@inproceedings{KKSGSPTlta18,
    author = {Alex Kearney and Anna Koop and Craig Sherstan and Johannes Günther and Richard Sutton and Patrick Pilarski and Matthew Taylor},
    title = {Evaluating Predictive Knowledge},
    booktitle = {Proceedings of the AAAI Fall Symposium on Reasoning and Learning in Real-World Systems for Long-Term Autonomy},
    pages = {43--46},
    year = {2018},
    abstract = {Predictive Knowledge (PK) is a group of approaches to machine perception and knowledgability using large collections of predictions made online in real-time through interaction with the environment. Determining how well a collection of predictions captures the relevant dynamics of the environment remains an open challenge. In this paper, we introduce specifications for sensorimotor baselines and robustness-to-transfer metrics for evaluation of PK. We illustrate the use of these metrics by comparing variant architectures of General Value Function (GVF) networks.}
}