Above are papers related to the following paper: [Paper_ID] Paper #47839. 	[Title] Incremental Pruning: A Simple, Fast, Exact Algorithm for Partially Observable Markov Decision Processes [Abstract] Most exact algorithms for general pomdps use a form of dynamic programming in which a piecewise-linear and convex representation of one value function is transformed into another. We examine variations of the "incremental pruning" approach for solving this problem and compare them to earlier algorithms from theoretical and empirical perspectives. We find that incremental pruning is presently the most efficient algorithm for solving pomdps. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1124837. 	[Title] Planning Medical Therapy Using Partially Observable Markov Decision Processes. [Abstract] Diagnosis of a disease and its treatment are not separate, one-shot activities. Instead they are very often dependent and interleaved over time, mostly due to uncertainty about the underlying disease, uncertainty associated with the response of a patient to the treatment and varying cost of different treatment and diagnostic (investigative) procedures. The framework particularly suitable for modeling such a complex therapy decision process is Partially observable Markov decision process (POMDP). Unfortunately the problem of finding the optimal therapy within the standard POMDP framework is also computationally very costly. In this paper we investigate various structural extensions of the standard POMDP framework and approximation methods which allow us to simplify model construction process for larger therapy problems and to solve them faster. A therapy problem we target specifically is the management of patients with ischemic heart disease. Please summarise all the given information.

Paper #47839 has the title `Incremental Pruning: A Simple, Fast, Exact Algorithm for Partially Observable Markov Decision Processes  
` and the abstract `Most exact algorithms for general pomdps use a form of dynamic programming in which a piecewise-linear and convex representation of one value function is transformed into another. We examine variations of the "incremental pruning" approach for solving this problem and compare them to earlier algorithms from theoretical and empirical perspectives. We find that incremental pruning is presently the most efficient algorithm for solving pomdps.
`.
Question: Which category should Paper #47839 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #47839 has the title `Incremental Pruning: A Simple, Fast, Exact Algorithm for Partially Observable Markov Decision Processes  
` and the abstract `Most exact algorithms for general pomdps use a form of dynamic programming in which a piecewise-linear and convex representation of one value function is transformed into another. We examine variations of the "incremental pruning" approach for solving this problem and compare them to earlier algorithms from theoretical and empirical perspectives. We find that incremental pruning is presently the most efficient algorithm for solving pomdps.
`.
Question: Which category should Paper #47839 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: Reinforcement_Learning