constraint propagation algorithm propagates the precedence constraints and machine interference constraints to reduce the search space generated by the machine sequencing approach. Further, by making use of the temporal nature of the job shop scheduling, efficient algorithms to propagate precedence constraints and machine interference constraints are developed. Experimental results reveal that embedding constraint propagation in the machine sequencing approach significantly reduces the computation time more than by just using the machine sequencing approach alone. Further, the proposed temporal constraint propagation algorithms provide an order of magnitude improvement on the computation time over the conventional constraint propagation algorithm.
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Chen, Jianhao, Junyang Ren, Wentao Ding, and Yuzhong Qu. "PaTeCon: A Pattern-Based Temporal Constraint Mining Method for Conflict Detection on Knowledge Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (June 26, 2023): 4166–72. http://dx.doi.org/10.1609/aaai.v37i4.25533.

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Temporal facts, the facts for characterizing events that hold in specific time periods, are attracting rising attention in the knowledge graph (KG) research communities. In terms of quality management, the introduction of time restrictions brings new challenges to maintaining the temporal consistency of KGs and detecting potential temporal conflicts. Previous studies rely on manually enumerated temporal constraints to detect conflicts, which are labor-intensive and may have granularity issues. We start from the common pattern of temporal facts and constraints and propose a pattern-based temporal constraint mining method, PaTeCon. PaTeCon uses automatically determined graph patterns and their relevant statistical information over the given KG instead of human experts to generate time constraints. Specifically, PaTeCon dynamically attaches type restriction to candidate constraints according to their measuring scores. We evaluate PaTeCon on two large-scale datasets based on Wikidata and Freebase respectively, the experimental results show that pattern-based automatic constraint mining is powerful in generating valuable temporal constraints.
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Campos, M., J. M. Juárez, J. Palma, and R. Marín. "Using temporal constraints for temporal abstraction." Journal of Intelligent Information Systems 34, no. 1 (February 12, 2009): 57–92. http://dx.doi.org/10.1007/s10844-009-0079-6.

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6

Frank, Jeremy. "Planning Solar Array Operations on the International Space Station." Proceedings of the International Conference on Automated Planning and Scheduling 23 (June 2, 2013): 470–71. http://dx.doi.org/10.1609/icaps.v23i1.13574.

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Flight controllers manage the orientation and modes of eight large solar arrays that power the International Space Station (ISS). The task requires generating plans that balance complex constraints and preferences. These considerations include context-dependent constraints on viable solar array configurations, temporal limits on transitions between configurations, and preferences on which considerations have priority. The Solar Array Constraint Engine (SACE) treats this operations planning problem as a sequence of tractable constrained optimization problems. SACE uses constraint management and automated planning capabilities to reason about the constraints, to find optimal array configurations subject to these constraints and solution preferences, and to automatically generate solar array operations plans.
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7

Barber, F. "Reasoning on Interval and Point-based Disjunctive Metric Constraints in Temporal Contexts." Journal of Artificial Intelligence Research 12 (February 1, 2000): 35–86. http://dx.doi.org/10.1613/jair.693.

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We introduce a temporal model for reasoning on disjunctive metric constraints on intervals and time points in temporal contexts. This temporal model is composed of a labeled temporal algebra and its reasoning algorithms. The labeled temporal algebra defines labeled disjunctive metric point-based constraints, where each disjunct in each input disjunctive constraint is univocally associated to a label. Reasoning algorithms manage labeled constraints, associated label lists, and sets of mutually inconsistent disjuncts. These algorithms guarantee consistency and obtain a minimal network. Additionally, constraints can be organized in a hierarchy of alternative temporal contexts. Therefore, we can reason on context-dependent disjunctive metric constraints on intervals and points. Moreover, the model is able to represent non-binary constraints, such that logical dependencies on disjuncts in constraints can be handled. The computational cost of reasoning algorithms is exponential in accordance with the underlying problem complexity, although some improvements are proposed.
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MOUHOUB, MALEK. "A HOPFIELD-TYPE NEURAL NETWORK BASED MODEL FOR TEMPORAL CONSTRAINTS." International Journal on Artificial Intelligence Tools 13, no. 03 (September 2004): 533–45. http://dx.doi.org/10.1142/s0218213004001673.

