Pub Date : 2017-03-01DOI: 10.1109/COGSIMA.2017.7929611
L. Reinerman-Jones, G. Matthews, R. Wohleber, Eric Ortiz
An important topic in cybersecurity is validating Active Indicators (AI), which are stimuli that can be implemented in systems to trigger responses from individuals who might or might not be Insider Threats (ITs). The way in which a person responds to the AI is being validated for identifying a potential threat and a non-threat. In order to execute this validation process, it is important to create a paradigm that allows manipulation of AIs for measuring response. The scenarios are posed in a manner that require participants to be situationally aware that they are being monitored and have to act deceptively. In particular, manipulations in the environment should no differences between conditions relative to immersion and ease of use, but the narrative should be the driving force behind non-deceptive and IT responses. The success of the narrative and the simulation environment to induce such behaviors is determined by immersion, usability, and stress response questionnaires, and performance. Initial results of the feasibility to use a narrative reliant upon situation awareness of monitoring and evasion are discussed.
{"title":"Scenarios using situation awareness in a simulation environment for eliciting insider threat behavior","authors":"L. Reinerman-Jones, G. Matthews, R. Wohleber, Eric Ortiz","doi":"10.1109/COGSIMA.2017.7929611","DOIUrl":"https://doi.org/10.1109/COGSIMA.2017.7929611","url":null,"abstract":"An important topic in cybersecurity is validating Active Indicators (AI), which are stimuli that can be implemented in systems to trigger responses from individuals who might or might not be Insider Threats (ITs). The way in which a person responds to the AI is being validated for identifying a potential threat and a non-threat. In order to execute this validation process, it is important to create a paradigm that allows manipulation of AIs for measuring response. The scenarios are posed in a manner that require participants to be situationally aware that they are being monitored and have to act deceptively. In particular, manipulations in the environment should no differences between conditions relative to immersion and ease of use, but the narrative should be the driving force behind non-deceptive and IT responses. The success of the narrative and the simulation environment to induce such behaviors is determined by immersion, usability, and stress response questionnaires, and performance. Initial results of the feasibility to use a narrative reliant upon situation awareness of monitoring and evasion are discussed.","PeriodicalId":252066,"journal":{"name":"2017 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125127188","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 : 2017-03-01DOI: 10.1109/COGSIMA.2017.7929600
Jennifer Danczyk, Paula Jacobs, O. Montgomery, Michael P. Jenkins, Michael Farry
Military facility managers must track repeated contaminant release that occurs from scheduled training exercises to mitigate the effects of those releases before negative effects occur. Training facility managers are tasked with analyzing the accrual of contamination to their facility grounds, understanding the potential for contaminant transport, and planning future mitigation to remove documented contamination. To provide greater awareness to facility managers of the complex contaminant behavior and effects to the environment, we have developed a decision support system (DSS) that assists facility managers with both tracking the environment quality and contaminant accrual and allows them to select proper mitigation exercises. During the design phase of our DSS, we conducted a usability test to identify breakdowns within the DSS's design and workflow to direct the future design and capabilities of the application. Our informal usability test consisted of creating a hypothetical use-case backed by a realistic scenario, tasks for our participants to complete, and implementing our static design mockups into an interactive, high-fidelity prototyping environment to simulate the intended functionality of the software. Participants consisted of a mixture of internal company employees including several software usability experts. The results of our usability test showed a mixture of low-level and high-level opportunities for design enhancements regarding the layout and organization of information included within individual tools and capabilities. We have made revisions on the design and plan to conduct additional usability tests with active duty and/or civilian facility managers to further enhance the usability and usefulness of this DSS application.
