The affinity of information within a multi-agent value of information paradigm

Abstract

The multi-agent Value of Information (MAVoI) problem is concerned with the capacity of autonomous agents to evaluate the estimated value of available information from and for other agents. Such a paradigm would allow agents to limit information sharing under network constraints, environmental and adversarial, and also to decide when to prioritize inter-agent communications over further information-gathering. In our work, we contribute to the conceptual development of an MAVoI framework by expanding upon a previous paradigm [1] that connected the notion of the Value of Information (VoI), as it originates from decision theory, to the body of work on the Quality of Information (QoI/IQ). Within this paradigm, QoI characterizes the intrinsic attributes of information objects that may be used to judge them, while VoI judges their context- and user-specific utility and fitness-for-use. For teams of multiple, autonomous agents with distributed intelligence, we propose an additional category: the Affinity of Information (AoI). Since classical VoI quantifies the gain in value that comes from more information regarding the expected value of available actions, we propose AoI as those metrics and features that characterize the change in an agent’s state model. We then conceptually illustrate our proposed MAVoI taxonomy in the context of a distributed Simultaneous Localization and Mapping (SLAM) task, in which the mission objective is the localization of multiple objects of interest.