ISSN :3049-2297
Thomas E. Portegys
Jou. Artif. Intell. Auto. Intell., 1 (1):1-12
Thomas E. Portegys : Dialectek
DOI: https://dx.doi.org/10.54364/JAIAI.2024.1101
Article History: Received on: 07-Apr-24, Accepted on: 16-Jun-24, Published on: 23-Jun-24
Corresponding Author: Thomas E. Portegys
Email: portegys@gmail.com
Citation: Thomas E. Portegys (2024). Learning causation event conjunction sequences. Jou. Artif. Intell. Auto. Intell., 1 (1 ):1-12
This is an examination of some methods that learn causations in event sequences. A causation is defined as a conjunction of one or more cause events occurring in an arbitrary order, with possible intervening non-causal events, that lead to an effect. The methods include recurrent and non-recurrent artificial neural networks (ANNs), as well as a histogram-based algorithm. An attention recurrent ANN performed the best of the ANNs, while the histogram algorithm was significantly superior to all the ANNs.