Temporal Reasoning Techniques Research Articles

Activity recognition in video sequences over qualitative abstracts of a diagram-based representation schema

Explicit reasoning over a spatial substrate, i.e., space–time information structures underlying a spatial problem, simplifies reasoning. Diagrammatic reasoning makes use of diagrams for exploiting such underlying structures. This paper proposes a novel approach combining diagrammatic reasoning with qualitative spatial and temporal reasoning techniques to visualize and perceive spatio-temporal relations among objects in a video. The hybrid techniques explore information over the spatial substrate for relational extractions. Different relations among objects in transition define short-term activities. Mealy machines are learned over patterns of short-term activities as activity recognizers. The proposed representation and recognition mechanism is validated by conducting experiments for video activity recognition from DARPA Mind’s Eye and J-HMDB dataset.

Temporal detection and analysis of guideline interactions.

Clinical practice guidelines (CPGs) are assuming a major role in the medical area, to grant the quality of medical assistance, supporting physicians with evidence-based information of interventions in the treatment of single pathologies. The treatment of patients affected by multiple diseases (comorbid patients) is one of the main challenges for the modern healthcare. It requires the development of new methodologies, supporting physicians in the treatment of interactions between CPGs. Several approaches have started to face such a challenging problem. However, they suffer from a substantial limitation: they do not take into account the temporal dimension. Indeed, practically speaking, interactions occur in time. For instance, the effects of two actions taken from different guidelines may potentially conflict, but practical conflicts happen only if the times of execution of such actions are such that their effects overlap in time. We aim at devising a methodology to detect and analyse interactions between CPGs that considers the temporal dimension. In this paper, we first extend our previous ontological model to deal with the fact that actions, goals, effects and interactions occur in time, and to model both qualitative and quantitative temporal constraints between them. Then, we identify different application scenarios, and, for each of them, we propose different types of facilities for user physicians, useful to support the temporal detection of interactions. We provide a modular approach in which different Artificial Intelligence temporal reasoning techniques, based on temporal constraint propagation, are widely exploited to provide users with such facilities. We applied our methodology to two cases of comorbidities, using simplified versions of CPGs. We propose an innovative approach to the detection and analysis of interactions between CPGs considering different sources of temporal information (CPGs, ontological knowledge and execution logs), which is the first one in the literature that takes into account the temporal issues, and accounts for different application scenarios.

Intelligent user interfaces for calendar management

Reasoning about time for calendar management can be complex, especially when handling multi-user events, because a possibly large number of temporal constraints might have to be analyzed to find solutions which satisfy the commitments of the involved people. Artificial Intelligence temporal reasoning techniques are the key to address this issue but, due to the personal nature of calendar management, they have to be applied in a model that grants the user control in the scheduling decisions to be made. This paper explores the use of Intelligent User Interfaces to mitigate the problem by supporting the management of a dialog between user and automated calendar manager during the scheduling process. The idea is that of enabling the user to steer the calendar manager in the selection of the scheduling solutions to be applied when a temporal conflict occurs. In this way, the user benefits from automated reasoning support but controls the system inferences on the basis of her/his own preferences. The paper also describes the results of a user test we carried out to assess the suitability of the proposed model.

Avian influenza: Temporal modeling of a human to human transmission case

The aim of this paper is to demonstrate how temporal reasoning techniques can assist in the analysis of epidemiological data to determine the possibility of contagion among humans of the avian influenza virus (H5N1). To do so, we use a documented real case of a possible infection of H5N1 between humans, and formally substantiate the medical evidence. The need to represent and manage time is implicit in many reasoning processes. This is particularly evident in the field of medicine, in which the temporal distribution of symptoms is crucial for understanding the evolution of diseases. Many models have been proposed for time representation and management. In this paper we propose the use of a generic temporal reasoner, FuzzyTIME, which is able to represent both points and intervals, as well as relations, including quantitative and qualitative ones, through a specifically designed language. Moreover, being based on FTCN (fuzzy temporal constraint networks), the proposed model also allows us to capture both temporal imprecision and uncertainty present in complex domains as the medical one. Nevertheless, the reasoner has been design to have an easy integration with any knowledge based system that need this kind of representation and reasoning. To perform the analysis of the real case selected, we first clearly identified the temporal information in each of the timelines of the subjects under study. Subsequently, this information was formalized in the language provided by FuzzyTIME. After verifying the temporal consistency of such information in every subject, we proceeded to check the temporal consistency of such information with the generic clinical symptoms of evolution of avian influenza. Finally, the queries which were modeled allowed us to determine the temporal possibility of infection between human of the H5N1 virus. First, we demonstrate the usefulness of temporal reasoning in the modeling and analysis of epidemiological data, showing how the FTCN formalism and the proposed language is sufficiently expressive to represent qualitative and quantitative temporal relations as well as the temporal imprecision implicit in certain expressions extracted from the real case selected. Moreover, the temporal reasoner permits efficient determination of the temporal consistency of the medical evidence concerning the possibility of infection of H5N1 between humans.