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In this paper we present an approximation method based on discrete Hopfield neural network (DHNN) for solving temporal constraint satisfaction problems. This method is of interest for problems involving numeric and symbolic temporal constraints and where a solution satisfying the constraints of the problem needs to be found within a given deadline. More precisely the method has the ability to provide a solution with a quality proportional to the allocated process time. The quality of the solution corresponds here to the number of satisfied constraints. This property is very important for real world applications including reactive scheduling and planning and also for over constrained problems where a complete solution cannot be found. Experimental study, in terms of time cost and quality of the solution provided, of the DHNN based method we propose provides promising results comparing to the other exact methods based on branch and bound and approximation methods based on stochastic local search.
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9

Duisberg, Robert A. "Animation Using Temporal Constraints." ACM SIGCHI Bulletin 20, no. 1 (July 1988): 81. http://dx.doi.org/10.1145/49103.1046503.

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Zuenko, Aleksandr A., and Olga V. Fridman. "Reasoning with temporal constraints." Transactions of the Kоla Science Centre of RAS. Series: Engineering Sciences 14, no. 7/2023 (February 27, 2024): 43–51. http://dx.doi.org/10.37614/2949-1215.2023.14.7.005.

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The work deals with the organization of temporal reasoning basing on constraint satisfaction methods. The definition of the constraint satisfaction problem and the notion of constraint consistency are given. The possibilities of epresentation of a planning problem as an interval constraint network are considered. As a mathematical apparatus for the formalization of temporal reasining, the Allen’s interval algebra is described, which main operations are composition and the intersection of temporal relations. A path consistency algorithm is given that implements one of the types of local consistency on interval constraint network and uses computations based on operations of interval algebra. An example of the application of this algorithm is presented. In the conclusion the prospects for the development of methods of temporal reasoning are considered.
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Micheli, Andrea, and Enrico Scala. "Temporal Planning with Temporal Metric Trajectory Constraints." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7675–82. http://dx.doi.org/10.1609/aaai.v33i01.33017675.

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In several industrial applications of planning, complex temporal metric trajectory constraints are needed to adequately model the problem at hand. For example, in production plants, items must be processed following a “recipe” of steps subject to precise timing constraints. Modeling such domains is very challenging in existing action-based languages due to the lack of sufficiently expressive trajectory constraints.We propose a novel temporal planning formalism allowing quantified temporal constraints over execution timing of action instances. We build on top of instantaneous actions borrowed from classical planning and add expressive temporal constructs. The paper details the semantics of our new formalism and presents a solving technique grounded in classical, heuristic forward search planning. Our experiments prove the proposed framework superior to alternative state-of-theart planning approaches on industrial benchmarks, and competitive with similar solving methods on well known benchmarks took from the planning competition.
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Chen, Y., B. W. Wah, and C. Hsu. "Temporal Planning using Subgoal Partitioning and Resolution in SGPlan." Journal of Artificial Intelligence Research 26 (August 6, 2006): 323–69. http://dx.doi.org/10.1613/jair.1918.

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In this paper, we present the partitioning of mutual-exclusion (mutex) constraints in temporal planning problems and its implementation in the SGPlan4 planner. Based on the strong locality of mutex constraints observed in many benchmarks of the Fourth International Planning Competition (IPC4), we propose to partition the constraints of a planning problem into groups based on their subgoals. Constraint partitioning leads to significantly easier subproblems that are similar to the original problem and that can be efficiently solved by the same planner with some modifications to its objective function. We present a partition-and-resolve strategy that looks for locally optimal subplans in constraint-partitioned temporal planning subproblems and that resolves those inconsistent global constraints across the subproblems. We also discuss some implementation details of SGPlan4, which include the resolution of violated global constraints, techniques for handling producible resources, landmark analysis, path finding and optimization, search-space reduction, and modifications of Metric-FF when used as a basic planner in SGPlan4. Last, we show results on the sensitivity of each of these techniques in quality-time trade-offs and experimentally demonstrate that SGPlan4 is effective for solving the IPC3 and IPC4 benchmarks.
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Xu, Rui, Zhaoyu Li, Pingyuan Cui, Shengying Zhu, and Ai Gao. "A Geometric Dynamic Temporal Reasoning Method with Tags for Cognitive Systems." International Journal of Software Science and Computational Intelligence 8, no. 4 (October 2016): 43–59. http://dx.doi.org/10.4018/ijssci.2016100103.