{"title":"Testing the usability of a decision support system for increasing environmental awareness","authors":"Jennifer Danczyk, Paula Jacobs, O. Montgomery, Michael P. Jenkins, Michael Farry","doi":"10.1109/COGSIMA.2017.7929600","DOIUrl":"https://doi.org/10.1109/COGSIMA.2017.7929600","url":null,"abstract":"Military facility managers must track repeated contaminant release that occurs from scheduled training exercises to mitigate the effects of those releases before negative effects occur. Training facility managers are tasked with analyzing the accrual of contamination to their facility grounds, understanding the potential for contaminant transport, and planning future mitigation to remove documented contamination. To provide greater awareness to facility managers of the complex contaminant behavior and effects to the environment, we have developed a decision support system (DSS) that assists facility managers with both tracking the environment quality and contaminant accrual and allows them to select proper mitigation exercises. During the design phase of our DSS, we conducted a usability test to identify breakdowns within the DSS's design and workflow to direct the future design and capabilities of the application. Our informal usability test consisted of creating a hypothetical use-case backed by a realistic scenario, tasks for our participants to complete, and implementing our static design mockups into an interactive, high-fidelity prototyping environment to simulate the intended functionality of the software. Participants consisted of a mixture of internal company employees including several software usability experts. The results of our usability test showed a mixture of low-level and high-level opportunities for design enhancements regarding the layout and organization of information included within individual tools and capabilities. We have made revisions on the design and plan to conduct additional usability tests with active duty and/or civilian facility managers to further enhance the usability and usefulness of this DSS application.","PeriodicalId":252066,"journal":{"name":"2017 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114444327","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 : 2017-03-01DOI: 10.1109/COGSIMA.2017.7929577
G. Jakobson
The emergence of a new class of complex applications in bio-medical and health-care systems, intelligent transportation, disaster situation management systems and others, has defined new requirements to the methods of control of these systems. Central to those applications is the requirement to understand the meaning of complex situations happening in dynamic environments, and to act based upon those situations so that certain goal situations will be reached. Often actions of situation control face hardly definable goal situations and lack of control optimality. Although the importance of theories such as situation awareness has been well recognized, we are still away from a broadly accepted understanding of the mechanisms of situation control. We argue that augmenting situation control with capabilities exhibited by human cognition provides more effective mechanisms for organizing goal-directed behavior of complex systems. The paper presents conceptual framework of cognitive situation control and discusses details of the main components of the proposed architecture, including situation recognition, negative situation control feedback, and action planning.
{"title":"A framework for cognitive situation control","authors":"G. Jakobson","doi":"10.1109/COGSIMA.2017.7929577","DOIUrl":"https://doi.org/10.1109/COGSIMA.2017.7929577","url":null,"abstract":"The emergence of a new class of complex applications in bio-medical and health-care systems, intelligent transportation, disaster situation management systems and others, has defined new requirements to the methods of control of these systems. Central to those applications is the requirement to understand the meaning of complex situations happening in dynamic environments, and to act based upon those situations so that certain goal situations will be reached. Often actions of situation control face hardly definable goal situations and lack of control optimality. Although the importance of theories such as situation awareness has been well recognized, we are still away from a broadly accepted understanding of the mechanisms of situation control. We argue that augmenting situation control with capabilities exhibited by human cognition provides more effective mechanisms for organizing goal-directed behavior of complex systems. The paper presents conceptual framework of cognitive situation control and discusses details of the main components of the proposed architecture, including situation recognition, negative situation control feedback, and action planning.","PeriodicalId":252066,"journal":{"name":"2017 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133709641","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 : 2017-03-01DOI: 10.1109/COGSIMA.2017.7929607
R. Gutzwiller, J. Reeder
Unmanned systems are increasing in number, while their manning requirements remain the same. To decrease manpower demands, machine learning techniques and autonomy are gaining traction and visibility. One barrier is human perception and understanding of autonomy. Machine learning techniques can result in “black box” algorithms that may yield high fitness, but poor comprehension by operators. However, Interactive Machine Learning (IML), a method to incorporate human input over the course of algorithm development by using neuro-evolutionary machine-learning techniques, may offer a solution. IML is evaluated here for its impact on developing autonomous team behaviors in an area search task. Initial findings show that IML-generated search plans were chosen over plans generated using a non-interactive ML technique, even though the participants trusted them slightly less. Further, participants discriminated each of the two types of plans from each other with a high degree of accuracy, suggesting the IML approach imparts behavioral characteristics into algorithms, making them more recognizable. Together the results lay the foundation for exploring how to team humans successfully with ML behavior.
