Word sense disambiguation using the SP theory of intelligence – We present the first fully connected knowledge graph (P3-CP) using both natural language and machine learning. The key element of our work is to learn both the semantics and the semantics underlying P3-CP. We demonstrate that NP-hardness plays a key role of the semantics learning, as well as we show that the computational cost of learning a complete knowledge graph can be reduced down to a small computational loss, which is equivalent to a small computation on the CPU. We illustrate the usefulness of the P3-CP to our research community by showing that (i) we can perform a full knowledge graph on a PC with high computational cost, and (ii) we can achieve a similar theoretical analysis of the semantics learning. We report our results in the context of the study of knowledge retrieval. In particular, we present a method to learn a fully connected knowledge graph which combines natural language and machine learning algorithms and which is a major topic of the research community. We also present a method to learn a knowledge graph which combines both the semantics learning and the semantics learning algorithms.

We present an algorithm for learning and solving simple logic programs (SMPs) that can be successfully implemented using pure reinforcement learning (RL). This work, called Deep Logic Programming (DLP), is a novel RL technique that aims to harness the state-of-the-art state-of-the-art reinforcement learning methods for reasoning about logic programs. Our approach is based on two simple yet powerful RL tasks: solving the problem of determining the best way to answer a query, and solving the problem of finding a policy based on a random search of a constraint set. We demonstrate that DLP is able to learn to solve complex logic programs using high-dimensional logic programs. We further show that DLP is the best possible option for solving logical programs that do not have any logical properties, and that it is the best available model for reasoning about logic programs that can be learned using purely reinforcement learning methods.

Constraint-Based, Minimum Description Length Computation and Total Sampling for Efficient Constraint Problems

Inverted Reservoir Computing

Word sense disambiguation using the SP theory of intelligence

A New Algorithm for Unsupervised Learning of Motion Patterns from Moving Object Data

Using Artificial Neurons to Generate Spatial Spaces for Brain-like MachinesWe present an algorithm for learning and solving simple logic programs (SMPs) that can be successfully implemented using pure reinforcement learning (RL). This work, called Deep Logic Programming (DLP), is a novel RL technique that aims to harness the state-of-the-art state-of-the-art reinforcement learning methods for reasoning about logic programs. Our approach is based on two simple yet powerful RL tasks: solving the problem of determining the best way to answer a query, and solving the problem of finding a policy based on a random search of a constraint set. We demonstrate that DLP is able to learn to solve complex logic programs using high-dimensional logic programs. We further show that DLP is the best possible option for solving logical programs that do not have any logical properties, and that it is the best available model for reasoning about logic programs that can be learned using purely reinforcement learning methods.