Conflict Detection Algorithm Research Articles

Constraint-based conflict and error management

The objective of this research is to develop a general methodology to manage conflicts and errors (CEs). Two constraint-based conflict and error prevention and detection (CEPD) algorithms, centralized and decentralized algorithms, have been designed. These two algorithms model and utilize the relationship between CEs for effective CEPD. They are compared with the traditional CEPD algorithm over undirected and directed constraint networks. Experiments results show that the two CEPD algorithms outperform the traditional CEPD algorithm in terms of four performance measures. The decentralized CEPD algorithm shall be used to manage CEs over a large constraint network. If information transmission between distributed agents is not feasible for the decentralized CEPD algorithm, the centralized CEPD algorithm shall be used for CEPD. The centralized CEPD algorithm, however, is sensitive to network topology and information transmission time.

Identification of conflicting questions in the PARES system

<p>Student testing and knowledge assessment is a significant aspect of the learning process. In a number of cases, it is expedient not to present the exact same test to all learners all the time (Pritchett, 1999). This may be desired so that cheating in the exam is made harder to carry out or so that the learners can take several practice tests on the same subject as part of the course.</p><p><br />This study presents an e-testing platform, namely PARES, which aims to provide assessment services to academic staff by facilitating the creation and management of question banks and powering the delivery of nondeterministically generated test suites. PARES uses a conflict detection algorithm based on the vector space model to compute the similarity between questions and exclude questions which are deemed to have an unacceptably large similarity from appearing in the same test suite. The conflict detection algorithm and a statistical evaluation of its accuracy are presented. Evaluation results show that PARES succeeds in detecting question types at about 90% and its efficiency can be further increased through continuing education and enrichment of the system’s correlation vocabulary.<br /><br /></p><p> </p>

A CD&CR causal model based on path shortening/path stretching techniques

Some attempts have been done to alleviate the airspace congestion in Terminal Manoeuvring Area (TMA): reduced vertical separation minima, negotiation of voluntary reductions in scheduled service, construction of additional runways at major airports. However, there is still a pending matter to be solved regarding the efficiency of the airspace use, by avoiding non-efficient procedures such as holdings. In line with the requirements of the future air traffic management (ATM) concepts proposed by SESAR and NextGen, the air-traffic flow needs to be more predictable to offer the possibility of more effective use of airspace and airport capacity. In the context of SESAR, 4D trajectory management is expected to improve air traffic operations, in particular to increase the overall predictability of traffic, with benefit to airlines and air traffic management. A deep knowledge concerning the events that take place in the air traffic management of 4D trajectories and their interactions in a TMA is essential to remove non-effective operations, avoid delay propagation between arrivals and optimise the occupancy of the runway. The present paper describes a discrete event model, based on Coloured Petri-Nets formalism, useful to specify a conflict detection and conflict resolution algorithm focusing on the arrival phase of flight. The causal model developed considers different alternative pre-defined turning points for each flight by evaluating path shortening/path stretching of all trajectories upwards the assigned merging points in a TMA.

An Efficient Conflict Detection Algorithm for Packet Filters

Packet classification is essential for supporting advanced network services such as firewalls, quality-of-service (QoS), virtual private networks (VPN), and policy-based routing. The rules that routers use to classify packets are called packet filters. If two or more filters overlap, a conflict occurs and leads to ambiguity in packet classification. This study proposes an algorithm that can efficiently detect and resolve filter conflicts using tuple based search. The time complexity of the proposed algorithm is O(nW+s), and the space complexity is O(nW), where n is the number of filters, W is the number of bits in a header field, and s is the number of conflicts. This study uses the synthetic filter databases generated by ClassBench to evaluate the proposed algorithm. Simulation results show that the proposed algorithm can achieve better performance than existing conflict detection algorithms both in time and space, particularly for databases with large numbers of conflicts.

Conflict Resolution in Converging Air Traffic Using Trajectory Patterns

A method for conflict detection and resolution based on the parameterization of the aircraft intents is presented. The method is also based on the use of predefined trajectory patterns, which are in fact flight intents; these patterns include standard airline procedures and air traffic control regulations. The conflict resolution problem is formulated as a parametric optimization problem subject to constraints; the optimality criterium is defined so that the deviation from the intended (preferred) trajectories in the lateral profile is minimized. The resolution trajectory patterns take into account changes of the nominal waypoints (vectoring) and changes of the aircraft speeds. The method is applied to the case of multiple conflicts among commercial transport aircraft in converging traffic in the terminal area; different scenarios are considered, which include locked aircraft, that is, aircraft whose trajectories are known and fixed. The conflict detection and resolution algorithm relies on a kinetic trajectory predictor (nonlinear point-mass model with variable mass), which is accurate, flexible, and transparent, and provides the high-fidelity prediction required in the analysis of the demanding terminal area traffic. The cost of the global conflict resolution process is assessed, in terms of extra distance traveled, extra flight time, and extra fuel consumed for each aircraft.