{"title":"Human interactive machine learning for trust in teams of autonomous robots","authors":"R. Gutzwiller, J. Reeder","doi":"10.1109/COGSIMA.2017.7929607","DOIUrl":"https://doi.org/10.1109/COGSIMA.2017.7929607","url":null,"abstract":"Unmanned systems are increasing in number, while their manning requirements remain the same. To decrease manpower demands, machine learning techniques and autonomy are gaining traction and visibility. One barrier is human perception and understanding of autonomy. Machine learning techniques can result in “black box” algorithms that may yield high fitness, but poor comprehension by operators. However, Interactive Machine Learning (IML), a method to incorporate human input over the course of algorithm development by using neuro-evolutionary machine-learning techniques, may offer a solution. IML is evaluated here for its impact on developing autonomous team behaviors in an area search task. Initial findings show that IML-generated search plans were chosen over plans generated using a non-interactive ML technique, even though the participants trusted them slightly less. Further, participants discriminated each of the two types of plans from each other with a high degree of accuracy, suggesting the IML approach imparts behavioral characteristics into algorithms, making them more recognizable. Together the results lay the foundation for exploring how to team humans successfully with ML behavior.","PeriodicalId":252066,"journal":{"name":"2017 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114756668","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 : 2017-03-01DOI: 10.1109/COGSIMA.2017.7929587
R. Ilin, G. Rogova
Research in progress described in this paper addresses the problem of decision making in situations involving low probability high consequence events. The traditional Expected Utility Model (EU) has significant limitations in such circumstances as documented in multiple research results. The models discussed in this paper is an adaptation of the Multiple Quantile Model (MQT) representing a rational decision support scheme suited to regular as well as low probability high consequence events to the complex dynamic scenarios, in which decision making has to be based on highly uncertain, often unreliable heterogeneous data and information. The core of this scheme is a combination of the Multiple Quantile Theory with the Transferable Belief Model (TBM) and Anytime Decision making. An example of this approach with numeric simulations is given and the directions of future work are outlined.
{"title":"Decision-making involving low probability high consequence events under risk and uncertainty","authors":"R. Ilin, G. Rogova","doi":"10.1109/COGSIMA.2017.7929587","DOIUrl":"https://doi.org/10.1109/COGSIMA.2017.7929587","url":null,"abstract":"Research in progress described in this paper addresses the problem of decision making in situations involving low probability high consequence events. The traditional Expected Utility Model (EU) has significant limitations in such circumstances as documented in multiple research results. The models discussed in this paper is an adaptation of the Multiple Quantile Model (MQT) representing a rational decision support scheme suited to regular as well as low probability high consequence events to the complex dynamic scenarios, in which decision making has to be based on highly uncertain, often unreliable heterogeneous data and information. The core of this scheme is a combination of the Multiple Quantile Theory with the Transferable Belief Model (TBM) and Anytime Decision making. An example of this approach with numeric simulations is given and the directions of future work are outlined.","PeriodicalId":252066,"journal":{"name":"2017 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124111384","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 : 2017-03-01DOI: 10.1109/COGSIMA.2017.7929604
K. Mykoniatis, A. Angelopoulou
This paper describes the system dynamics architecture of UTASiMo, a simulation-based task analysis tool that simulates the outcomes of task analysis for a system design and estimates task execution times, workload, and human error probability. UTASiMo combines discrete event, agent-based, and system dynamics simulation methods to automatically construct and simulate models that correspond to different scenarios to test prospective human system designs. Here, we focus on the system dynamics model, which captures the causal relationships of factors affecting human error and uses them to assess the overall human error probability of the simulated system (SimHEP). This SimHEP provides a quantitative basis to the simulated human system's evaluation. The present work is a continuation of our previous work on UTASiMo and aims to introduce system dynamics simulation as a potential method to assess human reliability.
{"title":"The system dynamics architecture of UTASiMo: A simulation-based task analysis tool to predict human error probability","authors":"K. Mykoniatis, A. Angelopoulou","doi":"10.1109/COGSIMA.2017.7929604","DOIUrl":"https://doi.org/10.1109/COGSIMA.2017.7929604","url":null,"abstract":"This paper describes the system dynamics architecture of UTASiMo, a simulation-based task analysis tool that simulates the outcomes of task analysis for a system design and estimates task execution times, workload, and human error probability. UTASiMo combines discrete event, agent-based, and system dynamics simulation methods to automatically construct and simulate models that correspond to different scenarios to test prospective human system designs. Here, we focus on the system dynamics model, which captures the causal relationships of factors affecting human error and uses them to assess the overall human error probability of the simulated system (SimHEP). This SimHEP provides a quantitative basis to the simulated human system's evaluation. The present work is a continuation of our previous work on UTASiMo and aims to introduce system dynamics simulation as a potential method to assess human reliability.","PeriodicalId":252066,"journal":{"name":"2017 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125231235","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 : 2017-03-01DOI: 10.1109/COGSIMA.2017.7929584
Noah Lesch, Andrew Compton, John M. Pecarina, M. Ippolito, D. Hodson
Vision is vital to decision making, as humans naturally trust their eyes to enhance situation awareness. Yet the modern age has overwhelmed humans with massive amounts of visual information, which is problematic in time sensitive and mission critical situations, such as emergency management and disaster response. More efficient search and retrieval systems address some of these issues, which is why many seek to develop and extend Content Based Image Retrieval (CBIR) techniques to support situational awareness in a more autonomous fashion. However, there is currently no adequate system for CBIR to support situational awareness in dynamic and sensor rich environments. This research proposes an extensible framework for CBIR to support a holistic understanding of the environment through the automated search and retrieval of relevant images and the context of their capture. This constitutes assisted CBIR as embodied in the multi-sensor assisted CBIR system (MSACS). We design the MSACS framework and implement the core CBIR system of MSACS using the state of the art Bag of Visual Words paradigm. The system is evaluated using a dataset of GPS tagged images to show favorable precision and recall of spatially related images. Applications for localization and search for Wi-Fi access points demonstrate improved situational awareness using the system. Assisted CBIR could enable vision based understanding of an environment to ease the burdens of information overload and increase human confidence in autonomous systems.
