Identifying route selection strategies in offshore emergency situations using decision trees

Musharraf, Mashrura; Smith, Jennifer; Khan, Faisal; Veitch, Brian

doi:10.1016/j.ress.2018.06.007

Cited by 22 publications

(25 citation statements)

References 13 publications

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“…The preconditions (antecedents of FOL rules) used here are common among experts and have been suggested in earlier studies [8,21,49,56,57,[61][62][63][64][65]. Similar work is reported in [38] where the authors constructed decision trees based on some of the preconditions used in this study, such as the presence of hazard, route direction in PA that actuallyis a byproduct of understanding thePA. Table 3 Joint probability table for possible worlds entails by rule#9 The probability p({HFO,FPA,KMPLA,¬HITR}) = 1/Z represents the probability of a world that is inconsistent with rule#9.…”

Section: An Explanation Of the Fol Rulesmentioning

confidence: 89%

On the realization of the recognition-primed decision model for artificial agents

Danial

Smith

Veitch

et al. 2019

Hum. Cent. Comput. Inf. Sci.

Self Cite

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Naturalistic decision making (NDM) is a relatively new approach to decision-making that relies on situation awareness (SA) [20] rather than having a fixed set of principles from which to choose the best or optimal solution. One of the prominent models of NDM is Klein's [33] recognition-primed decision model. RPDM has a descriptive nature, and it requires a thorough understanding of philosophical concepts, such as intuition, perception, and mental simulation. The purpose of this study is to develop a method based on the theory of RPDM that can be implemented in an artificial agent.

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Section: An Explanation Of the Fol Rulesmentioning

confidence: 89%

On the realization of the recognition-primed decision model for artificial agents

Danial

Smith

Veitch

et al. 2019

Hum. Cent. Comput. Inf. Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The repetition or recursion functions concerning the selection attribute and splitting-set construct internal nodes and a root node. In the pruning-tree phase, some data from the trained sub-dataset may have an inappropriate branch after the DT structure is built, i.e., overfitting, and these inappropriate branches must be eliminated to improve the classification performance [ 48 , 49 ].…”

Section: Related Workmentioning

confidence: 99%

“…In previous studies, the related DT algorithms have typically been applied to functions of the relevant areas of specialization, such as churn prediction [ 51 ], forecasting corporate credit ratings [ 52 ], object classification in autonomous driving [ 53 ], and the identification of a route selection strategy in classification [ 49 ]. Thus, it is an effective method for the selection of medical research.…”

Section: Related Workmentioning

confidence: 99%

Identification of the Framingham Risk Score by an Entropy-Based Rule Model for Cardiovascular Disease

Chen

Cheng

Chen

et al. 2020

Entropy

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Since 2001, cardiovascular disease (CVD) has had the second-highest mortality rate, about 15,700 people per year, in Taiwan. It has thus imposed a substantial burden on medical resources. This study was triggered by the following three factors. First, the CVD problem reflects an urgent issue. A high priority has been placed on long-term therapy and prevention to reduce the wastage of medical resources, particularly in developed countries. Second, from the perspective of preventive medicine, popular data-mining methods have been well learned and studied, with excellent performance in medical fields. Thus, identification of the risk factors of CVD using these popular techniques is a prime concern. Third, the Framingham risk score is a core indicator that can be used to establish an effective prediction model to accurately diagnose CVD. Thus, this study proposes an integrated predictive model to organize five notable classifiers: the rough set (RS), decision tree (DT), random forest (RF), multilayer perceptron (MLP), and support vector machine (SVM), with a novel use of the Framingham risk score for attribute selection (i.e., F-attributes first identified in this study) to determine the key features for identifying CVD. Verification experiments were conducted with three evaluation criteria—accuracy, sensitivity, and specificity—based on 1190 instances of a CVD dataset available from a Taiwan teaching hospital and 2019 examples from a public Framingham dataset. Given the empirical results, the SVM showed the best performance in terms of accuracy (99.67%), sensitivity (99.93%), and specificity (99.71%) in all F-attributes in the CVD dataset compared to the other listed classifiers. The RS showed the highest performance in terms of accuracy (85.11%), sensitivity (86.06%), and specificity (85.19%) in most of the F-attributes in the Framingham dataset. The above study results support novel evidence that no classifier or model is suitable for all practical datasets of medical applications. Thus, identifying an appropriate classifier to address specific medical data is important. Significantly, this study is novel in its calculation and identification of the use of key Framingham risk attributes integrated with the DT technique to produce entropy-based decision rules of knowledge sets, which has not been undertaken in previous research. This study conclusively yielded meaningful entropy-based knowledgeable rules in tree structures and contributed to the differentiation of classifiers from the two datasets with three useful research findings and three helpful management implications for subsequent medical research. In particular, these rules provide reasonable solutions to simplify processes of preventive medicine by standardizing the formats and codes used in medical data to address CVD problems. The specificity of these rules is thus significant compared to those of past research.

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“…2020, 9, 279 4 of 20 models can be extended to macro-or large-scale space models. Therefore, the grid-graph model is preferred, as in [23,24] for simulating the environment model. An office layout usually has a symmetrical geometric design and is easy to implement on a gridgraph.…”

Section: Environment Modelmentioning

confidence: 99%

“…Niyomubyeyi et al [22] compared metaheuristic approaches to the evacuation problem. Musharraf et al [23] used lecture-based training to develop a decision tree for evacuating agents. Golas et al [24] proposed a method for long-range collision avoidance using a multi-agent system with local collision avoidance mechanisms and global path planning.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Behavior Model for Multi-Agent System with Evasion Capabilities and Dynamic Memory

Çetin

Bülbül

2020

IJGI

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The behavior of an agent may be simple or complex depending on its role. Behavioral simulation using agents can have multiple approaches that have different advantages and disadvantages. By combining different behaviors in a hierarchical model, situational inefficiencies can be compensated. This paper proposes a behavioral hierarchy model that combines different mechanisms in behavior plans. The study simulates the social behavior in an office environment during an emergency using collision avoidance, negotiation, conflict solution, and path-planning mechanisms in the same multi-agent model to find their effects and the efficiency of the combinational setups. Independent agents were designed to have memory expansion, pathfinding, and searching capabilities, and the ability to exchange information among themselves and perform evasive actions to find a way out of congestion and conflict. The designed model allows us to modify the behavioral hierarchy and action order of agents during evacuation scenarios. Moreover, each agent behavior can be enabled or disabled separately. The effects of these capabilities on escape performance were measured in terms of time required for evacuation and evacuation ratio. Test results prove that all mechanisms in the proposed model have characteristics that fit each other well in situations where different hierarchies are needed. Dynamic memory management (DMM), together with a hierarchical behavior plan, achieved a performance improvement of 23.14% in escape time without providing agents with any initial environmental information.

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Identifying route selection strategies in offshore emergency situations using decision trees

Cited by 22 publications

References 13 publications

On the realization of the recognition-primed decision model for artificial agents

On the realization of the recognition-primed decision model for artificial agents

Identification of the Framingham Risk Score by an Entropy-Based Rule Model for Cardiovascular Disease

Hierarchical Behavior Model for Multi-Agent System with Evasion Capabilities and Dynamic Memory

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