Decision Learning and Adaptation over Multi-Task Networks

TL;DR

This paper explores how multi-agent networks can learn and adapt in real-time to multi-task decision problems, including change detection and multi-state decision-making, with theoretical analysis and simulation validation.

Contribution

It introduces a framework for multi-task learning and adaptation in multi-agent networks, with theoretical steady-state performance analysis and handling of complex cluster structures.

Findings

Theoretical steady-state performance bounds are derived.

Simulations validate the theoretical predictions.

The framework effectively manages multi-task decision scenarios.

Abstract

This paper studies the operation of multi-agent networks engaged in multi-task decision problems under the paradigm of simultaneous learning and adaptation. Two scenarios are considered: one in which a decision must be taken among multiple states of nature that are known but can vary over time and space, and another in which there exists a known "normal" state of nature and the task is to detect unpredictable and unknown deviations from it. In both cases the network learns from the past and adapts to changes in real time in a multi-task scenario with different clusters of agents addressing different decision problems. The system design takes care of challenging situations with clusters of complicated structure, and the performance assessment is conducted by computer simulations. A theoretical analysis is developed to obtain a statistical characterization of the agents' status at steady-state, under the simplifying assumption that clustering is made without errors. This provides approximate bounds for the steady-state decision performance of the agents. Insights are provided for deriving accurate performance prediction by exploiting the derived theoretical results.