{"title":"Image retrieval for visual understanding in dynamic and sensor rich environments","authors":"Noah Lesch, Andrew Compton, John M. Pecarina, M. Ippolito, D. Hodson","doi":"10.1109/COGSIMA.2017.7929584","DOIUrl":"https://doi.org/10.1109/COGSIMA.2017.7929584","url":null,"abstract":"Vision is vital to decision making, as humans naturally trust their eyes to enhance situation awareness. Yet the modern age has overwhelmed humans with massive amounts of visual information, which is problematic in time sensitive and mission critical situations, such as emergency management and disaster response. More efficient search and retrieval systems address some of these issues, which is why many seek to develop and extend Content Based Image Retrieval (CBIR) techniques to support situational awareness in a more autonomous fashion. However, there is currently no adequate system for CBIR to support situational awareness in dynamic and sensor rich environments. This research proposes an extensible framework for CBIR to support a holistic understanding of the environment through the automated search and retrieval of relevant images and the context of their capture. This constitutes assisted CBIR as embodied in the multi-sensor assisted CBIR system (MSACS). We design the MSACS framework and implement the core CBIR system of MSACS using the state of the art Bag of Visual Words paradigm. The system is evaluated using a dataset of GPS tagged images to show favorable precision and recall of spatially related images. Applications for localization and search for Wi-Fi access points demonstrate improved situational awareness using the system. Assisted CBIR could enable vision based understanding of an environment to ease the burdens of information overload and increase human confidence in autonomous systems.","PeriodicalId":252066,"journal":{"name":"2017 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122039050","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 : 2017-03-01DOI: 10.1109/COGSIMA.2017.7929593
K. Gross, K. Baclawski, Eric S. Chan, D. Gawlick, Adel Ghoneimy, Z. Liu
This paper presents a novel tandem human-machine cognition approach for human-in-the-loop control of complex business-critical and mission-critical systems and processes that are monitored by Internet-of-Things (IoT) sensor networks and where it is of utmost importance to mitigate and avoid cognitive overload situations for the human operators. The approach is based on a decision making supervisory loop for situation awareness and control combined with a machine learning technique that is especially well suited to this control problem. The goal is to achieve a number of functional requirements: (1) ultra-low false alarm probabilities for all monitored transducers, components, machines, systems, and processes; (2) fastest mathematically possible decisions regarding the incipience or onset of anomalies in noisy process metrics; and (3) the ability to unambiguously differentiate between sensor degradation events and degradation in the systems/processes under surveillance. The novel approach that is presented here does not replace the role of the human in operation of complex engineering systems and processes, but rather augments that role in a manner that minimizes cognitive overload by very rapidly processing, interpreting, and displaying final diagnostic and prognostic information to the human operator in a prioritized format that is readily perceived and comprehended.
