Pub Date : 2015-03-09DOI: 10.1109/COGSIMA.2015.7108182
Giuseppe D’aniello, M. Gaeta, Antonio Granito, F. Orciuoli, V. Loia
This paper faces the problem of increasing the awareness of learners, with respect to their whole learning processes, in order to sustain their capabilities to adapt such processes. The idea is to exploit models and approaches for Situation Awareness, previously adopted in other fields, also in the human learning domain by defining a framework that can be instantiated in a wide range of seamless learning scenarios. Being aware of the learning situations in which they are, learners can make decisions to adapt their behaviours and self-regulate their processes. More specifically, the approach is able to identify learning path types by exploiting the metaphor of bubbles, which represent sets of concepts already acquired by learners. It is possible to identify the situations in which learners are involved by taking into account the way in which such bubbles arise, grow and join together. Lastly, this work also provides a description and an early evaluation of the developed software prototype.
{"title":"Sustaining self-regulation processes in seamless learning scenarios by situation awareness","authors":"Giuseppe D’aniello, M. Gaeta, Antonio Granito, F. Orciuoli, V. Loia","doi":"10.1109/COGSIMA.2015.7108182","DOIUrl":"https://doi.org/10.1109/COGSIMA.2015.7108182","url":null,"abstract":"This paper faces the problem of increasing the awareness of learners, with respect to their whole learning processes, in order to sustain their capabilities to adapt such processes. The idea is to exploit models and approaches for Situation Awareness, previously adopted in other fields, also in the human learning domain by defining a framework that can be instantiated in a wide range of seamless learning scenarios. Being aware of the learning situations in which they are, learners can make decisions to adapt their behaviours and self-regulate their processes. More specifically, the approach is able to identify learning path types by exploiting the metaphor of bubbles, which represent sets of concepts already acquired by learners. It is possible to identify the situations in which learners are involved by taking into account the way in which such bubbles arise, grow and join together. Lastly, this work also provides a description and an early evaluation of the developed software prototype.","PeriodicalId":373467,"journal":{"name":"2015 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121937642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-03-09DOI: 10.1109/COGSIMA.2015.7108200
Michael P. Jenkins, Chris Hogan, Ryan M. Kilgore
We present two critical elements from the design of a cockpit heads-down display symbology to support safe and efficient shipboard landings of rotorcraft in degraded visual environments. The symbology applies ecological interface design (EID) principles to support pilots' direct perception of critical vehicle operating characteristics within the limitations of safety and performance constraints (while remaining viable in daylight, nighttime, and night-vision compatible viewing contexts). Our approach combines precision, ship-relative navigation (PS-RN) information with automated flight director cues in an integrated heads-down display designed for cockpit multifunction displays to help pilots perceive, understand, and respond to dynamic landing situations.
{"title":"Ecological display symbology to support pilot situational awareness during shipboard operations","authors":"Michael P. Jenkins, Chris Hogan, Ryan M. Kilgore","doi":"10.1109/COGSIMA.2015.7108200","DOIUrl":"https://doi.org/10.1109/COGSIMA.2015.7108200","url":null,"abstract":"We present two critical elements from the design of a cockpit heads-down display symbology to support safe and efficient shipboard landings of rotorcraft in degraded visual environments. The symbology applies ecological interface design (EID) principles to support pilots' direct perception of critical vehicle operating characteristics within the limitations of safety and performance constraints (while remaining viable in daylight, nighttime, and night-vision compatible viewing contexts). Our approach combines precision, ship-relative navigation (PS-RN) information with automated flight director cues in an integrated heads-down display designed for cockpit multifunction displays to help pilots perceive, understand, and respond to dynamic landing situations.","PeriodicalId":373467,"journal":{"name":"2015 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126059645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-03-09DOI: 10.1109/COGSIMA.2015.7108187
Kristin E. Schaefer, Daniel N. Cassenti
The vision for future Soldier-robot relationships has supported the transition of the robot's role from a tool to an integrated team member. This vision has provided support for the advancement of robot autonomy and intelligence as a means to better support action and cognitive decision-making in the network-centric operational environment. To accomplish this goal, the Soldier's perspective of the human-robot interaction must be further developed, as it directly impacts overall situation management: mission planning, operational roles, function allocation, and decision-making. Here we present a theoretical concept paper that promotes using the foundation of network science to better understand how and why advances in effective Soldier-robot situation management may be realized. We begin by providing a primer on how a network science approach may be used to understand multi-agent teams and network-centric operations. This is followed with a review on the impact of human perception on the human-robot team network structure. Two key points are highlighted. First, the network structure is influenced by the extent to which a Soldier-robot coupling performs independent operations. Second, the degree of automaticity for several properties of the robot specifies the strength of their networked relationship. We conclude with possible advantages of using a network science approach for understanding situation management of Soldier-robot teams in an operational environment. This approach provides a structure for creating visual maps of team structures to understand perceived and anticipated role interdependency, which thus provides the foundation for developing a mathematical description of the dynamic Soldier-robot relationship.