Fuzzy theory approach for temporal model-based diagnosis: An application to medical domains

The aim of this work is to provide a theoretical framework which is sufficiently expressive to describe temporal evolution of diseases, and also to propose a diagnostic process for building explanations of patient's observed temporal evolution based on these disease descriptions. Model-based diagnosis (MBD) tackles the problem of troubleshooting systems by starting from a description of their structure and function (or behaviour). It is in this area where the use of deep causal models, as part of MBD systems, has shown its greater efficiency over classical rule based systems. From its beginnings, the temporal dimension was considered as an important component in MBD, since it makes it possible to define the dynamic behaviour. Several approaches have been proposed to represent time in MBD, enabling the representation of temporal concepts and relations, as well as the use of temporal reasoning mechanisms. We first propose a temporal behavioural model (TBM), which allows us to capture the dynamics underlying temporal evolution of diseases and to include contextual information. Contextual information is required to model how contextual factors change the temporal evolution of diseases. The temporal component is modelled by fuzzy temporal constraints networks (FTCN), which makes the representation of quantitative and qualitative imprecise temporal information possible. We also provide a diagnostic process, which is based on a temporal adaptation of classical cover and differentiate method. The TBM and diagnostic process proposed provides a unique framework which addresses three problems not dealt with together so far: (a) the inclusion of contextual information, (b) the expressivity of the solution provided, and (c) the evaluation of the diagnostic hypotheses. This proposal demonstrates that the FTCN formalism provides mechanisms sufficiently expressive to cope with the intrinsic imprecision in the description of diseases' temporal evolution. The explanation generated provides the user with a complete picture of the temporal evolution of diseases and its causal links, thus allowing the appearance of repeated instances of the same disease through time. Mechanisms are provided which evaluate the credibility of alternative hypotheses, based on possibility theory. A prototype is presented along with a knowledge acquisition tool that guides medical experts in the model building process. In this paper, we propose a model that tightly couples methods from MBD area with constraint-based temporal reasoning techniques. The proposed model allows us to model complex contextual relationships in a compact way as well as providing solutions expressive enough to be used for decision support purposes. The solution provided conforms a causal network entailing the abnormal observations, including pathophysiological and etiological states. Furthermore, different instances of the same diagnostic hypotheses, located at different time instants, are also possible in the final solution. Finally, we provide an analysis of related and future works.

An Approach to Temporal Planning and Scheduling in Domains with Predictable Exogenous Events

The treatment of exogenous events in planning is practically important in many real-world domains where the preconditions of certain plan actions are affected by such events. In this paper we focus on planning in temporal domains with exogenous events that happen at known times, imposing the constraint that certain actions in the plan must be executed during some predefined time windows. When actions have durations, handling such temporal constraints adds an extra difficulty to planning. We propose an approach to planning in these domains which integrates constraint-based temporal reasoning into a graph-based planning framework using local search. Our techniques are implemented in a planner that took part in the 4th International Planning Competition (IPC-4). A statistical analysis of the results of IPC-4 demonstrates the effectiveness of our approach in terms of both CPU-time and plan quality. Additional experiments show the good performance of the temporal reasoning techniques integrated into our planner.

Modeling and reasoning techniques in geologic interpretation

A thorough investigation is reported on the qualitative modeling of geologic systems, focusing on the reconstruction of three-dimensional (3-D) profiles from image data by means of spatial and temporal reasoning techniques. A conceptual model of the relevant knowledge is proposed for both the domain elements and the inference processes. At the former level the authors describe the objects in terms of geometric primitives and relations among them; at the inference level, reconstruction is identified as a synthesis task, in which a 3-D model of underground bodies results from assembling simpler components. The process is incremental and nomnonotonic, according to a basic assemble-validate-and-debug cycle, underlying both low-level and high-level steps. A formal (logical) model of the latter is proposed and worked out in detail. Concepts from topology and graph theory provide effective tools to define representations and algorithms, and allow one to address the intertwining of spatial and temporal knowledge. Some relevant reasoning steps are also regarded as constraint satisfaction problems. The authors analyze the constraints, show that the related tasks can be solved with algorithms of polynomial complexity, and provide the appropriate procedures. The practical feasibility of the model has been tested, and results of the applications to realistic input data are discussed. The authors also discuss solutions for embedding the modules into a man-machine interface for the intelligent support to the interpretation of data.

Qualitative and quantitative temporal constraints and relational databases: theory, architecture, and applications

Many different applications in different areas need to deal with both: databases, in order to take into account large amounts of structured data; and quantitative and qualitative temporal constraints about such data. We propose an approach that extends: temporal databases and artificial intelligence temporal reasoning techniques and integrate them in order to face such a need. Regarding temporal reasoning, we consider some results that we proved recently about efficient query answering in the Simple Temporal Problem framework and we extend them in order to deal with partitioned sets of constraints and to support relational database operations. Regarding databases, we extend the relational model in order to consider also qualitative and quantitative temporal constraints both in the data (data expressiveness) and in the queries (query expressiveness). We then propose a modular architecture integrating a relational database with a temporal reasoner. We also consider classes of applications that fit into our approach and consider patient management in a hospital as an example.

