2018 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)最新文献

Empirically Identified Gaps in a Situation Awareness Model for Human-Machine Coordination 人机协调情境感知模型的经验缺口识别

2018 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA) Pub Date : 2018-06-01 DOI: 10.1109/COGSIMA.2018.8423980

Mary D. Freiman, Michelle Caisse, J. Ball, T. Halverson, Christopher W. Myers

引用次数: 4

2018 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA) Pub Date : 2018-06-01 DOI: 10.1109/cogsima.2018.8423997

引用次数: 0

Operationalized Intent for Communication in Human-Agent Teams 人机代理团队中可操作的沟通意图

2018 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA) Pub Date : 2018-06-01 DOI: 10.1109/COGSIMA.2018.8423992

Michael F. Schneider, Michael E. Miller

{"title":"Operationalized Intent for Communication in Human-Agent Teams","authors":"Michael F. Schneider, Michael E. Miller","doi":"10.1109/COGSIMA.2018.8423992","DOIUrl":"https://doi.org/10.1109/COGSIMA.2018.8423992","url":null,"abstract":"As artificial intelligent agents are employed to increase the cognitive capability of systems leveraged by human machine teams, intra-team communication presents a significant challenge. To address this challenge, we propose an intelligent agent for the express purpose of maintaining a computational representation of human intent for the multi-agent environment. This computational representation, referred to as operationalized intent, seeks to provide a designed shared mental model that can be leveraged by the humans and agents as a shared semantic space. Following the example of high performing human teams, the shared mental model is explicitly trained to both the human operators and the intelligent agents. The model of intent is represented by a hierarchy of goal statements and a summary of constraints. The model is extended by estimating future states of the intent model as part of planning activities. This projection of intent allows the intelligent agents to understand what is important to the human operator now and in the near future. This enhanced context could be used by intelligent agent designers to impart greater responsiveness and anticipatory behavior into a multiple intelligent agent environment, ideally without increasing the human’s workload. The system level implications of enhanced context are laid out in the intent architecture pattern as an aid to system designers. Finally, operationalized intent proposes a direct evaluation method to assess the agent’s interpretation of the human’s intent to evolve the design.","PeriodicalId":231353,"journal":{"name":"2018 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115023997","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}

引用次数: 3

Handling Ambiguous Object Recognition Situations in a Robotic Environment via Dynamic Information Fusion 基于动态信息融合的机器人环境中模糊目标识别问题的处理

2018 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA) Pub Date : 2018-06-01 DOI: 10.1109/COGSIMA.2018.8423982

A. S.PouryaHoseini, M. Nicolescu, M. Nicolescu

{"title":"Handling Ambiguous Object Recognition Situations in a Robotic Environment via Dynamic Information Fusion","authors":"A. S.PouryaHoseini, M. Nicolescu, M. Nicolescu","doi":"10.1109/COGSIMA.2018.8423982","DOIUrl":"https://doi.org/10.1109/COGSIMA.2018.8423982","url":null,"abstract":"Vision is usually a rich source of information for robots aiming to understand activities that take place in their surroundings, where a relevant task can be to detect and recognize objects of interest. In real world conditions a robot may not have a good viewing angle or be sufficiently close to an object to distinguish its features, which can lead to misclassifications. One solution to address this problem is active vision, leading to an improved level of situational awareness in a dynamic environment. In that context, a vision system on the robot actively manipulates the camera to obtain enough discriminating features for the task of object detection and recognition. In this paper, an active vision system is proposed that is able to identify a situation with a high possibility of misclassification (for example, partial occlusions) and then to take appropriate action by dynamically incorporating another camera installed on the robot’s hand. A decision fusion technique based on a transferable belief model generates the final classification results. Experimental results show considerable improvements in object detection and recognition performance.","PeriodicalId":231353,"journal":{"name":"2018 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132277540","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}

引用次数: 3

CogSIMA 2018 Front cover CogSIMA 2018封面

2018 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA) Pub Date : 2018-06-01 DOI: 10.1109/cogsima.2018.8424000

引用次数: 0

Human understanding of information represented in natural versus artificial language (Poster) 人类对自然语言与人工语言所表示的信息的理解(海报)

2018 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA) Pub Date : 2018-06-01 DOI: 10.1109/COGSIMA.2018.8423998