{"title":"A network science approach to future human-robot interaction","authors":"Kristin E. Schaefer, Daniel N. Cassenti","doi":"10.1109/COGSIMA.2015.7108187","DOIUrl":"https://doi.org/10.1109/COGSIMA.2015.7108187","url":null,"abstract":"The vision for future Soldier-robot relationships has supported the transition of the robot's role from a tool to an integrated team member. This vision has provided support for the advancement of robot autonomy and intelligence as a means to better support action and cognitive decision-making in the network-centric operational environment. To accomplish this goal, the Soldier's perspective of the human-robot interaction must be further developed, as it directly impacts overall situation management: mission planning, operational roles, function allocation, and decision-making. Here we present a theoretical concept paper that promotes using the foundation of network science to better understand how and why advances in effective Soldier-robot situation management may be realized. We begin by providing a primer on how a network science approach may be used to understand multi-agent teams and network-centric operations. This is followed with a review on the impact of human perception on the human-robot team network structure. Two key points are highlighted. First, the network structure is influenced by the extent to which a Soldier-robot coupling performs independent operations. Second, the degree of automaticity for several properties of the robot specifies the strength of their networked relationship. We conclude with possible advantages of using a network science approach for understanding situation management of Soldier-robot teams in an operational environment. This approach provides a structure for creating visual maps of team structures to understand perceived and anticipated role interdependency, which thus provides the foundation for developing a mathematical description of the dynamic Soldier-robot relationship.","PeriodicalId":373467,"journal":{"name":"2015 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128311533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-03-09DOI: 10.1109/COGSIMA.2015.7108195
G. Rogova, J. Llinas, Geoff A. Gross
This paper describes a mixed-initiative model of knowledge discovery capable of monitoring a dynamic environment, in which uncertain and unreliable messages can be reasoned over for recognizing human activities and predicting likely threats. The model represents “an argument assistant” helping an analyst in argument production by considering pro and contra arguments from uncertain transient information while seeing each piece of this information as an element of alternative stories (hypotheses based on “what might happen”). These hypotheses are evaluated within the framework of the Transferable Belief Model by assigning beliefs to each argument, combining these beliefs, and selecting a story (hypothesis) based on the highest pignistic probability. Anytime decision making provides decision quality control by weighing time and hypothesis credibility.
{"title":"Belief-based hybrid argumentation for threat assessment","authors":"G. Rogova, J. Llinas, Geoff A. Gross","doi":"10.1109/COGSIMA.2015.7108195","DOIUrl":"https://doi.org/10.1109/COGSIMA.2015.7108195","url":null,"abstract":"This paper describes a mixed-initiative model of knowledge discovery capable of monitoring a dynamic environment, in which uncertain and unreliable messages can be reasoned over for recognizing human activities and predicting likely threats. The model represents “an argument assistant” helping an analyst in argument production by considering pro and contra arguments from uncertain transient information while seeing each piece of this information as an element of alternative stories (hypotheses based on “what might happen”). These hypotheses are evaluated within the framework of the Transferable Belief Model by assigning beliefs to each argument, combining these beliefs, and selecting a story (hypothesis) based on the highest pignistic probability. Anytime decision making provides decision quality control by weighing time and hypothesis credibility.","PeriodicalId":373467,"journal":{"name":"2015 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130797322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-03-09DOI: 10.1109/COGSIMA.2015.7107969
Andrea Salfinger, W. Retschitzegger, W. Schwinger, B. Pröll
Disasters pose severe challenges on emergency responders, who need to appropriately interpret the situational picture and take adequate actions in order to save human lives. Whereas Information Fusion (IF) systems have proven their capability of supporting human operators in rapidly gaining Situation Awareness (SAW) in control center domains, disaster management presents novel challenges: Due to the unpredictability, uniqueness and large-scale dimensions of disasters, their situational pictures typically cannot be extensively captured by sensors - a substantial amount of situational information is delivered by human observers. The ubiquitous availability of social media on mobile devices enables humans to act as crowd sensors, as valuable crisis information can be broadcast over social media channels. Although various systems have been proposed which successfully demonstrate that such crowd-sensed information can be exploited for disaster management, current systems mostly lack means for automated reasoning on these information, as well as an integration with structured data obtained from other sensors. Therefore, in the present work we provide a first attempt towards comprehensively integrating social media-based crowd-sensing in SAWsystems: We contribute an architecture on an adaptive SAW framework exploiting both, traditionally sensed data as well as unstructured social media content, and present our initial solutions based on real-world case studies.