Spatial and temporal reasoning techniques in geological modeling

In building geological models of the crust, spatial and temporal reasoning plays an essential role, providing constraints that narrow down the set of acceptable models. We present here the main results of an analysis of the temporal and spatial reasoning in geological modeling. Using concepts from topology and graph theory, we propose a mathematical model of sedimentary bodies, which captures some of their basic qualitative properties, including the relations among them. We introduce a formal representation of the spatial and temporal constraints that consistent models must satisfy, and present a set of algorithms aimed at: 1) helping the user in focusing attention on the most relevant structural elements, like markers and pinch-outs; 2) validating the spatial and temporal consistency of model hypotheses and diagnosing inconsistencies. These tools have been applied to the problem of building subsurface models from reflection seismic exploration data, as a part of the Horizons research project aimed at the construction of an advanced workstation for seismic interpretation. The design principles and the architecture of the system are also summarised.

Temporal constraint reasoning mechanisms for microprocessor systems diagnosis

Abstract A diagnostic system for microprocessor systems design is being designed and developed. In microprocessor systems diagnosis and design, temporal reasoning about event changes occurring at imprecisely knon time instants is an important issue. The concept of a time range has been proposed to capture the notion of time imprecision in event occurrence. According to this concept, efficient temporal reasoning techniques for constraint satisfaction and propagation of time ranges have been developed for the embedding of domain knowledge in a deep-level constraint model. The imprecision in these events contributes to a certain degree of uncertainty about the correctness of a microprocessor system operation. The proposed system performs worst-case timing analysis. In particular, for the asynchronous bus operation of the MC68000 microprocessor, the sequence of events during a read cycle is traced through an inference process to determine whether any constraint in the model is violated. Satisfactory results have been obtained in a practical implementation of the system using the CLIPS expert system shell.

Microprocessor systems diagnosis using a time-range approach

In many aspects of microprocessor systems design and diagnosis, temporal reasoning of a sequence of event changes occurring at imprecisely known time instants is often required. By combining the change-based and time-based approaches of temporal logics, the concept of time range is proposed as a key component of an enhanced time structure which captures the notion of time impreciseness in event occurrence. A practical MC68000 CPU-memory interface design problem is used as an extensive example to illustrate the various temporal reasoning techniques derived from this new time structure. Efficient methods for time referencing, constraint satisfaction and propagation of time ranges have been developed for embedding domain knowledge in a deep-level constraint model. It is also shown how different shallow rules (regarded as expert's rule of thumb) for system diagnosis can be generated and explained through an inference process of the constraint model.

AN APPROACH TO REASONING ABOUT HYBRID SYSTEMS

A formal approach is presented for proving temporal properties of dynamic systems. Its main advantage is that it can be used to prove properties of hybrid systems, i.e. those whose state contains both discrete and continuous parameters. In contrast, most current temporal reasoning techniques are restricted either to purely discrete systems or to purely continuous systems. Our approach is based upon a new modeling technique called DMOD. A DMOD model of a system defines the causality relation between events in the system, using definite clauses, i.e. logic programs. Thereby, the problem of reasoning about hybrid systems is reduced to one of reasoning about the behavior of definite clauses. As these possess a simple proof theory, reasoning is substantially simplified.

Architectural Issues for Expert Systems in Real-Time Control

There is an increasing interest in using expert system technology in the field of automatic control. We show that some particular requirements are imposed on the functionality and architecture of expert systems which shall support or extend conventional control systems. We discuss the necessity and the impacts of deep modeling, temporal reasoning and reason maintenance techniques from an architectural point of view. We argue for the utility of a separate mediating module, which tries to reduce the gap between the different knowledge levels of a conventional control system and an expert system. Furthermore, we show that it is highly valuable to have a distributed architecture such that the expert system and the mediating module consist of a set of parallel processes. The ideas developed in this paper are based on three years of experience in the German national venture TEX-I.

Architectural issues for expert systems in real-time control

Abstract There is an increasing interest in using expert system technology in the field of automatic control. We show that some particular requirements are imposed on the functionality and architecture of expert systems which shall support or extend conventional control systems. We discuss the necessity and the impacts of deep modeling, temporal reasoning and reason maintenance techniques from an architectural point of view. We argue for the utility of a separate mediating module, which tries to reduce the gap between the different knowledge levels of a conventional control system and an expert system. Furthermore, we show that it is highly valuable to have a distributed architecture such that the expert system and the mediating module consist of a set of parallel processes. The ideas developed in this paper are based on three years of experience in the German national venture TEX-I.