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Temporal reasoning is one of the cognitive capabilities humans involve in communicating with others and everything appears related because of temporal reference. Therefore, in this paper a geometric dynamic temporal reasoning algorithm is proposed to solve the temporal reasoning problem, especially in autonomous planning and scheduling. This method is based on the representation of actions in a two dimensional coordination system. The main advantage of this method over others is that it uses tags to mark new constraints added into the constraint network, which leads the algorithm to deal with pending constraints rather than all constraints. This characteristic makes the algorithm suitable for temporal reasoning, where variables and constraints are always added dynamically. This algorithm can be used not only in intelligent planning, but also computational intelligence, real-time systems, and etc. The results show the efficiency of our algorithm from four cases simulating the planning and scheduling process.
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Clements, Stephen E., Patrick L. Dallain, and Mark S. Jamnick. "An operational, spatially constrained harvest scheduling model." Canadian Journal of Forest Research 20, no. 9 (September 1, 1990): 1438–47. http://dx.doi.org/10.1139/x90-190.

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A Monte Carlo integer programming algorithm was developed to generate short-term (25-year), spatially feasible timber harvest plans for a New Brunswick Crown license. Solutions for the short-term plan are considered feasible if they meet spatial and temporal harvest-flow and adjacency constraints. The solution search procedure integrates a randomly generated harvesting sequence and checks of harvest-flow and adjacency constraints. The model was used to determine the annual allowable cut under three constraint formulations. The three formulations represented increasing levels of adjacency constraints, from no constraints to levels similar to current provincial requirements. The annual allowable cut under the most strict constraint formulation was reduced by 9% from the unconstrained formulation, for a given mapping strategy of a long-term harvest schedule. These applications of the model indicate that it is suitable for spatially constrained harvest scheduling on Crown licenses in New Brunswick.
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Belaid, Mohamed-Bachir, Nassim Belmecheri, Arnaud Gotlieb, Nadjib Lazaar, and Helge Spieker. "GEQCA: Generic Qualitative Constraint Acquisition." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (June 28, 2022): 3690–97. http://dx.doi.org/10.1609/aaai.v36i4.20282.

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Many planning, scheduling or multi-dimensional packing problems involve the design of subtle logical combinations of temporal or spatial constraints. On the one hand, the precise modelling of these constraints, which are formulated in various relation algebras, entails a number of possible logical combinations and requires expertise in constraint-based modelling. On the other hand, active constraint acquisition (CA) has been used successfully to support non-experienced users in learning conjunctive constraint networks through the generation of a sequence of queries. In this paper, we propose GEACQ, which stands for Generic Qualitative Constraint Acquisition, an active CA method that learns qualitative constraints via the concept of qualitative queries. GEACQ combines qualitative queries with time-bounded path consistency (PC) and background knowledge propagation to acquire the qualitative constraints of any scheduling or packing problem. We prove soundness, completeness and termination of GEACQ by exploiting the jointly exhaustive and pairwise disjoint property of qualitative calculus and we give an experimental evaluation that shows (i) the efficiency of our approach in learning temporal constraints and, (ii) the use of GEACQ on real scheduling instances.
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Song, Shaoxu, Ruihong Huang, Yue Cao, and Jianmin Wang. "Cleaning timestamps with temporal constraints." VLDB Journal 30, no. 3 (February 23, 2021): 425–46. http://dx.doi.org/10.1007/s00778-020-00641-6.

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Song, Shaoxu, Yue Cao, and Jianmin Wang. "Cleaning timestamps with temporal constraints." Proceedings of the VLDB Endowment 9, no. 10 (June 2016): 708–19. http://dx.doi.org/10.14778/2977797.2977798.

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18

Milicia, Giuseppe, and Vladimiro Sassone. "Temporal Constraints for Concurrent Objects." Electronic Notes in Theoretical Computer Science 82, no. 8 (October 2003): 30–32. http://dx.doi.org/10.1016/s1571-0661(04)80800-4.

