Temporal Constraint Propagation Research Articles

Incrementally Solving STNs by Enforcing Partial Path Consistency

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.

A fast temporal constraint semi-dense stereo matching method

For some applications that do not require a dense disparity map, we propose a fast semi-dense stereo matching method. First, we use grading rank transformation to obtaining the texture points of the image. Then, we use downsampling to reduce the matching complexity of texture points between consecutive frames. The disparity search range of the current frame is constrained according to the matching of texture points between consecutive frames and the disparity map obtained from the preceding frame. Finally, we perform multi-constrained stereo matching and disparity propagation to obtain the disparity map of the current frame. The proposed method reduces the complexity of stereo matching by the temporal disparity constraint and disparity propagation. Experimental results show that the semi-dense disparity map obtained is accurate and fast. For some video sequences, the proposed method can achieve real-time running speed.

Probabilistic Quantitative Temporal Constraints: Representing, Reasoning, and Query Answering

In many applications and domains, temporal constraints between actions, and their probabilities play an important role. We propose the first approach in the literature coping with probabilistic quantitative constraints. To achieve such a challenging goal, we extend the widely used simple temporal problem (STP) framework to consider probabilities. Specifically, we propose i) a formal representation of probabilistic quantitative constraints, ii) an algorithm, based on the operations of intersection and composition, for the propagation of such temporal constraints, and iii) facilities to support query answering on a set of such constraints. As a result, we provide users with the first homogeneous method supporting the treatment (representing, reasoning, and querying) of probabilistic quantitative constraints, as required by many applications and domains.

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.

Geometry-based propagation of temporal constraints

In recent years, the Internet of Things (IoT) has been introduced to offer promising solutions in many areas. A big challenge faced by the IoT is to integrate heterogeneous information sources and process information effectively. As an important element in information integration, temporal reasoning is highly related to the dynamic, sequential aspect of both the information integration and the decision making process. Focusing on temporal reasoning, this paper introduces a method to represent both qualitative and quantitative temporal constraints in a 2-dimensional (2-D) space. Meanwhile, an efficient constraint-based geometric (CG) algorithm for propagating constraints (including inherent constraints and constraint pairs) on events in a 2-D space is proposed. A geometric recombination and intersection (GRI) method, a part of the CG algorithm, is presented to propagate one constraint pair from a geometric point. The experimental results show that in terms of both constructed and realistic benchmarks, the CG algorithm outperforms the existing Floyd-Warshall's algorithm with the time complexity of O(n3), especially for benchmarks with a large number of events.

Temporal reasoning about composite and/or periodic events

In many application areas, including planning, workflow, guideline and protocol management, the description of the domain involves composite and/or periodic events, mutually related by temporal constraints on the execution order. Such events represent ‘classes’, since they can be instantiated to specific executions of the plan, guideline etc., and each execution must ‘respect’ the temporal constraints imposed on the corresponding classes. The main goal of our work is to propose an approach dealing with the above-mentioned temporal phenomena. To achieve such an objective, the authors propose a tractable domain-independent temporal reasoner. This enhances the generality of our approach, which provides a domain-independent module that can be integrated with other software tools to solve temporal problems in specific domains. From the methodological point of view, the authors first devise a representation formalism coping with the aforesaid phenomena, and then they describe temporal constraint propagation algorithms to deal with constraint inheritance and to perform temporal consistency checking. The representation formalism has been designed carefully, to obtain algorithms that are both complete and tractable. Finally, the paper also shows experimental results, including an application of the authors’ approach to clinical guidelines, evaluating their impact on future applications and research activities.

Representing Action and Time in an RMS Based Framework

A reason maintenance system (RMS) has been in use for representation of actions. This paper presents an architecture that handles both action and time in a framework while preserving the advantages of both RMS and temporal constraint propagation(TCP). This framework captures action as well as temporal knowledge in the network in terms of relations between intervals. Thus it enables integration of temporal reasoning along with causal reasoning among activities. An algorithm for truth propagation in such a network has been applied to a specific domain, and an example illustrating its use in temporal and causal reasoning is provided. Abstraction of activities and states has been done by defining abstract temporal intervals and thus layering the knowledge into various levels. Some relational abstractions which are useful in describing the temporal relationships among activities and facts have also been defined.

