Above are papers related to the following paper: [Paper_ID] Paper #1123991. 	[Title] Bayesian Probability Theory A General Method for Machine Learning [Abstract] This paper argues that Bayesian probability theory is a general method for machine learning. From two well-founded axioms, the theory is capable of accomplishing learning tasks that are incremental or non-incremental, supervised or unsupervised. It can learn from different types of data, regardless of whether they are noisy or perfect, independent facts or behaviors of an unknown machine. These capabilities are (partially) demonstrated in the paper through the uniform application of the theory to two typical types of machine learning: incremental concept learning and unsupervised data classification. The generality of the theory suggests that the process of learning may not have so many different "types" as currently held, and the method that is the oldest may be the best after all. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #10793. 	[Title] Learning Finite Automata Using Local Distinguishing Experiments [Abstract] One of the open problems listed in [ Rivest and Schapire, 1989 ] is whether and how that the copies of L fl in their algorithm can be combined into one for better performance. This paper describes an algorithm called D fl that does that combination. The idea is to represent the states of the learned model using observable symbols as well as hidden symbols that are constructed during learning. These hidden symbols are created to reflect the distinct behaviors of the model states. The distinct behaviors are represented as local distinguishing experiments (LDEs) (not to be confused with global distinguishing sequences), and these LDEs are created when the learner's prediction mismatches the actual observation from the unknown machine. To synchronize the model with the environment, these LDEs can also be concatenated to form a homing sequence. It can be shown that D fl can learn, with probability 1 , a model that is an *-approximation of the unknown machine, in a number of actions polynomial in the size of the environment and Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #84459. 	[Title] Learning Concepts from Sensor Data of a Mobile Robot [Abstract] Machine learning can be a most valuable tool for improving the flexibility and efficiency of robot applications. Many approaches to applying machine learning to robotics are known. Some approaches enhance the robot's high-level processing, the planning capabilities. Other approaches enhance the low-level processing, the control of basic actions. In contrast, the approach presented in this paper uses machine learning for enhancing the link between the low-level representations of sensing and action and the high-level representation of planning. The aim is to facilitate the communication between the robot and the human user. A hierarchy of concepts is learned from route records of a mobile robot. Perception and action are combined at every level, i.e., the concepts are perceptually anchored. The relational learning algorithm grdt has been developed which completely searches in a hypothesis space, that is restricted by rule schemata, which the user defines in terms of grammars. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1130356. 	[Title] Learning from the Environment by Experimentation: The Need for Few and Informative Examples [Abstract] An intelligent system must be able to adapt and learn to correct and update its model of the environment incrementally and deliberately. In complex environments that have many parameters and where interactions have a cost, sampling the possible range of states to test the results of action executions is not a practical approach. We present a practical approach based on continuous and selective interaction with the environment that pinpoints the type of fault in the domain knowledge that causes any unexpected behavior of the environment, and resorts to experimentation when additional information is needed to correct the system's knowledge. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6238. 	[Title] Discovery as Autonomous Learning from the Environment [Abstract] Discovery involves collaboration among many intelligent activities. However, little is known about how and in what form such collaboration occurs. In this paper, a framework is proposed for autonomous systems that learn and discover from their environment. Within this framework, many intelligent activities such as perception, action, exploration, experimentation, learning, problem solving, and new term construction can be integrated in a coherent way. The framework is presented in detail through an implemented system called LIVE, and is evaluated through the performance of LIVE on several discovery tasks. The conclusion is that autonomous learning from the environment is a feasible approach for integrating the activities involved in a discovery process. Please summarise all the given information.

Paper #1123991 has the title `Bayesian Probability Theory A General Method for Machine Learning  
` and the abstract `This paper argues that Bayesian probability theory is a general method for machine learning. From two well-founded axioms, the theory is capable of accomplishing learning tasks that are incremental or non-incremental, supervised or unsupervised. It can learn from different types of data, regardless of whether they are noisy or perfect, independent facts or behaviors of an unknown machine. These capabilities are (partially) demonstrated in the paper through the uniform application of the theory to two typical types of machine learning: incremental concept learning and unsupervised data classification. The generality of the theory suggests that the process of learning may not have so many different "types" as currently held, and the method that is the oldest may be the best after all. 
`.
Question: Which category should Paper #1123991 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1123991 has the title `Bayesian Probability Theory A General Method for Machine Learning  
` and the abstract `This paper argues that Bayesian probability theory is a general method for machine learning. From two well-founded axioms, the theory is capable of accomplishing learning tasks that are incremental or non-incremental, supervised or unsupervised. It can learn from different types of data, regardless of whether they are noisy or perfect, independent facts or behaviors of an unknown machine. These capabilities are (partially) demonstrated in the paper through the uniform application of the theory to two typical types of machine learning: incremental concept learning and unsupervised data classification. The generality of the theory suggests that the process of learning may not have so many different "types" as currently held, and the method that is the oldest may be the best after all. 
`.
Question: Which category should Paper #1123991 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Probabilistic_Methods