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19

Kvet, Michal, and Karol Matiaško. "Temporal transaction integrity constraints management." Cluster Computing 20, no. 1 (January 21, 2017): 673–88. http://dx.doi.org/10.1007/s10586-017-0740-8.

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20

Dixon, Clare, Boris Konev, Michael Fisher, and Sherly Nietiadi. "Deductive temporal reasoning with constraints." Journal of Applied Logic 11, no. 1 (March 2013): 30–51. http://dx.doi.org/10.1016/j.jal.2012.07.001.

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21

Mouhoub, Malek, and Amrudee Sukpan. "Managing Temporal Constraints with Preferences." Spatial Cognition & Computation 8, no. 1-2 (May 22, 2008): 131–49. http://dx.doi.org/10.1080/13875860801930407.

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22

Theodorou, Brandon, Shrusti Jain, Cao Xiao, and Jimeng Sun. "ConSequence: Synthesizing Logically Constrained Sequences for Electronic Health Record Generation." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 14 (March 24, 2024): 15355–63. http://dx.doi.org/10.1609/aaai.v38i14.29460.

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Generative models can produce synthetic patient records for analytical tasks when real data is unavailable or limited. However, current methods struggle with adhering to domain-specific knowledge and removing invalid data. We present ConSequence, an effective approach to integrating domain knowledge into sequential generative neural network outputs. Our rule-based formulation includes temporal aggregation and antecedent evaluation modules, ensured by an efficient matrix multiplication formulation, to satisfy hard and soft logical constraints across time steps. Existing constraint methods often fail to guarantee constraint satisfaction, lack the ability to handle temporal constraints, and hinder the learning and computational efficiency of the model. In contrast, our approach efficiently handles all types of constraints with guaranteed logical coherence. We demonstrate ConSequence's effectiveness in generating electronic health records, outperforming competitors in achieving complete temporal and spatial constraint satisfaction without compromising runtime performance or generative quality. Specifically, ConSequence successfully prevents all rule violations while improving the model quality in reducing its test perplexity by 5% and incurring less than a 13% slowdown in generation speed compared to an unconstrained model.
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Chen, Xiaojun, Shengbin Jia, Ling Ding, and Yang Xiang. "Reasoning over temporal knowledge graph with temporal consistency constraints." Journal of Intelligent & Fuzzy Systems 40, no. 6 (June 21, 2021): 11941–50. http://dx.doi.org/10.3233/jifs-210064.

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Knowledge graph reasoning or completion aims at inferring missing facts by reasoning about the information already present in the knowledge graph. In this work, we explore the problem of temporal knowledge graph reasoning that performs inference on the graph over time. Most existing reasoning models ignore the time information when learning entities and relations representations. For example, the fact (Scarlett Johansson, spouse Of, Ryan Reynolds) was true only during 2008 - 2011. To facilitate temporal reasoning, we present TA-TransRILP, which involves temporal information by utilizing RNNs and takes advantage of Integer Linear Programming. Specifically, we utilize a character-level long short-term memory network to encode relations with sequences of temporal tokens, and combine it with common reasoning model. To achieve more accurate reasoning, we further deploy temporal consistency constraints to basic model, which can help in assessing the validity of a fact better. We conduct entity prediction and relation prediction on YAGO11k and Wikidata12k datasets. Experimental results demonstrate that TA-TransRILP can make more accurate predictions by taking time information and temporal consistency constraints into account, and outperforms existing methods with a significant improvement about 6-8% on Hits@10.
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Planken, Léon, Mathijs De Weerdt, and Neil Yorke-Smith. "Incrementally Solving STNs by Enforcing Partial Path Consistency." Proceedings of the International Conference on Automated Planning and Scheduling 20 (May 25, 2021): 129–36. http://dx.doi.org/10.1609/icaps.v20i1.13417.

