Learning action models for reactive autonomous agents
Learning action models for reactive autonomous agents
Large Margin Classification Using the Perceptron Algorithm
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Learning of Boolean Functions Using Support Vector Machines
ALT '01 Proceedings of the 12th International Conference on Algorithmic Learning Theory
Learning action models from plan examples using weighted MAX-SAT
Artificial Intelligence
Noise Tolerant Variants of the Perceptron Algorithm
The Journal of Machine Learning Research
The initial development of object knowledge by a learning robot
Robotics and Autonomous Systems
Learning symbolic models of stochastic domains
Journal of Artificial Intelligence Research
Learning partially observable deterministic action models
Journal of Artificial Intelligence Research
Pengi: an implementation of a theory of activity
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 1
| Hi-index | 0.00 |
We investigate the problem of learning action effects in partially observable STRIPS planning domains. Our approach is based on a voted kernel perceptron learning model, where action and state information is encoded in a compact vector representation as input to the learning mechanism, and resulting state changes are produced as output. Our approach relies on deictic features that assume an attentional mechanism that reduces the size of the representation. We evaluate our approach on a number of partially observable planning domains, and show that it can quickly learn the dynamics of such domains, with low average error rates. We show that our approach handles noisy domains, conditional effects, and that it scales independently of the number of objects in a domain.