OIL SPILL RESPONSE AND PREPAREDNESS SYSTEM BASED ON CASE - BASED REASONING - DEMONSTRATED USING A HYPOTHETICAL CASE

2013 ◽  
Vol 12 (12) ◽  
pp. 2489-2500 ◽  
Author(s):  
Zhenliang Liao ◽  
Yanhui Liu ◽  
Zuxin Xu
Keyword(s):  
Oil Spill ◽  
Case Based Reasoning ◽  
Hypothetical Case ◽  
Oil Spill Response ◽  
Case Based

scholarly journals Scenario-Based Marine Oil Spill Emergency Response Using Hybrid Deep Reinforcement Learning and Case-Based Reasoning

2020 ◽  
Vol 10 (15) ◽  
pp. 5269
Author(s):  
Kui Huang ◽  
Wen Nie ◽  
Nianxue Luo
Keyword(s):  
Reinforcement Learning ◽  
Emergency Response ◽  
Oil Spill ◽  
Expert Knowledge ◽  
Decision Makers ◽  
Case Based Reasoning ◽  
Marine Oil ◽  
Knowledge Based ◽  
Challenges And Opportunities ◽  
Case Based

Case-based reasoning (CBR) systems often provide a basis for decision makers to make management decisions in disaster prevention and emergency response. For decades, many CBR systems have been implemented by using expert knowledge schemes to build indexes for case identification from a case library of situations and to explore the relations among cases. However, a knowledge elicitation bottleneck occurs for many knowledge-based CBR applications because expert reasoning is difficult to precisely explain. To solve these problems, this paper proposes a method using only knowledge to recognize marine oil spill cases. The proposed method combines deep reinforcement learning (DRL) with strategy selection to determine emergency responses for marine oil spill accidents by quantification of the marine oil spill scenario as the reward for the DRL agent. These accidents are described by scenarios and are considered the state inputs in the hybrid DRL/CBR framework. The challenges and opportunities of the proposed method are discussed considering different scenarios and the intentions of decision makers. This approach may be helpful in terms of developing hybrid DRL/CBR-based tools for marine oil spill emergency response.

Obama Beefs Up Oil Spill Response

2010 ◽  
pp. 10052710172048
Author(s):  
Jeff Johnson ◽  
Michael Torrice ◽  
Melody Voith
Keyword(s):  
Oil Spill ◽  
Oil Spill Response

Comparative Analysis of Oil Spill Response Methods in the Arctic Seas

2020 ◽  
pp. 18-26
Author(s):  
A.A. Gorbunov ◽  
◽  
S.I. Shepelyuk ◽  
A.G. Nesterenko ◽  
K.I. Drapey ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Oil Spill ◽  
The Arctic ◽  
Arctic Seas ◽  
Oil Spill Response

Solving the Message Classification Problem in Voice Interaction Systems

Vestnik MEI ◽  
2020 ◽  
Vol 5 (5) ◽  
pp. 132-139
Author(s):  
Ivan E. Kurilenko ◽  
◽  
Igor E. Nikonov ◽  
Keyword(s):  
Artificial Intelligence ◽  
Classification Problem ◽  
Subject Area ◽  
Text Messages ◽  
Case Based Reasoning ◽  
Short Text ◽  
Proposed Modification ◽  
Voice Interaction ◽  
Text Content ◽  
Case Based

A method for solving the problem of classifying short-text messages in the form of sentences of customers uttered in talking via the telephone line of organizations is considered. To solve this problem, a classifier was developed, which is based on using a combination of two methods: a description of the subject area in the form of a hierarchy of entities and plausible reasoning based on the case-based reasoning approach, which is actively used in artificial intelligence systems. In solving various problems of artificial intelligence-based analysis of data, these methods have shown a high degree of efficiency, scalability, and independence from data structure. As part of using the case-based reasoning approach in the classifier, it is proposed to modify the TF-IDF (Term Frequency - Inverse Document Frequency) measure of assessing the text content taking into account known information about the distribution of documents by topics. The proposed modification makes it possible to improve the classification quality in comparison with classical measures, since it takes into account the information about the distribution of words not only in a separate document or topic, but in the entire database of cases. Experimental results are presented that confirm the effectiveness of the proposed metric and the developed classifier as applied to classification of customer sentences and providing them with the necessary information depending on the classification result. The developed text classification service prototype is used as part of the voice interaction module with the user in the objective of robotizing the telephone call routing system and making a shift from interaction between the user and system by means of buttons to their interaction through voice.

Feature level intentions based product recommendations with case-based reasoning

2018 ◽  
Vol 6 (1) ◽  
pp. 266-274
Author(s):  
D. Teja Santosh ◽  
◽  
K.C. Ravi Kumar ◽  
P. Chiranjeevi ◽  
◽  
...  
Keyword(s):  
Case Based Reasoning ◽  
Product Recommendations ◽  
Case Based
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