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Efficient management and propagation of temporal constraints is important for temporal planning as well as for scheduling. During plan development, new events and temporal constraints are added and existing constraints may be tightened; the consistency of the whole temporal network is frequently checked; and results of constraint propagation guide further search. Recent work shows that enforcing partial path consistency provides an efficient means of propagating temporal information for the popular Simple Temporal Network (STN). We show that partial path consistency can be enforced incrementally, thus exploiting the similarities of the constraint network between subsequent edge tightenings. We prove that the worst-case time complexity of our algorithm can be bounded both by the number of edges in the chordal graph (which is better than the previous bound of the number of vertices squared), and by the degree of the chordal graph times the number of vertices incident on updated edges. We show that for many sparse graphs, the latter bound is better than that of the previously best-known approaches. In addition, our algorithm requires space only linear in the number of edges of the chordal graph, whereas earlier work uses space quadratic in the number of vertices. Finally, empirical results show that when incrementally solving sparse STNs, stemming from problems such as Hierarchical Task Network planning, our approach outperforms extant algorithms.
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WAH, BENJAMIN W., and YIXIN CHEN. "SUBGOAL PARTITIONING AND GLOBAL SEARCH FOR SOLVING TEMPORAL PLANNING PROBLEMS IN MIXED SPACE." International Journal on Artificial Intelligence Tools 13, no. 04 (December 2004): 767–90. http://dx.doi.org/10.1142/s0218213004001806.

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We study in this paper the partitioning of the constraints of a temporal planning problem by subgoals, their sequential evaluation before parallelizing the actions, and the resolution of inconsistent global constraints across subgoals. Using an ℓ1-penalty formulation and the theory of extended saddle points, we propose a global-search strategy that looks for local minima in the original-variable space of the ℓ1-penalty function and for local maxima in the penalty space. Our approach improves over a previous scheme that partitions constraints along the temporal horizon. The previous scheme leads to many global constraints that relate states in adjacent stages, which means that an incorrect assignment of states in an earlier stage of the horizon may violate a global constraint in a later stage of the horizon. To resolve the violated global constraint in this case, state changes will need to propagate sequentially through multiple stages, often leading to a search that gets stuck in an infeasible point for an extended period of time. In this paper, we propose to partition all the constraints by subgoals and to add new global constraints in order to ensure that state assignments of a subgoal are consistent with those in other subgoals. Such an approach allows the information on incorrect state assignments in one subgoal to propagate quickly to other subgoals. Using MIPS as the basic planner in a partitioned implementation, we demonstrate significant improvements in time and quality in solving some PDDL2.1 benchmark problems.
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Rossi, Francesca, Kristen Brent Venable, and Toby Walsh. "Preferences in Constraint Satisfaction and Optimization." AI Magazine 29, no. 4 (December 28, 2008): 58. http://dx.doi.org/10.1609/aimag.v29i4.2202.

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We review constraint-based approaches to handle preferences. We start by defining the main notions of constraint programming, then give various concepts of soft constraints and show how they can be used to model quantitative preferences. We then consider how soft constraints can be adapted to handle other forms of preferences, such as bipolar, qualitative, and temporal preferences. Finally, we describe how AI techniques such as abstraction, explanation generation, machine learning, and preference elicitation, can be useful in modelling and solving soft constraints.
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Zhang, Han, Neelesh Tiruviluamala, Sven Koenig, and T. K. Satish Kumar. "Temporal Reasoning with Kinodynamic Networks." Proceedings of the International Conference on Automated Planning and Scheduling 31 (May 17, 2021): 415–25. http://dx.doi.org/10.1609/icaps.v31i1.15987.

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Temporal reasoning is central to Artificial Intelligence (AI) and many of its applications. However, the existing algorithmic frameworks for temporal reasoning are not expressive enough to be applicable to robots with complex kinodynamic constraints typically described using differential equations. For example, while minimum and maximum velocity constraints can be encoded in Simple Temporal Networks (STNs), higher-order kinodynamic constraints cannot be represented in existing frameworks. In this paper, we present a novel framework for temporal reasoning called Kinodynamic Networks (KDNs). KDNs combine elements of existing temporal reasoning frameworks with the idea of Bernstein polynomials. The velocity profiles of robots are represented using Bernstein polynomials; and dynamic constraints on these velocity profiles can be converted to linear constraints on the to-be-determined coefficients of their Bernstein polynomials. We study KDNs for their attractive theoretical properties and apply them to the Multi-Agent Path Finding (MAPF) problem with higher-order kinodynamic constraints. We show that our approach is not only scalable but also yields smooth velocity profiles for all robots that can be executed by their controllers.
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Percus, Orin. "Pragmatic constraints on (adverbial) (temporal) quantification." ZAS Papers in Linguistics 22 (January 1, 2001): 113–38. http://dx.doi.org/10.21248/zaspil.22.2001.104.