Erin G. Zaroukian, J. Bakdash

{"title":"Human understanding of information represented in natural versus artificial language (Poster)","authors":"Erin G. Zaroukian, J. Bakdash","doi":"10.1109/COGSIMA.2018.8423998","DOIUrl":"https://doi.org/10.1109/COGSIMA.2018.8423998","url":null,"abstract":"In this paper we compare human understanding of information represented in a natural language (NL) to a type of artificial language, called a Controlled Natural Language (CNL). Potential applications for CNLs include decision support and conversational agents, but currently there is limited empirical research on the understandability of CNLs for untrained humans. We investigate a particular type of CNL, called Controlled English (CE), which was designed to be a simplified, artificial subset of natural language that is both human readable and unambiguous for fast and accurate machine processing. We quantify and compare human understanding of NL and CE using accuracy and speed for language statements. The statements described entities (people and objects) and relations (actions) among entities with the ground-truth represented using visual diagrams. Participants responded whether the statements matched the diagram (yes/no). In Experiment I, we found accuracy for NL and CE was comparable, although the speed for understanding CE was slower. To further examine the role of speed, we induced time pressure in Experiment II. We found both the accuracy and speed for CE was lower than NL. These results indicate that if people have sufficient time, understanding for CE can be equivalent to NL. However, with limited time the accuracy and speed for understanding NL is better than CE. Our findings indicate that both accuracy and speed of CNLs should be evaluated. Furthermore, under time pressure there can be meaningful differences in accuracy and speed between different ways of representing information. Understanding for methods of representing machine information has potential implications for situation understanding and management with human-machine interaction and collaboration.","PeriodicalId":231353,"journal":{"name":"2018 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114056635","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}

引用次数: 0

Artificial Swarms find Social Optima : (Late Breaking Report) 人工蜂群找到社会最优点:(最新报道)

2018 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA) Pub Date : 2018-06-01 DOI: 10.1109/COGSIMA.2018.8423987

Louis B. Rosenberg, G. Willcox

{"title":"Artificial Swarms find Social Optima : (Late Breaking Report)","authors":"Louis B. Rosenberg, G. Willcox","doi":"10.1109/COGSIMA.2018.8423987","DOIUrl":"https://doi.org/10.1109/COGSIMA.2018.8423987","url":null,"abstract":"in the natural world, many social species amplify their collective intelligence by forming real-time closed-loop systems. Referred to as Swarm Intelligence (SI), this phenomenon has been rigorously studied in schools of fish, flocks of birds, and swarms of bees. In recent years, technology has enabled human groups to form real-time closed-loop systems modeled after natural swarms and moderated by AI algorithms. Referred to as Artificial Swarm Intelligence (ASI), these methods have been shown to enable human groups to reach optimized decisions. The present research explores this further, testing if ASI enables groups with conflicting views to converge on socially optimal solutions. Results showed that “swarming” was significantly more effective at enabling groups to converge on the Social Optima than three common voting methods: (i) Plurality voting (i) Borda Count and (iii) Condorcet pairwise voting. While traditional voting methods converged on socially optimal solutions with 60% success across a test set of 100 questions, the ASI system converged on socially optimal solutions with 82% success (p<0.001).","PeriodicalId":231353,"journal":{"name":"2018 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133997006","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}

引用次数: 11

CogSIMA 2018 Preface CogSIMA 2018 前言

2018 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA) Pub Date : 2018-06-01 DOI: 10.1109/cogsima.2018.8423968

引用次数: 0

A Method to Identify Relevant Information Sufficient to Answer Situation Dependent Queries 一种识别足以回答情境相关查询的相关信息的方法

2018 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA) Pub Date : 2018-06-01 DOI: 10.1109/COGSIMA.2018.8423973

Shan Lu, M. Kokar

{"title":"A Method to Identify Relevant Information Sufficient to Answer Situation Dependent Queries","authors":"Shan Lu, M. Kokar","doi":"10.1109/COGSIMA.2018.8423973","DOIUrl":"https://doi.org/10.1109/COGSIMA.2018.8423973","url":null,"abstract":"In various complex and dynamic environments, having a good understanding of the current situation in hand is the foundation for successful decision-making. Several frameworks have been proposed for information gathering and interpretation in situation assessment. However, decision makers nowadays face an information overload challenge during situation assessment. When the decision maker deals with a specific situation, usually large volumes of information are delivered to him or her in real time, of which only a few are relevant. It is practically impossible for them to deal with such huge data streams in real time. Additionally, if a situation needs to be communicated to others, it is not clear what information is relevant and thus would need to be sent over (sometimes over-loaded) communication links in order to convey the description of the situation. Therefore, a method is needed to support the human decision makers to identify the relevant information in situation assessment. In this paper, we develop a inference-based information relevance reasoning method in situation assessment to automatically identify relevant information for characterizing the situation that a decision maker is dealing with. By using this method, the following two basic questions will be answered: (1) what kind of information is relevant to characterize a situation? (2) how to identify the relevant information automatically? In this paper, we take the cyber security as the application domain, and evaluate our method using a cyber security dataset generated by Skaion corporation. We use four metrics to evaluate our method.","PeriodicalId":231353,"journal":{"name":"2018 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115091322","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}

引用次数: 2

Modelling Complex System-Of-Systems for Creating Situation Awareness : (Late Breaking Report) 为创建态势感知的复杂系统的系统建模:(最新突发报告)

2018 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA) Pub Date : 2018-06-01 DOI: 10.1109/COGSIMA.2018.8423969

L. Motus, K. Taveter, Veiko Dieves

引用次数: 1