BFX: DIAGNOSING CONFLICTING REQUIREMENTS IN CONSTRAINT-BASED RECOMMENDATION

When interacting with constraint-based recommender applications, users describe their preferences with the goal of identifying the products that fit their wishes and needs. In such a scenario, users are repeatedly adapting and changing their requirements. As a consequence, situations occur where none of the products completely fulfils the given set of requirements and users need a support in terms of an indicator of minimal sets of requirements that need to be changed in order to be able to find a recommendation. The identification of such minimal sets relies heavily on the existence of (minimal) conflict sets. In this paper we introduce BFX (Boosted FastXplain), a conflict detection algorithm which exploits the basic structural properties of constraint-based recommendation problems. BFX shows a significantly better performance compared to existing conflict detection algorithms. In order to demonstrate the performance of BFX, we report the results of a comparative performance evaluation.

Tactical Conflict Detection in Terminal Airspace

Air traffic systems have long relied on automated short-term conflict prediction algorithms to warn controllers of impending conflicts (losses of separation). The complexity of terminal airspace has proven difficult for such systems as it often leads to excessive false alerts. Thus, the legacy system, called Conflict Alert, which provides short-term alerts in both en-route and terminal airspace currently, is often inhibited or degraded in areas where frequent false alerts occur, even though the alerts are provided only when an aircraft is in dangerous proximity of other aircraft. This research investigates how a minimal level of flight intent information may be used to improve short-term conflict detection in terminal airspace such that it can be used by the controller to maintain legal aircraft separation. The flight intent information includes a site-specific nominal arrival route and inferred altitude clearances in addition to the flight plan that includes the RNAV (Area Navigation) departure route. A new tactical conflict detection algorithm is proposed, which uses a single analytic trajectory, determined by the flight intent and the current state information of the aircraft, and includes a complex set of current, dynamic separation standards for terminal airspace to define losses of separation. The new algorithm is compared with an algorithm that imitates a known en-route algorithm and another that imitates Conflict Alert by analysis of false-alert rate and alert lead time with recent real-world data of arrival and departure operations and a large set of operational error cases from Dallas/Fort Worth TRACON (Terminal Radar Approach Control). The new algorithm yielded a false-alert rate of two per hour and an average alert lead time of 38 seconds.

An Extensible Dialogue Script for a Robot Based on Unification of State-Transition Models

We propose extension-by-unification method to improve reusability of the dialogue components in the development of communication function of the robot. Compared to previous extension-by-connection method used in behavior-based communication robot developments, the extension-by-unification method has the ability to decompose the script into components. The decomposed components can be recomposed to build a new application easily. In this paper, first we, explain a reformulation we have applied to the conventional state-transition model. Second, we explain a set of algorithms to decompose, recompose, and detect the conflict of each component. Third, we explain a dialogue engine and a script management server we have developed. The script management server has a function to propose reusable components to the developer in real time by implementing the conflict detection algorithm. The dialogue engine SEAT (Speech Event-Action Translator) has flexible adapter mechanism to enable quick integration to robotic systems. We have confirmed that by the application of three robots, development efficiency has improved by 30%.

Terminal-Area Aircraft Tracking Using Hybrid Estimation

A bottleneck to the flow of air traffic is in the traffic around airports. Accurate tracking of aircraft movements around airports is important to improve traffic capacity and safety. We propose a hybrid estimation algorithm that uses the knowledge of departure or arrival aircraft flight plans and nominal flight profiles to improve tracking of aircraft movements around airports. The algorithm also provides an accurate estimation of aircraft flight modes that is often used in conflict detection, intent inference, and trajectory prediction algorithms. In the algorithm, we first develop a methodology to model flight mode transitions based on typical aircraft landing profiles, takeoff profiles, and standard flight routes around airports. Based on the model, we derive a conditional mode transition matrix that is dependent (or conditioned) on the continuous state of the aircraft. The aircraft-tracking problem is then solved using a hybrid estimation algorithm. We also present an analytical result that reduces the dimension, and hence the computational cost, of a multivariate integral that arises due to the continuous-state-dependent mode transition matrix. We present two simulation examples to illustrate the performance of our aircraft-tracking algorithm.