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Even if we can generate a logical form, principles of use may limit the ways in which we can use it. In this paper, I motivate one such principle of use, and explore its effects. Much of the discussion involves kinds of sentences that have received attention in the literature on "individual-level predicates".
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Munhall, K. G., P. Gribble, L. Sacco, and M. Ward. "Temporal constraints on the McGurk effect." Perception & Psychophysics 58, no. 3 (January 1996): 351–62. http://dx.doi.org/10.3758/bf03206811.

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Lodaya, Kamal, and Paritosh K. Pandya. "Deterministic Temporal Logics and Interval Constraints." Electronic Proceedings in Theoretical Computer Science 243 (March 6, 2017): 23–40. http://dx.doi.org/10.4204/eptcs.243.2.

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Kung, C. H. "On verification of database temporal constraints." ACM SIGMOD Record 14, no. 4 (May 1985): 169–79. http://dx.doi.org/10.1145/971699.318911.

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Brunel, Julien, David Chemouil, Alcino Cunha, and Nuno Macedo. "Simulation under Arbitrary Temporal Logic Constraints." Electronic Proceedings in Theoretical Computer Science 310 (December 23, 2019): 63–69. http://dx.doi.org/10.4204/eptcs.310.7.

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Baggio, Giosuè. "Processing Temporal Constraints: An ERP Study." Language Learning 58 (December 2008): 35–55. http://dx.doi.org/10.1111/j.1467-9922.2008.00460.x.

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Winkler, István, István Czigler, Maria Jaramillo, Petri Paavilainen, and Risto Näätänen. "Temporal constraints of auditory event synthesis." NeuroReport 9, no. 3 (February 1998): 495–99. http://dx.doi.org/10.1097/00001756-199802160-00025.

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Han, Ching-Chih, Kwei-Jay Lin, and Jane W. S. Liu. "Scheduling Jobs with Temporal Distance Constraints." SIAM Journal on Computing 24, no. 5 (October 1995): 1104–21. http://dx.doi.org/10.1137/s0097539791218081.

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Nicholls, Geoff, and Martin Jones. "Radiocarbon dating with temporal order constraints." Journal of the Royal Statistical Society: Series C (Applied Statistics) 50, no. 4 (January 2001): 503–21. http://dx.doi.org/10.1111/1467-9876.00250.

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Currim, Faiz A., Sabah A. Currim, Curtis E. Dyreson, Richard T. Snodgrass, Stephen W. Thomas, and Rui Zhang. "Adding Temporal Constraints to XML Schema." IEEE Transactions on Knowledge and Data Engineering 24, no. 8 (August 2012): 1361–77. http://dx.doi.org/10.1109/tkde.2011.74.

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Cacciari, Leo, and Omar Rafiq. "Validation of protocols with temporal constraints." Computer Communications 19, no. 14 (December 1996): 1188–99. http://dx.doi.org/10.1016/s0140-3664(96)01153-x.

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Chen, Jinjun, and Yun Yang. "Localising temporal constraints in scientific workflows." Journal of Computer and System Sciences 76, no. 6 (September 2010): 464–74. http://dx.doi.org/10.1016/j.jcss.2009.11.007.

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Lundgaard, Stine S., Jesper Kjeldskov, and Mikael B. Skov. "Temporal Constraints in Human--Building Interaction." ACM Transactions on Computer-Human Interaction 26, no. 2 (April 28, 2019): 1–29. http://dx.doi.org/10.1145/3301424.

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41

Duisberg, R. A. "Animated graphical interfaces using temporal constraints." ACM SIGCHI Bulletin 17, no. 4 (April 1986): 131–36. http://dx.doi.org/10.1145/22339.22361.

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42

Li, Zhaoyu, Rui Xu, Pingyuan Cui, Lida Xu, and Wu He. "Geometry-based propagation of temporal constraints." Information Systems Frontiers 19, no. 4 (February 22, 2016): 855–68. http://dx.doi.org/10.1007/s10796-016-9635-0.

