A framework for specifying and monitoring user tasks

Interrupting users engaged in tasks typically has negative effects on their task completion time, error rate, and affective state. Empirical research has shown that these negative effects can be mitigated by deferring interruptions until more opportune moments in a user’s task sequence. However, existing systems that reason about when to interrupt do not have access to models of user tasks that would allow for such finer-grained temporal reasoning. To enable this reasoning, we have developed an integrated framework for specifying and monitoring user tasks. For task specification, our framework provides a language that supports expressive specification of tasks using a concise notation. For task monitoring, our framework provides an event database and handler that manages events from any instrumented application and a task monitor that observes a user’s progress through specified tasks. We describe the design and implementation of our framework, showing how it can be used to specify and monitor practical, representative user tasks. We also report results from two user studies measuring the effectiveness of our existing implementation. The use of our framework will enable attention aware systems to consider a user’s position in a task when reasoning about when to interrupt.     

Spatial reasoning in a fuzzy region connection calculus

Although the region connection calculus (RCC) offers an appealing framework for modelling topological relations, its application in real-world scenarios is hampered when spatial phenomena are affected by vagueness. To cope with this, we present a generalization of the RCC based on fuzzy set theory, and discuss how reasoning tasks such as satisfiability and entailment checking can be cast into linear programming problems. We furthermore reveal that reasoning in our fuzzy RCC is NP-complete, thus preserving the computational complexity of reasoning in the RCC, and we identify an important tractable subfragment. Moreover, we show how reasoning tasks in our fuzzy RCC can also be reduced to reasoning tasks in the original RCC. While this link with the RCC could be exploited in practical reasoning algorithms, we mainly focus on the theoretical consequences. In particular, using this link we establish a close relationship with the Egg-Yolk calculus, and we demonstrate that satisfiable knowledge bases can be realized by fuzzy regions in any dimension.     

On point-duration networks for temporal reasoning

We present here a point-duration network formalism which extends the point algebra model to include additional variables that represent durations between points of time. Thereafter the new qualitative model is enlarged for allowing unary metric constraints on points and durations, subsuming in this way several point-based approaches to temporal reasoning. We deal with some reasoning tasks within the new models and we show that the main problem, deciding consistency, is NP-complete. However, tractable special cases are identified and we show efficient algorithms for checking consistency, finding a solution and obtaining the minimal network.     

Building text-based temporally linked event network for scientific big data analytics

Events formulate the world of the human being and could be regarded as the semantic units in different granularities for information organization. Extracting events and temporal information from texts plays an important role for information analytics in big data because of the wide use of multilingual texts. This paper surveys existing research work on text-based event temporal resolution and reasoning including identification of events, temporal information resolutions of events in English and Chinese texts, the rule-based temporal relation reasoning between events and relevant temporal representations. For the scientific big data analytics, we point out the shortcomings of existing research work and give the argument about the future research work for advancing identification of events, establishment of temporal relations and reasoning of temporal relations.     

Representation and Reasoning with Multi-Point Events

Allen's Interval Algebra (IA) and Vilain & Kautz's Point Algebra (PA) consider an interval and a point as basic temporal entities (i.e., events) respectively. However, in many situations we need to deal with recurring events that include multiple points, multiple intervals or combinations of points and intervals. In this paper, we present a framework to model recurring events as multi-point events (MPEs) by extending point algebra. The reasoning tasks are formulated as binary constraint satisfaction problems. We propose a polynomial time algorithm (based on van Beek's algorithm) for finding all feasible relations. For the problem of finding a consistent scenario, we propose a backtracking method with a local search heuristic. We also describe an implementation and a detail empirical evaluation of the proposed algorithms. Our empirical results indicate that the MPE-based approach performs better than the existing approaches.     

Toward a comprehensive treatment of temporal constraints about periodic events

In this article, we propose an Allen‐like approach to deal with different types of temporal constraints about periodic events. We consider the different components of such constraints (thus, unlike Allen, we also take into account quantitative constraints) including frame times, user‐defined periods, qualitative temporal constraints, and numeric quantifiers and the interactions between such components. We propose a specialized high‐level formalism to represent temporal constraints about periodic events; temporal reasoning on the formalism is performed by a path‐consistency algorithm repeatedly applying our operations of inversion, intersection, and composition and by a specialized reasoner about periods and numeric quantification. The high‐level formalism has been designed in such a way that different types of temporal constraints about periodic events can be represented in a compact and (hopefully) user‐friendly way and path‐consistency‐based temporal reasoning on the formalism can be performed in polynomial time. We also prove that our definitions of inversion, intersection, and composition and, thus, of our path‐consistency algorithm, are correct. This article also sketches the general architecture of the temporal manager for periodic events (TeMP⁺), that has been designed on the basis of our approach. As a working example, we show an application of our approach to scheduling in a school. © 2003 Wiley Periodicals, Inc.     

