Using Artificial Neurons to Generate Spatial Spaces for Brain-like Machines – 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.
Answer Set Programming has been one of the most developed and influential methods for generating answers. This paper proposes a new method to solve the task of solving a set of logical questions by solving the logical problem. The problem may include: 1. How to identify the correct answer in every question, 2. Is there the right answer in every question, 3. Why are human minds different? 4. Can we solve this problem, and if it is not the right answer, can we solve it? We demonstrate that the answer set problem is NP-complete and that a simple algorithm can be solved in a time of hours.
We present a model of a probabilistic network that can be constructed from a finite number of observations. We use the model to show how this network has a probabilistic structure, and it is possible to derive its logic. We also describe examples of this network for which the model is proved to be correct, and use it to illustrate the properties of the network.
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Using Artificial Neurons to Generate Spatial Spaces for Brain-like Machines
How Many Words and How Much Word is In a Question and Answers ?Answer Set Programming has been one of the most developed and influential methods for generating answers. This paper proposes a new method to solve the task of solving a set of logical questions by solving the logical problem. The problem may include: 1. How to identify the correct answer in every question, 2. Is there the right answer in every question, 3. Why are human minds different? 4. Can we solve this problem, and if it is not the right answer, can we solve it? We demonstrate that the answer set problem is NP-complete and that a simple algorithm can be solved in a time of hours.
We present a model of a probabilistic network that can be constructed from a finite number of observations. We use the model to show how this network has a probabilistic structure, and it is possible to derive its logic. We also describe examples of this network for which the model is proved to be correct, and use it to illustrate the properties of the network.