{"title":"CrowdSA — towards adaptive and situation-driven crowd-sensing for disaster situation awareness","authors":"Andrea Salfinger, W. Retschitzegger, W. Schwinger, B. Pröll","doi":"10.1109/COGSIMA.2015.7107969","DOIUrl":"https://doi.org/10.1109/COGSIMA.2015.7107969","url":null,"abstract":"Disasters pose severe challenges on emergency responders, who need to appropriately interpret the situational picture and take adequate actions in order to save human lives. Whereas Information Fusion (IF) systems have proven their capability of supporting human operators in rapidly gaining Situation Awareness (SAW) in control center domains, disaster management presents novel challenges: Due to the unpredictability, uniqueness and large-scale dimensions of disasters, their situational pictures typically cannot be extensively captured by sensors - a substantial amount of situational information is delivered by human observers. The ubiquitous availability of social media on mobile devices enables humans to act as crowd sensors, as valuable crisis information can be broadcast over social media channels. Although various systems have been proposed which successfully demonstrate that such crowd-sensed information can be exploited for disaster management, current systems mostly lack means for automated reasoning on these information, as well as an integration with structured data obtained from other sensors. Therefore, in the present work we provide a first attempt towards comprehensively integrating social media-based crowd-sensing in SAWsystems: We contribute an architecture on an adaptive SAW framework exploiting both, traditionally sensed data as well as unstructured social media content, and present our initial solutions based on real-world case studies.","PeriodicalId":373467,"journal":{"name":"2015 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision","volume":"423 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133515541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-03-09DOI: 10.1109/COGSIMA.2015.7108198
E. Casini, Jessica Depree, Niranjan Suri, J. Bradshaw, Teresa Nieten
Extensive deployment of sensor networks in recent years has led to the generation of large volumes of data. One approach to processing such large volumes of data is to rely on parallelized approaches based on architectures such as MapReduce. However, fully-automated processing without human intervention is error prone. Supporting human involvement in processing pipelines of data in a variety of contexts such as warfare, cyber security, threat monitoring, and malware analysis leads to improved decision-making. Although this kind of human-machine collaboration seems straightforward, involving a human operator into an automated processing pipeline presents some challenges. For example, due to the asynchronous nature of the human intervention, care must be taken to ensure that once a user-made correction or assertion is introduced, all necessary adjustment and reprocessing is performed. In addition, to make the best use of limited resources and processing capabilities, reprocessing of data in light of such corrections must be minimized. This paper introduces an innovative approach for human-machine integration in decisionmaking for large-scale sensor networks that rely on the popular Hadoop MapReduce framework.
