2016年智能，自适应和自治系统复杂的模拟与建模研讨会 2016 Modeling and Simulation of Complexity in Intelligent, Adaptive and Autonomous Systems (MSCIAAS) Symposiu

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Heterogeneous systems are the norm today. A system deployed in a netcentric environment eventually becomes a part of a system of systems (SoS). This makes design, analysis and testing for the system-at-hand a complex endeavor in itself. Testing in isolation is not the same as a real-system operation, since the system’s behavior is also determined by the input, which evolves from the environment. This exact factor is difficult to predict, due to an ever-increasing level of autonomy.

Advanced Modeling and Simulation (M&S) frameworks are required in order to facilitate SoS design, development, testing, and integration. In more particular, these frameworks have to provide methods to deal with intelligent, emergent, and adaptive behavior as well as autonomy.

The subject of emergent behavior and M&S of emergent behaviors takes the center-stage in such systems as it is unknown how a particular system responds in the face of emergent behavior arising out of interactions with other systems. Intelligent behavior is also defined as an emergent property in some complex systems. Consequently, systems that respond and adapt to such behaviors may be called intelligent systems as well. Complexity is a multi-level phenomenon that exists at structural, behavioral and knowledge levels in such SoS. Emergent behavior is an outcome of this complexity. This track aims to focus on M&S of these aspects of complex SoS engineering and aims to bring researchers, developers and industry practitioners working in the areas of complex, adaptive and autonomous SoS engineering that may incorporate human as an integral part of SoS operations

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The cutting edge research is invited in the following topical areas but not limited to:

Complexity in Structure: network, hierarchical, small-world, flat, etc.
Complexity in Behavior: Micro and macro behaviors, local and global behaviors, teleologic and epistemological behaviors
Complexity in Knowledge: ontology design, ontology-driven modeling, ontology-evaluation, ontology transformation, etc.
Complexity in Human-in-the-loop: artificial agents, cognitive agents, multi-agents, man-inloop, human-computer-interaction
Complexity in intelligence-based systems: Situated behavior, knowledge-based behavior, memoic behavior, resource-constrained systems, energy-aware systems
Complexity in adaptation and autonomy
Complexity in architecture: Flat, full-mesh, hierarchical, adaptive, swarm, transformative
Complexity in awareness: Self-* (organization, explanation, configuration)
Complexity in interactions: collaboration, negotiation, greedy, rule-based, environment-based, etc.
Complexity in Live, Virtual and Constructive environment
Complexity in Artificial Systems, Social systems, techno-economic-social systems
Complexity in Model Engineering of complex SoS
Complexity in Model Specification using modeling languages and architecture frameworks such as UML, PetriNets, SysML, DoDAF, MoDAF, etc.
Complexity in Simulation environment engineering: distributed simulation, parallel simulation, cloud simulation, netcentric parallel distributed environments
Complexity in Testing and Evaluation tools for SoS engineering
Complexity in Heterogeneity: Hardware/Software Co-design, Hardware in the Loop, Cyber Physical Systems, the Internet of Things
Metrics for Complexity design and evaluation
Verification, validation and accreditation of Complexity in SoS
Application of Complexity aspects in domain engineering: Financial, Power, Robotics, Swarm, Economic, Policy, etc.
SoS Failure due to Complexity