Search Results for « Temporal Reasoning »

Many applications -- from planning and scheduling to problems in molecular biology -- rely heavily on a temporal reasoning component. In this paper, we discuss the design and empirical analysis of algorithms for a temporal reasoning system based on Allen's influential interval-based framework for representing temporal information. At the core of the system are algorithms for determining whether the temporal information is consistent, and, if so, finding one or more scenarios that are consistent with the temporal information. Two important algorithms for these tasks are a path consistency algorithm and a backtracking algorithm. For the path consistency algorithm, we develop techniques that can result in up to a ten-fold speedup over an already highly optimized implementation. For the backtracking algorithm, we develop variable and value ordering heuristics that are shown empirically to dramatically improve the performance of the algorithm. As well, we show that a previously suggested ref...

Many applications -- from planning and scheduling to problems in molecular biology -- rely heavily on a temporal reasoning component. In this paper, we discuss the design and empirical analysis of algorithms for a temporal reasoning system based on Allen's influential interval-based framework for representing temporal information. At the core of the system are algorithms for determining whether the temporal information is consistent, and, if so, finding one or more scenarios that are consistent with the temporal information. Two important algorithms for these tasks are a path consistency algorithm and a backtracking algorithm. For the path consistency algorithm, we develop techniques that can result in up to a ten-fold speedup over an already highly optimized implementation. For the backtracking algorithm, we develop variable and value ordering heuristics that are shown empirically to dramatically improve the performance of the algorithm. As well, we show that a previously suggested reformulation of the backtracking search problem can reduce the time and space requirements of the backtracking search. Taken together, the techniques we develop allow a temporal reasoning component to solve problems that are of practical size.

This paper describes the performance evaluation of six temporal reasoning systems. We show that if you are working with large temporal datasets where information is added incrementally throughout the execution of the program, systems using incompletely connected graphs ( i.e., TMM, TimeGraph and TimeGraph-II) seem the best option. While they do not offer the constant query time of systems using fully connected graphs ( i.e. the systems based on constraint satisfaction), the savings at assertion time are so substantial that the relatively small performance penalty for queries is a reasonable tradeoff. Of course, these systems do not offer the expressivity of the interval-based systems as they only handle point-based relations. Of the three, TimeGraph-II offers a wider range of qualitative relations as it handles point inequality. It does not currently handle metric information, however, as do TMM and TimeGraph. Thus decisions between these three may be more determined by the reasoning capabilities required rather than raw performance.

Multimodal question answering tasks can be used as proxy tasks to study systems that can perceive and reason about the world. Answering questions about different types of input modalities stresses different aspects of reasoning such as visual reasoning, reading comprehension, story understanding, or navigation. In this article, we use the task of Audio Question Answering (AQA) to study the temporal reasoning abilities of machine learning models. To this end, we introduce the Diagnostic Audio Question Answering (DAQA) dataset comprising audio sequences of natural sound events and programmatically generated questions and answers that probe various aspects of temporal reasoning. We adapt several recent state-of-the-art methods for visual question answering to the AQA task, and use DAQA to demonstrate that they perform poorly on questions that require in-depth temporal reasoning. Finally, we propose a new model, Multiple Auxiliary Controllers for Linear Modulation (MALiMo) that extends the recent Feature-wise Linear Modulation (FiLM) model and significantly improves its temporal reasoning capabilities. We envisage DAQA to foster research on AQA and temporal reasoning and MALiMo a step towards models for AQA.

Hoyerswerda, Germany's fastest-shrinking city, faces problems with the future that seem initially unrelated to the past and yet excite manifold conflicting accounts of it. The multiple and conflicting temporal references employed by Hoyerswerdians indicate that the temporal regime of postsocialism is accompanied, if not overcome, by the temporal framework of shrinkage. By reintroducing the analytical domain of the future, I show that local temporal knowledge practices are not historically predetermined by a homogenous postsocialist culture or by particular generational experiences. Rather, they exhibit what I call temporal complexity and temporal flexibility-creative uses of a variety of coexisting temporal references. My ethnographic material illustrates how such expressions of different forms of temporal reasoning structure social relations within and between different generations. Corresponding social groups are not simply divided by age, but are united through shared and heavily disputed negotiations of the post-Cold War era's contemporary crisis.

The availability of large scale event data with time stamps has given rise to dynamically evolving knowledge graphs that contain temporal information for each edge. Reasoning over time in such dynamic knowledge graphs is not yet well understood. To this end, we present Know-Evolve, a novel deep evolutionary knowledge network that learns non-linearly evolving entity representations over time. The occurrence of a fact (edge) is modeled as a multivariate point process whose intensity function is modulated by the score for that fact computed based on the learned entity embeddings. We demonstrate significantly improved performance over various relational learning approaches on two large scale real-world datasets. Further, our method effectively predicts occurrence or recurrence time of a fact which is novel compared to prior reasoning approaches in multi-relational setting.

We introduce five new natural language inference (NLI) datasets focused on temporal reasoning.We recast four existing datasets annotated for event duration-how long an event lasts-and event ordering-how events are temporally arranged-into more than one million NLI examples.We use these datasets to investigate how well neural models trained on a popular NLI corpus capture these forms of temporal reasoning.