{"title":"Enhancing decision-making by leveraging human intervention in large-scale sensor networks","authors":"E. Casini, Jessica Depree, Niranjan Suri, J. Bradshaw, Teresa Nieten","doi":"10.1109/COGSIMA.2015.7108198","DOIUrl":"https://doi.org/10.1109/COGSIMA.2015.7108198","url":null,"abstract":"Extensive deployment of sensor networks in recent years has led to the generation of large volumes of data. One approach to processing such large volumes of data is to rely on parallelized approaches based on architectures such as MapReduce. However, fully-automated processing without human intervention is error prone. Supporting human involvement in processing pipelines of data in a variety of contexts such as warfare, cyber security, threat monitoring, and malware analysis leads to improved decision-making. Although this kind of human-machine collaboration seems straightforward, involving a human operator into an automated processing pipeline presents some challenges. For example, due to the asynchronous nature of the human intervention, care must be taken to ensure that once a user-made correction or assertion is introduced, all necessary adjustment and reprocessing is performed. In addition, to make the best use of limited resources and processing capabilities, reprocessing of data in light of such corrections must be minimized. This paper introduces an innovative approach for human-machine integration in decisionmaking for large-scale sensor networks that rely on the popular Hadoop MapReduce framework.","PeriodicalId":373467,"journal":{"name":"2015 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125709686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-03-09DOI: 10.1109/COGSIMA.2015.7108185
D. Megherbi, Minsuk Kim
Collaborative monitoring of large infrastructures, such as military, transportation and maritime systems are decisive issues in many surveillance, protection, and security applications. In many of these applications, dynamic multi-agent systems using reinforcement learning for agents' autonomous path planning, where agents could be moving randomly to reach their respective goals and avoiding topographical obstacles intelligently, becomes a challenging problem. This is specially so in a dynamic agent environment. In our prior work we presented an intelligent multi-agent hybrid reactive and reinforcement learning technique for collaborative autonomous agent path planning for monitoring Critical Key Infrastructures and Resources (CKIR) in a geographically and a computationally distributed systems. Here agent monitoring of large environments is reduced to monitoring of relatively smaller track-able geographically distributed agent environment regions. In this paper we tackle this problem in the challenging case of complex and cluttered environments, where agents' initial random-walk paths become challenging and relatively nonconverging. Here we propose a multi-agent distributed hybrid reactive re-enforcement learning technique based on selected agent intermediary sub-goals using a learning reward scheme in a distributed-computing memory setting. Various case study scenarios are presented for convergence study to the shortest minimum-amount-of-time exploratory steps for faster and efficient agent learning. In this work the distributed dynamic agent communication is done via a Message Passing Interface (MPI).
{"title":"A collaborative distributed multi-agent reinforcement learning technique for dynamic agent shortest path planning via selected sub-goals in complex cluttered environments","authors":"D. Megherbi, Minsuk Kim","doi":"10.1109/COGSIMA.2015.7108185","DOIUrl":"https://doi.org/10.1109/COGSIMA.2015.7108185","url":null,"abstract":"Collaborative monitoring of large infrastructures, such as military, transportation and maritime systems are decisive issues in many surveillance, protection, and security applications. In many of these applications, dynamic multi-agent systems using reinforcement learning for agents' autonomous path planning, where agents could be moving randomly to reach their respective goals and avoiding topographical obstacles intelligently, becomes a challenging problem. This is specially so in a dynamic agent environment. In our prior work we presented an intelligent multi-agent hybrid reactive and reinforcement learning technique for collaborative autonomous agent path planning for monitoring Critical Key Infrastructures and Resources (CKIR) in a geographically and a computationally distributed systems. Here agent monitoring of large environments is reduced to monitoring of relatively smaller track-able geographically distributed agent environment regions. In this paper we tackle this problem in the challenging case of complex and cluttered environments, where agents' initial random-walk paths become challenging and relatively nonconverging. Here we propose a multi-agent distributed hybrid reactive re-enforcement learning technique based on selected agent intermediary sub-goals using a learning reward scheme in a distributed-computing memory setting. Various case study scenarios are presented for convergence study to the shortest minimum-amount-of-time exploratory steps for faster and efficient agent learning. In this work the distributed dynamic agent communication is done via a Message Passing Interface (MPI).","PeriodicalId":373467,"journal":{"name":"2015 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision","volume":"2018 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122982044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-03-09DOI: 10.1109/COGSIMA.2015.7108181
Risto Vaarandi, Bernhards Blumbergs, E. Çalışkan
During the past two decades, event correlation has emerged as a prominent monitoring technique, and is essential for achieving better situational awareness. Since its introduction in 2001 by one of the authors of this paper, Simple Event Correlator (SEC) has become a widely used open source event correlation tool. During the last decade, a number of papers have been published that describe the use of SEC in various environments. However, recent SEC versions have introduced a number of novel features not discussed in existing works. This paper fills this gap and provides an up-to-date coverage of best practices for creating scalable SEC configurations.