A Path-Consistent Singleton Modeling (CSM) Algorithm for Arc-Constrained Networks

There is very little of “time” in a temporal constraint propagation algorithm. Most of these algorithms could easily handle reasoning over any domain mapable onto rational numbers, e.g., weight, frequency, luminosity, etc. Actually some of these algorithms are capable of handling more sophisticated domains than those mapped onto rational numbers, e.g., intervals, or partially ordered objects (say, time). In this article we have generalized such an algorithm, which was originally developed for time-interval domain, to any generic domain, where binary constraints are expressed over arcs of the constraint network. Given the composition table for the primitive relations between a pair of the domain entities (e.g., intervals) as an additional input along with a constraint graph, the algorithm would generate all consistent singleton models for a given network. The algorithm is also extended here to handle uncertainty values.

Experimenting with a temporal constraint propagation algorithm

3-consistency algorithm for temporal constraint propagation over interval-based network, proposed by James Allen, is finding its use in many practical temporal reasoning systems. Apart from the polynomial behavior of this algorithm with respect to the number of nodes in the network, very little is known about its time complexity with respect to other properties of the initially given temporal constraints. In this article we have reported some of our results analyzing the complexity with respect to some structural parameters of the input constraint network. We have identified some regions, with respect to the structural parameters of the input network, where the algorithm takes much more time than it needs over other regions. Similar features have been observed in recent studies on NP-hard problems. Average case complexity of Allen's algorithm is also studied empirically, over a hundred thousand randomly generated networks, and the growth rate is observed to be of the order of quadratic with respect to the problem size (at least up to node 40, and expected to be lower above that). We have analyzed our data statistically to develop a model with which one can calculate the expected time to be consumed by the algorithm for a given input network.

Embedding temporal constraint propagation in machine sequencing for job shop scheduling

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.

An On-line Scheduling Method for Flexible Manufacturing Systems by a Hierarchical Decision Making System.

With introduction of automated production lines, it is becoming more difficult to manage manufacturing systems. Many scheduling methods have been researched to improve the efficiency of manufacturing systems, yet they have not been utilized in large scale and complex systems because of many requirements and constraints in real manufacturing systems and lack of know-how for large scale and complex production control.This paper presents a hierarchical scheduling method for an on-line production line control, which consists of a strategic level scheduler (SLS) and operational level schedulers (OLS). The SLS aims at making a schedule referred by the OLS. The schedule solved by the SLS satisfies some constraints, and it is generated by a scheduling algorithm based on a human planning procedure and a temporal constraint propagation mechanism. To obtain a desired result (expected throughput), it is necessary to control the production line to obey the higher level decision. The OLS aims at the on-line control of the production line and is based on the know-how of the supervisor.This hierarchical method is evaluated by discrete event simulations before introducing it into real production lines. The results show that this method is more useful than the practical operation subject to minimizing of lateness and tardiness to dynamic environment such as failures of machines or AGVs.

CONSISTENCY CHECKING ALONG TIME

Most model-based approaches to diagnosis require a consistency-checking procedure to perform their task. When dealing with a dynamically changing system, such a procedure must take into account time-varying data. This requires suitable techniques for reasoning over time. Additional difficulties arise when delays are involved in interactions between variables. The worst case occurs when some of the delays are completely unspecified. This report presents an approach to consistency checking that handles qualitative models of dynamic systems exhibiting time lags. A component-centered ontology is adopted to model the structure of the physical system, and an episode-based approach is adopted for representing its behavior over time. An example consisting of a physical process exhibiting transportation lags is used to illustrate the power of the approach. We present algorithms and an implementation in PROLOG called C-CAT (consistency checking along time). Some meaningful outputs from the program are used as examples. The solution proposed represents an extension to B. C. Williams' temporal constraint propagation methodology. It also extends the applicability range of existing approaches to model-based diagnosis, permitting its use in tasks such as online diagnosis of dynamic systems.