Above are papers related to the following paper: [Paper_ID] Paper #1116181. 	[Title] Exploration in Machine Learning [Abstract] Most researchers in machine learning have built their learning systems under the assumption that some external entity would do all the work of furnishing the learning experiences. Recently, however, investigators in several subfields of machine learning have designed systems that play an active role in choosing the situations from which they will learn. Such activity is generally called exploration. This paper describes a few of these exploratory learning projects, as reported in the literature, and attempts to extract a general account of the issues involved in exploration. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #887. 	[Title] Some studies in machine learning using the game of checkers. IBM Journal, 3(3):211-229, 1959. Some [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #20972. 	[Title] Exploratory Learning in the Game of GO [Abstract] This paper considers the importance of exploration to game-playing programs which learn by playing against opponents. The central question is whether a learning program should play the move which offers the best chance of winning the present game, or if it should play the move which has the best chance of providing useful information for future games. An approach to addressing this question is developed using probability theory, and then implemented in two different learning methods. Initial experiments in the game of Go suggest that a program which takes exploration into account can learn better against a knowledgeable opponent than a program which does not. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #35863. 	[Title] Associative Reinforcement Learning: Functions in k-DNF [Abstract] An agent that must learn to act in the world by trial and error faces the reinforcement learning problem, which is quite different from standard concept learning. Although good algorithms exist for this problem in the general case, they are often quite inefficient and do not exhibit generalization. One strategy is to find restricted classes of action policies that can be learned more efficiently. This paper pursues that strategy by developing algorithms that can efficiently learn action maps that are expressible in k-DNF. The algorithms are compared with existing methods in empirical trials and are shown to have very good performance. Please summarise all the given information.

Paper #1116181 has the title `Exploration in Machine Learning  
` and the abstract `Most researchers in machine learning have built their learning systems under the assumption that some external entity would do all the work of furnishing the learning experiences. Recently, however, investigators in several subfields of machine learning have designed systems that play an active role in choosing the situations from which they will learn. Such activity is generally called exploration. This paper describes a few of these exploratory learning projects, as reported in the literature, and attempts to extract a general account of the issues involved in exploration.
`.
Question: Which category should Paper #1116181 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1116181 has the title `Exploration in Machine Learning  
` and the abstract `Most researchers in machine learning have built their learning systems under the assumption that some external entity would do all the work of furnishing the learning experiences. Recently, however, investigators in several subfields of machine learning have designed systems that play an active role in choosing the situations from which they will learn. Such activity is generally called exploration. This paper describes a few of these exploratory learning projects, as reported in the literature, and attempts to extract a general account of the issues involved in exploration.
`.
Question: Which category should Paper #1116181 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: Theory