Autonomy-Oriented Computing for Web Intelligence and Brain Informatics

In this talk, we will discuss the roles and potential implications of autonomy-oriented computing (AOC) in/to the future of Web intelligence (WI) and brain informatics (BI). Generally speaking, AOC is a methodology for self-organized computing that is well suited for two types of applications: (i) to characterize the working mechanisms that lead to certain emergent behavior in natural and artificial complex systems (e.g., phenomena in ldquoWeb Sciencerdquo, and the dynamics of social networks and neural systems), and (ii) to develop solutions to large-scale, distributed computational problems (e.g., distributed scalable scientific or social computing, and collective intelligence). AOC emphasizes the modeling of autonomous entities or agents that locally interact following certain nature or real-world inspired behavioral rules, resulting in some self-organized behavior of the entities and/or their nonlinearly aggregated effects. Computing based on interacting entities and their self-organization can offer several means as well as advantages for WI and BI development, such as natural formulation, distributed implementation, scalable performance, robustness, and behavioral or first-principle understanding.