CIF: Small: Inference over Asymmetric Network and Data Structures

University Of California-Los Angeles, Los Angeles CA

Investigators

Abstract

In an age when the word "network" may refer to social networks, power networks, transportation networks, or data networks, interest in information processing over graphs has met with resurgence. The efforts under this proposal are relevant to applications involving large and distributed amounts of data, as is the case with health informatics, surveillance applications, data networks, or cloud computing. The research results will enable engineering systems to benefit from a bottom-up design paradigm involving coordination among less powerful agents to achieve higher levels of cognition and performance by an interconnected network of cooperating agents. The research will develop techniques that enable agents to work collectively for a common objective and to counter the degrading effect of imbalances that may exist in their interactions such as the fact that some agents in the network may be more informed than other agents; some agents may have a domineering tendency; some agents may be willing to share only partial information due to privacy and secrecy considerations; and some agents may be subject to corrupted data; or have different objectives than the group. Results developed under this work will benefit the training of a diverse body of students in the strategic area of network science. The results will also be disseminated broadly to the research community online and in archival publications and meetings. Performance indicators in networked applications include cooperation among agents to bestow resilience to failure; privacy and secrecy considerations where agents may not be comfortable sharing data with remote centers for processing; and the fact that large amounts of data may already be available in dispersed form and aggregation of the data at a central location can be costly. These considerations have motivated the development of powerful distributed mechanisms that enable agents to cooperate to attain superior inference capabilities. Many distributed techniques ignore critical asymmetries that exist in both network and data structures. Robotic swarms are one example of a notable application where agents can benefit from adjusting their exploration space in response to asymmetry conditions, malfunctioning of neighbors, or suspicious behavior by intruders. A second example is the use of networked learners to mine information from big databases, such as those related to health informatics, power grids, social networks, or surveillance applications. If asymmetries are ignored, they can lead to erroneous inference conclusions and degraded performance. This research effort exploits cognitive abilities to enable networks to counter asymmetries and their deleterious effects. In particular, this project seeks adaptation and learning mechanisms that endow multi-agent systems with the ability to implement decision-making processes that mimic quorum responses by animal groups, the ability to identify and react to domineering or intrusive behavior, the ability to implement divide-and-conquer, clustering, and labor division strategies, and the ability to counter the effect of corrupted data.