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43

WU, Li-Hua, Ai-Xiang CHEN, Yun-Fei JIANG, and Rui BIAN. "A CSP-Based Approach for Temporal Constraints in Temporal Planning." Chinese Journal of Computers 35, no. 8 (2012): 1759. http://dx.doi.org/10.3724/sp.j.1016.2012.01759.

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44

Santos, Eugene, Deqing Li, Eunice E. Santos, and John Korah. "Temporal Bayesian Knowledge Bases – Reasoning about uncertainty with temporal constraints." Expert Systems with Applications 39, no. 17 (December 2012): 12905–17. http://dx.doi.org/10.1016/j.eswa.2012.05.002.

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45

Belaid, Mohamed-Bachir, Nassim Belmecheri, Arnaud Gotlieb, Nadjib Lazaar, and Helge Spieker. "Query-driven Qualitative Constraint Acquisition." Journal of Artificial Intelligence Research 79 (January 26, 2024): 241–71. http://dx.doi.org/10.1613/jair.1.14752.

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Many planning, scheduling or multi-dimensional packing problems involve the design of subtle logical combinations of temporal or spatial constraints. Recently, we introduced GEQCA-I, which stands for Generic Qualitative Constraint Acquisition, as a new active constraint acquisition method for learning qualitative constraints using qualitative queries. In this paper, we revise and extend GEQCA-I to GEQCA-II with a new type of query, universal query, for qualitative constraint acquisition, with a deeper query-driven acquisition algorithm. Our extended experimental evaluation shows the efficiency and usefulness of the concept of universal query in learning randomly-generated qualitative networks, including both temporal networks based on Allen’s algebra and spatial networks based on region connection calculus. We also show the effectiveness of GEQCA-II in learning the qualitative part of real scheduling problems.
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46

Lauffer, Niklas T., William B. Lassiter, and Jeremy D. Frank. "On Expected Value Strong Controllability." Journal of Artificial Intelligence Research 78 (November 29, 2023): 849–900. http://dx.doi.org/10.1613/jair.1.14561.

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The Probabilistic Simple Temporal Network with Uncertainty (PSTNU) is a variant of the Simple Temporal Network with Uncertainty (STNU) in which known probability distributions govern the timing of uncontrollable timepoints. Previous approaches to solving PSTNUs focus mininizing risk, that is, the probability of violating constraints. These approaches are not applicable in over-constrained controllability problems, when it is certain that all constraints can’t be satisfied. We introduce the Weighted Probabilistic Simple Temporal Network with Uncertainty (WPSTNU), which extends the PSTNU by attaching a fixed value to the satisfaction of temporal constraints, and allows the schedule to violate some constraints in order to maximize the expected value of satisfying others. We study the problem of Expected Value Strong Controllability (EvSC) of WPSTNUs, which seeks a fixed-time schedule maximizing the expected value of satisfied constraints. We solve the EvSC problem using a mixed integer linear program (MILP) that bounds below the probability of satisfying constraints involving uncontrollable timepoints. While solving MILPs generally takes exponential time, we demonstrate our formulation’s effective performance using scheduling problems derived from the HEATlab and MIT ROVERS data sets. We then show how to use this MILP to reschedule during execution, after time has passed and uncertainty is reduced. We describe different fixed-period rescheduling approaches, including time-based and event-based, and report on the most successful strategies compared to the expected value of the fixed schedule produced by the MILP. All of our methods are evaluated on problems with both symmetric and asymmetric (skewed) probability distributions. We show that periodically rescheduling improves the expected value when compared to the fixed schedule, and describe how the benchmark and skewness impact the schedule value improvement. The resulting analysis shows that solving EvSC problems on WPSTNUs is a viable alternative to solving over-constrained controllability problems.
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47

Rodrigues Quemel e Assis Santana, Pedro, and Brian Williams. "Chance-Constrained Consistency for Probabilistic Temporal Plan Networks." Proceedings of the International Conference on Automated Planning and Scheduling 24 (May 11, 2014): 271–79. http://dx.doi.org/10.1609/icaps.v24i1.13651.

