The power of amnesia: learning probabilistic automata with variable memory length
Machine Learning - Special issue on COLT '94
Adaptive mixtures of probabilistic transducers
Neural Computation
Shared context probabilistic transducers
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Multiagent learning using a variable learning rate
Artificial Intelligence
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Design of a linguistic postprocessor using variable memory length Markov models
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
Non-stationary policy learning in 2-player zero sum games
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Integration of sequence learning and CBR for complex equipment failure prediction
ICCBR'11 Proceedings of the 19th international conference on Case-Based Reasoning Research and Development
| Hi-index | 0.00 |
Robust sequence prediction is an essential component of an intelligent agent acting in a dynamic world. We consider the case of near-future event prediction by an online learning agent operating in a non-stationary environment. The challenge for a learning agent under these conditions is to exploit the relevant experience from a limited environmental event history while preserving flexibility.We propose a novel time/space efficient method for learning temporal sequences and making short-term predictions. Our method operates on-line, requires few exemplars, and adapts easily and quickly to changes in the underlying stochastic world model. Using a short-term memory of recent observations, the method maintains a dynamic space of candidate hypotheses in which the growth of the space is systematically and dynamically pruned using an entropy measure over the observed predictive quality of each candidate hypothesis.The method compares well against Markov-chain predictions, and adapts faster than learned Markov-chain models to changes in the underlying distribution of events. We demonstrate the method using both synthetic data and empirical experience from a game-playing scenario with human opponents.