Multi-granularity and metric spatial reasoning

Composition reasoning is a basic reasoning task in qualitative spatial reasoning (QSR). It is an important qualitative method for robot navigation, node localization in wireless sensor networks and other fields. The previous composition reasoning works dedicated in single granularity framework. Multi-granularity spatial relation is not rare in real world, and some qualitative spatial relation models are multi-granularity models, such as RCC, STAR_m, CDC_m and OPRA_m. Although multi-granularity composition reasoning is very useful in many applications, it has not been systematically studied before. A special case of multi-granularity composition reasoning, referred to as metric spatial reasoning, is also discussed here. The general frameworks and basic theories for multi-granularity and metric spatial reasoning are put forward here. Furthermore, we redefine the spatial relation models for distance, topology and direction under the proposed multi-granularity and metric frameworks. We add metric representation for the OPRA_m. The multi-granularity and metric reasoning tasks are studied for these four models for the first time. Finally we perform some experiments on OPRA_m with encouraging results to verify our theories. Multi-granularity and metric spatial reasoning tasks are new problems in QSR and quite different from the previous works. Our works can be potentially applied in robot navigation, wireless sensor networks and other applications.     

Reasoning about fuzzy temporal information from the web: towards retrieval of historical events

When searching for information about historical events, queries are naturally formulated using temporal constraints. However, the structured temporal information needed to support such constraints is usually not available to information retrieval systems. Furthermore, the temporal boundaries of most historical events are inherently ill-defined, calling for suitable extensions of classical temporal reasoning frameworks. In this paper, we propose a framework based on a fuzzification of Allen’s Interval Algebra to cope with these issues. By using simple heuristic techniques to extract temporal information from web documents, we initially focus more on recall than on precision, relying on the subsequent application of a fuzzy temporal reasoner to improve the reliability of the extracted information, and to deal with conflicts that arise because of the vagueness of events. Experimental results indicate that a consistent and reliable knowledge base of fuzzy temporal relations can thus be obtained, which effectively allows us to target temporally constrained retrieval tasks.     

A complete chronicle discovery approach: application to activity analysis

Discovering temporal patterns hidden in a sequence of events has applications in numerous areas like network failure analysis, customer behaviour analysis, web navigation pattern discovery, etc. In this article, we present an approach to the discovery of chronicles hidden in the interaction traces of a human activity with the intention of characterizing some interesting tasks. Chronicles are a special type of temporal patterns, where temporal orders of events are quantified with numerical bounds. The algorithm we present is the first existing chronicle discovery algorithm that is complete. It is a chronicle discovery framework that can be configured to behave exactly as non‐complete algorithms existing in litterature with no reduction of performance, but it can also be extended to other useful chronicle discovery problems like hybrid episode discovery. We show that the complete chronicle discovery problem has a very high complexity but we argue and illustrate that this high complexity is acceptable when the knowledge discovery process in which our algorithm takes part is real time and interactive. The platform Scheme Emerger, also presented in this paper, has been developed in order to implement the algorithm and to support graphically the real time and interactive chronicle discovery process.     

Temporal Reasoning and Bayesian Networks

This work examines important issues in probabilistic temporal representation and reasoning using Bayesian networks (also known as belief networks). The representation proposed here utilizes temporal (or dynamic) probabilities to represent facts, events, and the effects of events. The architecture of a belief network may change with time to indicate a different causal context. Probability variations with time capture temporal properties such as persistence and causation. They also capture event interaction, and when the interaction between events follows known models such as the competing risks model, the additive model, or the dominating event model, the net effect of many interacting events on the temporal probabilities can be calculated efficiently. This representation of reasoning also exploits the notion of temporal degeneration of relevance due to information obsolescence to improve the efficiency.     

On integrating event definition and event detection

We develop, in this paper, a representation of time and events that supports a range of reasoning tasks such as monitoring and detection of event patterns which may facilitate the explanation of root cause(s) of faults. We shall compare two approaches to event definition: the active database approach in which events are defined in terms of the conditions for their detection at an instant, and the knowledge representation approach in which events are defined in terms of the conditions for their occurrence over an interval. We shall show the shortcomings of the former definition and employ a three-valued temporal first order nonmonotonic logic, extended with events, in order to integrate both definitions.     

