CogSIMA 2014 - 2014 IEEE International Inter-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support
Date2014-03-03 - 2014-03-06
Deadline2013-10-15
VenueSan Antonio, USA - United States
Keywords
Websitehttps://cogsima2014.org/
Topics/Call fo Papers
The 4th Annual IEEE CogSIMA Conference continues and expands the research domain on cognitive situation management that was established by the previous conferences. The aim of the CogSIMA conference is to provide a venue for presenting scientific results of multi-disciplinary studies specific to complex heterogeneous dynamical systems that include humans, physical systems, computer agents and networks whose behaviors depend on situations. Examples of such systems include ad hoc communication networks, social networks, physical and cyber security systems, disaster monitoring and recovery systems, epidemic monitoring and control, intelligent transportation systems, financial and investment services, and tactical and operational battlefield command and control systems.
Common to these systems is the need to adequately perceive, reflect and act according to the situational changes happening both in the surrounding world and within the systems themselves. The amounts of information that needs to be processed in order to derive decisions overwhelm both the human cognitive capabilities and the computer processing power. Consequently, there is a need to develop new situation awareness and decision support approaches for augmenting the cognitive capabilities of working collaboratively humans and computer agents. These agents, both humans and machines, are faced with collaborative solving of various problems:
Selecting which data is relevant to the objectives and actively requesting or searching for additional information;
Integrating disparate data sources into a coherent information representation and a consistent model of the world;
Inferring relations among various elements of the model, including prediction of the future states of the world;
Assessing uncertainties associated with decisions;
Learning new models and/or model elements;
Valuating particular current and/or future states of the world according to some metrics;
Identifying desirable states of the world, selecting actions that could lead to the desirable states;
Communicating and interacting with other agents; and
Engaging into the process of collective situation awareness and decision-making.
While this process is complex, it is further complicated by the fact that each of the steps mentioned above depend on the situation. A specific item of information is relevant in one situation but is irrelevant in another. A given model of the world is adequate in one situation, but is not appropriate in another. A given set of logical conditions implies a given relation holds in a given situation, but not in another situation. A given state is desirable in one situation but is harmful in another. A given sequence of actions leads to a given state in one situation, but not in another.
Common to these systems is the need to adequately perceive, reflect and act according to the situational changes happening both in the surrounding world and within the systems themselves. The amounts of information that needs to be processed in order to derive decisions overwhelm both the human cognitive capabilities and the computer processing power. Consequently, there is a need to develop new situation awareness and decision support approaches for augmenting the cognitive capabilities of working collaboratively humans and computer agents. These agents, both humans and machines, are faced with collaborative solving of various problems:
Selecting which data is relevant to the objectives and actively requesting or searching for additional information;
Integrating disparate data sources into a coherent information representation and a consistent model of the world;
Inferring relations among various elements of the model, including prediction of the future states of the world;
Assessing uncertainties associated with decisions;
Learning new models and/or model elements;
Valuating particular current and/or future states of the world according to some metrics;
Identifying desirable states of the world, selecting actions that could lead to the desirable states;
Communicating and interacting with other agents; and
Engaging into the process of collective situation awareness and decision-making.
While this process is complex, it is further complicated by the fact that each of the steps mentioned above depend on the situation. A specific item of information is relevant in one situation but is irrelevant in another. A given model of the world is adequate in one situation, but is not appropriate in another. A given set of logical conditions implies a given relation holds in a given situation, but not in another situation. A given state is desirable in one situation but is harmful in another. A given sequence of actions leads to a given state in one situation, but not in another.
Other CFPs
- Doctoral Workshop at LDIC 2014
- 4th International Conference on Dynamics in Logistics
- International Workshop on Trusted Information in Big Data (TIBiDa)
- The 2013 International Workshop on Cloud-assisted Smart Cyber-Physical Systems (C-SmartCPS)
- 11th International Conference on Theory and Applications of Models of Computation
Last modified: 2013-08-07 21:57:13