An ensemble approach for conflict detection in Free Flight by data mining

Airborne separation assurance is a key requirement for Free Flight operations. A variety of conflict detection (CD) and resolution algorithms have been developed for this task. A lack of rigorous evaluation and the existence of an infinite number of possible conflict geometries in Free Flight makes the choice of which algorithm to be placed in the cockpit a challenging task for the designers of future air traffic management (ATM) systems. In this paper, we propose an ensemble approach for CD in Free Flight. The ensemble consists of several CD algorithms, a rule set for each algorithm describing its learned behavior from its past performance and a switch mechanism to choose an appropriate CD algorithm given probe characteristics. A novel mechanism to evolve complex conflict scenarios, using genetic algorithms (GA), is developed and integrated in a fast time air traffic simulator to generate the performance data of CD algorithms. Data mining techniques are then employed to identify implicit patterns in the probe characteristics where the CD algorithms missed or falsely identified a conflict. These patterns are formulated as rule sets for each CD algorithm and are then used by a switch in the ensemble to route a probe for conflict prediction. Given probe characteristics, the CD algorithm, which is less likely to miss or falsely identify a conflict, is selected to evaluate the probe for potential conflict. The performance of the ensemble and of individual algorithms is evaluated by comparing the Pareto efficient set of solutions generated by them. The ensemble approach demonstrates a significant reduction in the number of missed detects and false alarms as compared to individual algorithms. The proposed methodology is capable of accommodating existing as well as new CD models and can be extended to other ATM concepts as well.

RDFacl: A Secure Access Control Model Based on RDF Triple

An expectation for more intelligent Web is recently being reflected through the new research field called Semantic Web. In this paper, related with Semantic Web security, we introduce an RDF triple based access control model having explicit authorization propagation by inheritance and implicit authorization propagation by inference. Especially, we explain an authorization conflict problem between the explicit and the implicit authorization propagation, which is an important concept in access control for Semantic Web. We also propose a novel conflict detection algorithm using graph labeling techniques in order to efficiently find authorization conflicts. Some experimental results show that the proposed detection algorithm has much better performance than the existing detection algorithm when data size and number of specified authorizations become larger.

Fast computation of query relaxations for knowledge-based recommenders

“No matching product found” is an undesirable message for the user of an online product finder application or interactive recommender system. Query Relaxation is an approach to recovery from such retrieval failures and works by eliminating individual parts of the original query in order to find products that satisfy as many of the user's constraints as possible. In this paper, new techniques for the fast computation of “user-optimal” query relaxations are proposed. We show how the number of costly catalog queries can be minimized with the help of a query pre-processing approach, how we can compute relaxations that contain at least n items in the recommendation, and finally, how a recent conflict-detection algorithm can be applied for fast determination of preferred conflicts in interactive recovery scenarios.

Maintaining time and space consistencies in hybrid CAD environments: Framework and algorithms

Based on the analysis of two types of collaborations during engineering design, namely time collaboration and space collaboration, this paper introduces a Total Data Model (TDM) centered framework to support the maintenance of time and space consistencies in hybrid CAD environments. By separating data and view, the proposed framework supports view-wandering between different perspectives so that designers can adjust their behaviors to avoid conflicts. The maintenance of time consistency is achieved by utilizing the Address Space Transformation (AST) algorithm. In order to adapt AST to new large-scale design environments, an effective garbage collection strategy is proposed. As for space collaboration, a conflict detection algorithm based on feature face is introduced. A prototype system has been developed to prove the validity, efficiency, and feasibility of the proposed framework.

Efficient Conflict Detection in Graph Transformation Systems by Essential Critical Pairs

The well-known notion of critical pairs already allows a static conflict detection, which is important for all kinds of applications and already implemented in AGG. Unfortunately the standard construction is not very efficient. This paper introduces the new concept of essential critical pairs allowing a more efficient conflict detection. This is based on a new conflict characterization, which determines for each conflict occuring between the rules of the system the exact conflict reason. This new notion of conflict reason leads us to an optimization of conflict detection. Efficiency is obtained because the set of essential critical pairs is a proper subset of all critical pairs of the system and therefore the set of representative conflicts to be computed statically diminishes. It is shown that for each conflict in the system, there exists an essential critical pair representing it. Moreover each essential critical pair is unique with regard to its conflict reason and thus represents each conflict not only in a minimal, but also in a unique way. Main new results presented in this paper are a characterization of conflicts, completeness and uniqueness of essential critical pairs and a local confluence lemma based on essential critical pairs. The theory of essential critical pairs is the basis to develop and implement a more efficient conflict detection algorithm in the near future.

Problems with using MPI 1.1 and 2.0 as compilation targets for parallel language implementations

MPI support is nearly ubiquitous on high-performance systems today and is generally highly tuned for performance. It would thus seem to offer a convenient 'portable network assembly language' to developers of parallel programming languages who wish to target different network architectures. Unfortunately, neither the traditional MPI 1.1 API nor the newer MPI 2.0 extensions for one-sided communication provide an adequate compilation target for global address space languages, and this is likely to be the case for many other parallel languages as well. Simulating one-sided communication under the MPI 1.1 API is too expensive, while the MPI 2.0 one-sided API imposes a number of significant restrictions on memory access patterns that would need to be incorporated at the language level, as a compiler cannot effectively hide them given current conflict and alias detection algorithms.