{"title":"A supervisory control loop with Prognostics for human-in-the-loop decision support and control applications","authors":"K. Gross, K. Baclawski, Eric S. Chan, D. Gawlick, Adel Ghoneimy, Z. Liu","doi":"10.1109/COGSIMA.2017.7929593","DOIUrl":"https://doi.org/10.1109/COGSIMA.2017.7929593","url":null,"abstract":"This paper presents a novel tandem human-machine cognition approach for human-in-the-loop control of complex business-critical and mission-critical systems and processes that are monitored by Internet-of-Things (IoT) sensor networks and where it is of utmost importance to mitigate and avoid cognitive overload situations for the human operators. The approach is based on a decision making supervisory loop for situation awareness and control combined with a machine learning technique that is especially well suited to this control problem. The goal is to achieve a number of functional requirements: (1) ultra-low false alarm probabilities for all monitored transducers, components, machines, systems, and processes; (2) fastest mathematically possible decisions regarding the incipience or onset of anomalies in noisy process metrics; and (3) the ability to unambiguously differentiate between sensor degradation events and degradation in the systems/processes under surveillance. The novel approach that is presented here does not replace the role of the human in operation of complex engineering systems and processes, but rather augments that role in a manner that minimizes cognitive overload by very rapidly processing, interpreting, and displaying final diagnostic and prognostic information to the human operator in a prioritized format that is readily perceived and comprehended.","PeriodicalId":252066,"journal":{"name":"2017 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)","volume":"168 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122044144","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 : 2017-03-01DOI: 10.1109/COGSIMA.2017.7929597
George W. Clark, M. V. Doran, T. Andel
Cybersecurity is not highly prioritized during the design and manufacture of robots. As with other embedded systems a higher priority is placed on development costs and delivering functionality to consumers. In the future greater attention to cybersecurity will need to be given as the use of robots continues to grow in the manufacturing, military, medical, eldercare and the automated vehicle markets. This work identifies current and potential cyber threats to robotics at the hardware, firmware/OS, and application levels. Attack scenarios at each level are presented and discussed. Additionally, the economic and human safety impact of a cyber attack on robots is examined. Finally, possible countermeasures are suggested.
{"title":"Cybersecurity issues in robotics","authors":"George W. Clark, M. V. Doran, T. Andel","doi":"10.1109/COGSIMA.2017.7929597","DOIUrl":"https://doi.org/10.1109/COGSIMA.2017.7929597","url":null,"abstract":"Cybersecurity is not highly prioritized during the design and manufacture of robots. As with other embedded systems a higher priority is placed on development costs and delivering functionality to consumers. In the future greater attention to cybersecurity will need to be given as the use of robots continues to grow in the manufacturing, military, medical, eldercare and the automated vehicle markets. This work identifies current and potential cyber threats to robotics at the hardware, firmware/OS, and application levels. Attack scenarios at each level are presented and discussed. Additionally, the economic and human safety impact of a cyber attack on robots is examined. Finally, possible countermeasures are suggested.","PeriodicalId":252066,"journal":{"name":"2017 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132739488","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 : 2017-03-01DOI: 10.1109/COGSIMA.2017.7929586
K. Baclawski, Eric S. Chan, D. Gawlick, Adel Ghoneimy, K. Gross, Z. Liu
Decision making is important for many systems and is fundamental for situation awareness and information fusion. When a decision making process is confronted with new situations, goals and kinds of data, it must evolve and adapt. Highly optimized processes and efficient data structures generally have the disadvantage of having little flexibility or adaptability when confronted with new forms of data and new or changing goals. Consequently, optimized processes may only be locally optimal and may deteriorate over time. The normal approach to changing conditions is to manually reconfigure and even redevelop the system, which can be costly and time-consuming. In this article. we propose an architecture for the self-adaptation of decision making processes using flexible data structures and a process that monitors and adapts the decision making process. The objective is to have the ability to adapt both data schemas and decision making processes so that they can be both responsive and efficient.
{"title":"Self-adaptive dynamic decision making processes","authors":"K. Baclawski, Eric S. Chan, D. Gawlick, Adel Ghoneimy, K. Gross, Z. Liu","doi":"10.1109/COGSIMA.2017.7929586","DOIUrl":"https://doi.org/10.1109/COGSIMA.2017.7929586","url":null,"abstract":"Decision making is important for many systems and is fundamental for situation awareness and information fusion. When a decision making process is confronted with new situations, goals and kinds of data, it must evolve and adapt. Highly optimized processes and efficient data structures generally have the disadvantage of having little flexibility or adaptability when confronted with new forms of data and new or changing goals. Consequently, optimized processes may only be locally optimal and may deteriorate over time. The normal approach to changing conditions is to manually reconfigure and even redevelop the system, which can be costly and time-consuming. In this article. we propose an architecture for the self-adaptation of decision making processes using flexible data structures and a process that monitors and adapts the decision making process. The objective is to have the ability to adapt both data schemas and decision making processes so that they can be both responsive and efficient.","PeriodicalId":252066,"journal":{"name":"2017 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133347074","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}
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