不确定时态信息表示的统一模型

时态信息表示和推理是人工智能研究中的一个重要课题,现有的模型大多只能表示确定时态信息,然而现实生活中很多事件的发生结束等时态信息都是不确定的。故提出了一个表示不确定时态信息的统一模型,可用于描述各种具有确定或不确定时态信息的事件。该模型首先定义各类时态对象(如时间点、时间区间)以及它们之间的关系,并给出时态对象间的传递关系表,利用该表能进行时态一致性约束满足问题的求解。最后,给出了两个不确定时态推理的例子,表明了该模型的实际应用意义。     

Coping with complexity in home hemodialysis: a fresh perspective on time as a medium of Distributed Cognition

The existing literature on Distributed Cognition (DCog) mostly presents the temporal distribution of cognition in terms of system evolution that happens over time. In this paper, we illustrate how cognition can also be distributed through time in more immediate ways, through four principles we developed while studying how renal patients cope with the complexity of home hemodialysis. These principles are temporal assignments to tasks to aid prospective remembering; temporal arrangement of tasks to help deal with anticipated problems; temporal distribution of a task plan to avoid omission of steps; and temporal re-arrangement of tasks to reduce peak complexity. Like the physical environment, the time continuum is an external medium that can support distributed cognitive processes, serving as a representation for task reminders and allowing actors to organize the order, duration, and spacing of tasks to reduce complexity in cognitive work. These principles can highlight problems and opportunities in the design of socio-technical systems, by explicitly considering time as another medium that can be used to support DCog in short-term activity.     

Formal semantics of meta‐level architectures: Dynamic control of reasoning

Meta‐level architectures for dynamic control of reasoning processes are quite powerful. In the literature, many applications in reasoning systems modeling complex tasks are described, usually in a procedural manner. In this article we present a semantic framework based on temporal partial logic to describe the dynamics of reasoning behavior. Using these models, the semantics of the behavior of the whole (meta‐level) reasoning system can be described by a set of (intended) temporal models. © 2002 Wiley Periodicals, Inc.     

Temporal representation of clinical algorithms using expert-system and database tools

The HyperLipid Advisory System combines a rule-based implementation of a clinical algorithm (the NIH Cholesterol Education Program Expert Panel recommendations) with a temporal representation that facilitates reasoning over time while maintaining efficient storage in a standard database. The temporal representation consists of objects that model point events such as visits and interval events such as specific therapies. These objects are combined into abstractions called phases, which correspond to higher level clinical concepts such as a diet or drug treatment. The time-oriented data objects are referenced in the rules using a custom-tailored operator query language. Between user sessions relevant clinical data are stored in external files. When the advisory system is reconsulted, this information is retrieved and mapped back into an object-oriented format. Use of a commercially available expert-system shell for such tasks allows algorithm implementation in standard personal computing environments.     

Privacy preserving existence recognition and construction of hypertree agent organization

Decentralized probabilistic reasoning, constraint reasoning, and decision theoretic reasoning are some essential tasks of cooperative multiagent systems. Several frameworks for these tasks organize agents into a junction tree (JT). We show that existing techniques for JT existence recognition and construction leak information on private variables, shared variables, agent identities and adjacency, that can potentially be protected. We present a scheme to quantify these privacy losses. We develop two novel algorithms for JT existence recognition and for JT construction when existing, that provide strong guarantee of agent privacy. Our experimental comparison shows that the proposed algorithms out-perform existing techniques, one of them having the lowest privacy loss and the other having no privacy loss, while being more efficient than most alternatives.     

Temporal stability in predictive process monitoring

Predictive process monitoring is concerned with the analysis of events produced during the execution of a business process in order to predict as early as possible the final outcome of an ongoing case. Traditionally, predictive process monitoring methods are optimized with respect to accuracy. However, in environments where users make decisions and take actions in response to the predictions they receive, it is equally important to optimize the stability of the successive predictions made for each case. To this end, this paper defines a notion of temporal stability for binary classification tasks in predictive process monitoring and evaluates existing methods with respect to both temporal stability and accuracy. We find that methods based on XGBoost and LSTM neural networks exhibit the highest temporal stability. We then show that temporal stability can be enhanced by hyperparameter-optimizing random forests and XGBoost classifiers with respect to inter-run stability. Finally, we show that time series smoothing techniques can further enhance temporal stability at the expense of slightly lower accuracy.     

结合度量空间和时间的逻辑

与时态和空间有关的推理问题是人工智能研究中重要的组成部分,近年来时空逻辑的研究受到相关领域研究者的极大重视。以多维逻辑为框架表示时空知识,提出了一组将度量空间逻辑和时态逻辑相结合的逻辑模型PTL-MS、PTL-MS1、PTL-MS2,表示和推理随时间变化的距离关系,看成是时态逻辑和度量逻辑的迪卡尔乘积,给出了语义和语法,研究了它们的表达能力,用于时空约束满足问题、时空知识库以及移动对象数据库(MOD)等。     

应急系统中不确定时态知识表示及其推理

在分析现有不确定信息表示模型基础上,提出了一种可扩展的时态知识表示模型,该模型对真实事件的间断性表示加以改进,进而讨论了应急决策知识库的构建方法和相应推理机制。实验数据表明,在原型系统中使用改进后的表示模型和推理方法,能够提高应急系统处理效率和决策方案的合理性。