Lifelong Learning Metrics

• URL: http://arxiv.org/abs/2201.08278v1
• Date: Thu, 20 Jan 2022 16:29:14 GMT
• Title: Lifelong Learning Metrics
• Authors: Alexander New and Megan Baker and Eric Nguyen and Gautam Vallabha
• Abstract summary: The DARPA Lifelong Learning Machines (L2M) program seeks to yield advances in artificial intelligence (AI) systems. This document outlines a formalism for constructing and characterizing the performance of agents performing lifelong learning scenarios.
• Score: 63.8376359764052
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The DARPA Lifelong Learning Machines (L2M) program seeks to yield advances in artificial intelligence (AI) systems so that they are capable of learning (and improving) continuously, leveraging data on one task to improve performance on another, and doing so in a computationally sustainable way. Performers on this program developed systems capable of performing a diverse range of functions, including autonomous driving, real-time strategy, and drone simulation. These systems featured a diverse range of characteristics (e.g., task structure, lifetime duration), and an immediate challenge faced by the program's testing and evaluation team was measuring system performance across these different settings. This document, developed in close collaboration with DARPA and the program performers, outlines a formalism for constructing and characterizing the performance of agents performing lifelong learning scenarios.

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