ToyArchitecture: Unsupervised Learning of Interpretable Models of the World

Research in Artificial Intelligence (AI) has focused mostly on two extremes: either on small improvements in narrow AI domains, or on universal theoretical frameworks which are usually uncomputable, incompatible with theories of biological intelligence, or lack practical implementations. The goal of this work is to combine the main advantages of the two: to follow a big picture view, while providing a particular theory and its implementation. In contrast with purely theoretical approaches, the resulting architecture should be usable in realistic settings, but also form the core of a framework containing all the basic mechanisms, into which it should be easier to integrate additional required functionality. In this paper, we present a novel, purposely simple, and interpretable hierarchical architecture which combines multiple different mechanisms into one system: unsupervised learning of a model of the world, learning the influence of one's own actions on the world, model-based reinforcement learning, hierarchical planning and plan execution, and symbolic/sub-symbolic integration in general. The learned model is stored in the form of hierarchical representations with the following properties: 1) they are increasingly more abstract, but can retain details when needed, and 2) they are easy to manipulate in their local and symbolic-like form, thus also allowing one to observe the learning process at each level of abstraction. On all levels of the system, the representation of the data can be interpreted in both a symbolic and a sub-symbolic manner. This enables the architecture to learn efficiently using sub-symbolic methods and to employ symbolic inference.Comment: Revision: changed the pdftitl

Kontextové modely pro statistickou kompresi dat

Current context modelling methods use an aggregated form of the statistics reusing the data history only rarely. This work proposes two independent methods that use the history in a more elaborate way. When the Prediction by Partial Matching (PPM) method updates its context tree, previous occurrences of a newly added context are ignored, which harms precision of the probabilities. An improved algorithm, which uses the complete data history, is described. The empirical results suggest that this PPM sub-obtimality is one of the major cause of the problem of inaccurate probabilities in high context orders. Current methods (especially PAQ) adapt to non-stationary data by strong favoring of the most recent statistics. The method proposed in this work generalizes this approach by favoring those parts of the history which are the most relevant to the current data, and its imlementation provides an improvement for almost all tested data especially for some samples of non-stationary data

Kontextové modely pro statistickou kompresi dat

Current context modelling methods use an aggregated form of the statistics reusing the data history only rarely. This work proposes two independent methods that use the history in a more elaborate way. When the Prediction by Partial Matching (PPM) method updates its context tree, previous occurrences of a newly added context are ignored, which harms precision of the probabilities. An improved algorithm, which uses the complete data history, is described. The empirical results suggest that this PPM sub-obtimality is one of the major cause of the problem of inaccurate probabilities in high context orders. Current methods (especially PAQ) adapt to non-stationary data by strong favoring of the most recent statistics. The method proposed in this work generalizes this approach by favoring those parts of the history which are the most relevant to the current data, and its imlementation provides an improvement for almost all tested data especially for some samples of non-stationary data