{"title":"Simple event correlator - Best practices for creating scalable configurations","authors":"Risto Vaarandi, Bernhards Blumbergs, E. Çalışkan","doi":"10.1109/COGSIMA.2015.7108181","DOIUrl":"https://doi.org/10.1109/COGSIMA.2015.7108181","url":null,"abstract":"During the past two decades, event correlation has emerged as a prominent monitoring technique, and is essential for achieving better situational awareness. Since its introduction in 2001 by one of the authors of this paper, Simple Event Correlator (SEC) has become a widely used open source event correlation tool. During the last decade, a number of papers have been published that describe the use of SEC in various environments. However, recent SEC versions have introduced a number of novel features not discussed in existing works. This paper fills this gap and provides an up-to-date coverage of best practices for creating scalable SEC configurations.","PeriodicalId":373467,"journal":{"name":"2015 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130294750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-03-09DOI: 10.1109/COGSIMA.2015.7108199
Odd Erik Gundersen
The oil and gas industry is moving towards automating the drilling process, and a lot of research and practical experiments are performed to achieve this. In this paper, the hypothesis “Automation of the drilling process will eliminate the need for online decision support” is investigated. This is an interesting hypothesis to investigate as it is a common hypothesis in the drilling community. The investigation is based on an analysis of the drilling process, literature about situation awareness, decision support and automation in the oil and gas drilling industry. The analysis shows that with increased automation, the situation awareness of the drillers is reduced, and thus the need for decision support systems that can enhance the situation awareness is increased.
{"title":"Decision support in the automated future: an analysis from the rig site","authors":"Odd Erik Gundersen","doi":"10.1109/COGSIMA.2015.7108199","DOIUrl":"https://doi.org/10.1109/COGSIMA.2015.7108199","url":null,"abstract":"The oil and gas industry is moving towards automating the drilling process, and a lot of research and practical experiments are performed to achieve this. In this paper, the hypothesis “Automation of the drilling process will eliminate the need for online decision support” is investigated. This is an interesting hypothesis to investigate as it is a common hypothesis in the drilling community. The investigation is based on an analysis of the drilling process, literature about situation awareness, decision support and automation in the oil and gas drilling industry. The analysis shows that with increased automation, the situation awareness of the drillers is reduced, and thus the need for decision support systems that can enhance the situation awareness is increased.","PeriodicalId":373467,"journal":{"name":"2015 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134180904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-03-09DOI: 10.1109/COGSIMA.2015.7108183
J. Roy, A. B. Guyard
The development of sensemaking support systems requires that one cares about knowledge representation. Motivated by the fact that no single representation method is ideally suited by itself for all tasks, the authors propose a collection of knowledge representation artifacts appropriate for processing in computer-based support systems for situation analysis. The approach described makes it possible to combine the advantages of different representational forms. Each representation paradigm can be matched to an aspect of sensemaking that is a natural fit with this aspect. For example, representing information as propositions is suitable for automated reasoning, while encoding this information using a graph representation enables knowledge discovery through network analytics techniques. The spatial features are a good fit with geospatial reasoning, while situation cases evidently fit well with the case-based reasoning paradigm. These representation artifacts (and a few others) are briefly described in the paper, and some directions for future work are discussed.
{"title":"Knowledge representation artifacts for use in sensemaking support systems","authors":"J. Roy, A. B. Guyard","doi":"10.1109/COGSIMA.2015.7108183","DOIUrl":"https://doi.org/10.1109/COGSIMA.2015.7108183","url":null,"abstract":"The development of sensemaking support systems requires that one cares about knowledge representation. Motivated by the fact that no single representation method is ideally suited by itself for all tasks, the authors propose a collection of knowledge representation artifacts appropriate for processing in computer-based support systems for situation analysis. The approach described makes it possible to combine the advantages of different representational forms. Each representation paradigm can be matched to an aspect of sensemaking that is a natural fit with this aspect. For example, representing information as propositions is suitable for automated reasoning, while encoding this information using a graph representation enables knowledge discovery through network analytics techniques. The spatial features are a good fit with geospatial reasoning, while situation cases evidently fit well with the case-based reasoning paradigm. These representation artifacts (and a few others) are briefly described in the paper, and some directions for future work are discussed.","PeriodicalId":373467,"journal":{"name":"2015 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124841863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：