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Unmanned deep-sea and planetary vehicles operate in highly uncertain environments. Autonomous agents often are not adopted in these domains due to the risk of mission failure, and loss of vehicles. Prior work on contingent plan execution addresses this issue by placing bounds on uncertain variables and by providing consistency guarantees for a `worst-case' analysis, which tends to be too conservative for real-world applications. In this work, we unify features from trajectory optimization through risk-sensitive execution methods and high-level, contingent plan execution in order to extend existing guarantees of consistency for conditional plans to a chance-constrained setting. The result is a set of efficient algorithms for computing plan execution policies with explicit bounds on the risk of failure. To accomplish this, we introduce Probabilistic Temporal Plan Network (pTPN), which improve previous formulations, by incorporating probabilistic uncertainty and chance-constraints into the plan representation. We then introduce a novel method to the chance-constrained strong consistency problem, by leveraging a conflict-directed approach that searches for an execution policy that maximizes reward while meeting the risk constraint. Experimental results indicate that our approach for computing strongly consistent policies has an average scalability gain of about one order of magnitude, when compared to current methods based on chronological search.
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Mattenet, Alex, Ian Davidson, Siegfried Nijssen, and Pierre Schaus. "Generic Constraint-based Block Modeling using Constraint Programming." Journal of Artificial Intelligence Research 70 (February 9, 2021): 597–630. http://dx.doi.org/10.1613/jair.1.12280.

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Block modeling has been used extensively in many domains including social science, spatial temporal data analysis and even medical imaging. Original formulations of the problem modeled it as a mixed integer programming problem, but were not scalable. Subsequent work relaxed the discrete optimization requirement, and showed that adding constraints is not straightforward in existing approaches. In this work, we present a new approach based on constraint programming, allowing discrete optimization of block modeling in a manner that is not only scalable, but also allows the easy incorporation of constraints. We introduce a new constraint filtering algorithm that outperforms earlier approaches, in both constrained and unconstrained settings, for an exhaustive search and for a type of local search called Large Neighborhood Search. We show its use in the analysis of real datasets. Finally, we show an application of the CP framework for model selection using the Minimum Description Length principle.
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Mueller, Carl L. "Abstract Constraints for Safe and Robust Robot Learning from Demonstration." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 10 (April 3, 2020): 13728–29. http://dx.doi.org/10.1609/aaai.v34i10.7136.

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My thesis research incorporates high-level abstract behavioral requirements, called ‘conceptual constraints’, into the modeling processes of robot Learning from Demonstration (LfD) techniques. My most recent work introduces an LfD algorithm called Concept Constrained Learning from Demonstration. This algorithm encodes motion planning constraints as temporal Boolean operators that enforce high-level constraints over portions of the robot's motion plan during learned skill execution. This results in more easily trained, more robust, and safer learned skills. Future work will incorporate conceptual constraints into human-aware motion planning algorithms. Additionally, my research will investigate how these concept constrained algorithms and models are best incorporated into effective interfaces for end-users.
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SADAOUI, SAMIRA, MALEK MOUHOUB, and BO CHEN. "AN EFFICIENT LOTOS-BASED FRAMEWORK FOR DESCRIBING AND SOLVING (TEMPORAL) CSPs." International Journal of Software Engineering and Knowledge Engineering 19, no. 06 (September 2009): 765–89. http://dx.doi.org/10.1142/s0218194009004416.

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Simulation of complex Lotos specifications is not always efficient due to the space explosion problem of their corresponding transition systems. To overcome this difficulty in practice, we present in this paper a novel approach which integrates constraint propagation techniques into the Lotos specifications. These solving techniques are used to reduce the size of the search space before and during the search for a solution to a given combinatorial problem under constraints. In order to do that, we first tackle the challenging task of describing combinatorial problems in Lotos using the Constraint Satisfaction Problem (CSP) framework. In this regard, we provide two generic Lotos templates for describing CSPs and temporal CSPs (CSPs involving temporal constraints). To evaluate the time performance of the framework we propose, we have conducted several experimental tests on instances of the N-Queens, the machine scheduling and randomly generated CSPs. The results of these experiments are promising and demonstrate the efficiency of Lotos simulation when CSP techniques are integrated.
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