Imperfect Automation in Aviation Traffic Alerts: A Review of Conflict Detection Algorithms and Their Implications for Human Factors Research

Automated warning and alert devices such as airborne collision avoidance systems (ACASs) represent a class of automation that is often found to be imperfect. The imperfections can be expressed as the number of false alarms or missed events. Most ACASs are constructed with a bias to prevent misses (which may have catastrophic consequences) and therefore, coupled with a low base-rate of conflict events, create high false alarm rates. In this paper, we review the adequacy of various CDTI warning algorithms that have been proposed and tested in addressing the false alarm issue, and the potential for multiple levels of alerting to mitigate the effects of false alarms on trust and reliance on the CDTI. We suggest new directions for future research, including evaluating the effects of false alarm rates on pilots' use of the CDTI, determining what strategies may enhance pilot tolerance of false alarms, and investigating the use of CDTI in conjunction with air traffic controllers.

Formal verification of conflict detection algorithms

Safety assessment of new air traffic management systems is a main issue for civil aviation authorities. Standard techniques such as testing and simulation have serious limitations in new systems that are significantly more autonomous than the older ones. In this paper, we present an innovative approach for establishing the correctness of conflict detection systems. Fundamental to our approach is the concept of trajectory, and how we represent a continuous physical trajectory by a continuous path in the x-y plane constrained by physical laws and operational requirements. From the model of trajectories, we extract, and formally prove, high-level properties that can serve as a framework to analyze conflict scenarios. We use the AILS (Airborne Information for Lateral Spacing) alerting algorithm as a case study of our approach.

Traffic Complexity Based Conflict Resolution

This paper presents a new approach to the development of Conflict Detection and Resolution (CDR) algorithms for a Free Flight environment. Most of the recently proposed CDR algorithms typically attempt to resolve impending conflicts while minimizing trajectory deviations, fuel usage, flight time, or some other “individually-oriented” criterion. However, in this research a newly developed CDR algorithm, the “Minimised number of Aircraft Solve Conflicts” (MASC) algorithm, is described that aims to resolve conflicts from a “collective” optimisation perspective. More specifically, MASC aims at reducing the local traffic complexity in conflict situations in a Free Flight sector. In this study, the results of simulations of several conflict scenarios are discussed and on the basis of so-called Dynamic Density computations, the MASC algorithm is compared with three currently existing CDR algorithms. The computed Dynamic Density time-histories are used as an indication of the local traffic complexity. The developed MASC algorithm shows considerably lower Dynamic Density values than the other three CDR algorithms.

Fast and scalable conflict detection for packet classifiers

Packet filters provide rules for classifying packets based on header fields. High speed packet classification has received much study. However, the twin problems of fast updates and fast conflict detection have not received much attention. A conflict occurs when two classifiers overlap, potentially creating ambiguity for packets that match both filters. For example, if Rule 1 specifies that all packets going to CNN be rate controlled and Rule 2 specifies that all packets coming from Walmart be given high priority, the rules conflict for traffic from Walmart to CNN. There has been prior work on efficient conflict detection for two-dimensional classifiers. However, the best known algorithm for conflict detection for general classifiers is the naive O( N 2) algorithm of comparing each pair of rules for a conflict. In this paper, we describe an efficient and scalable conflict detection algorithm for the general case that is significantly faster. For example, for a database of 20 000 rules, our algorithm is 40 times faster than the naive implementation. Even without considering conflicts, our algorithm also provides a packet classifier with fast updates and fast lookups that can be used for stateful packet filtering.

The integration of relationship instances from heterogeneous databases

In the process of integrating legacy databases, one has to resolve inter-database conflicts at both the schema and instance levels. In this paper, we discuss relationship conflicts as a special type of conflicts to be resolved during the database integration. Relationships are properties that relate real world objects. So far, most inter-database relationship conflicts are addressed at the schema-level by various schema integration techniques. However, instance-level relationship conflicts are largely neglected. This paper therefore investigates the causes of instance-level relationship conflicts and proposes a taxonomy for classifying instance-level relationship conflicts. In addition, we develop a systematic process to resolve instance-level relationship conflicts and incorporate the resolution steps into the overall database integration process. Instance-level relationship conflict detection algorithms have also been developed to aid the resolution process. This research should facilitate database integration work for both multidatabase and data warehousing approaches. Most importantly, it should improve the data quality of the integrated databases.