Introspective Forecasting
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Special Track on Machine Learning. Pages 3714-3719.
Abstract
Science ultimately seeks to reliably predict aspects of the future; but, how is this even possible in light of the logical paradox that making a prediction may cause the world to evolve in a manner that defeats it? We show how learning can naturally resolve this conundrum. The problem is studied within a causal or temporal version of the Probably Approximately Correct semantics, extended so that a learner's predictions are first recorded in the states upon which the learned hypothesis is later applied. On the negative side, we make concrete the intuitive impossibility of predicting reliably, even under very weak assumptions. On the positive side, we identify conditions under which a generic learning schema, akin to randomized trials, supports agnostic learnability.
@inproceedings{ijcai2015p522,
title = {Introspective Forecasting},
author = {Michael, Loizos},
booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-15}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Qiang Yang and Michael Wooldridge},
pages = {3714--3719},
year = {2015},
month = {7},
note = {Special Track on Machine Learning},
url = {https://ijcai.org/proceedings/2015/522},
}
TY - CPAPER TI - Introspective Forecasting AU - Michael, Loizos T2 - Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15 PB - International Joint Conferences on Artificial Intelligence PY - 2015 SP - 3714 EP - 3719 UR - https://ijcai.org/proceedings/2015/522 ER -Copied
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