Above are papers related to the following paper: [Paper_ID] Paper #118079. 	[Title] The Neural Network House: An Overview [Abstract] Typical home comfort systems utilize only rudimentary forms of energy management and conservation. The most sophisticated technology in common use today is an automatic setback thermostat. Tremendous potential remains for improving the efficiency of electric and gas usage. However, home residents who are ignorant of the physics of energy utilization cannot design environmental control strategies, but neither can energy management experts who are ignorant of the behavior patterns of the inhabitants. Adaptive control seems the only alternative. We have begun building an adaptive control system that can infer appropriate rules of operation for home comfort systems based on the lifestyle of the inhabitants and energy conservation goals. Recent research has demonstrated the potential of neural networks for intelligent control. We are constructing a prototype control system in an actual residence using neural network reinforcement learning and prediction techniques. The residence is equipped with sensors to provide information about environmental conditions (e.g., temperatures, ambient lighting level, sound and motion in each room) and actuators to control the gas furnace, electric space heaters, gas hot water heater, lighting, motorized blinds, ceiling fans, and dampers in the heating ducts. This paper presents an overview of the project as it now stands. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #40131. 	[Title] A comparison of neural net and conventional techniques for lighting control [Abstract] We compare two techniques for lighting control in an actual room equipped with seven banks of lights and photoresistors to detect the lighting level at four sensing points. Each bank of lights can be independently set to one of sixteen intensity levels. The task is to determine the device intensity levels that achieve a particular configuration of sensor readings. One technique we explored uses a neural network to approximate the mapping between sensor readings and device intensity levels. The other technique we examined uses a conventional feedback control loop. The neural network approach appears superior both in that it does not require experimentation on the fly (and hence fluctuating light intensity levels during settling, and lengthy settling times) and in that it can deal with complex interactions that conventional control techniques do not handle well. This comparison was performed as part of the "Adaptive House" project, which is described briefly. Further directions for control in the Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #40135. 	[Title] Refining PID Controllers using Neural Networks [Abstract] The Kbann approach uses neural networks to refine knowledge that can be written in the form of simple propositional rules. We extend this idea further by presenting the Manncon algorithm by which the mathematical equations governing a PID controller determine the topology and initial weights of a network, which is further trained using backpropagation. We apply this method to the task of controlling the outflow and temperature of a water tank, producing statistically-significant gains in accuracy over both a standard neural network approach and a non-learning PID controller. Furthermore, using the PID knowledge to initialize the weights of the network produces statistically less variation in testset accuracy when compared to networks initialized with small random numbers. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #202639. 	[Title] PREDICTING SUNSPOTS AND EXCHANGE RATES WITH CONNECTIONIST NETWORKS [Abstract] We investigate the effectiveness of connectionist networks for predicting the future continuation of temporal sequences. The problem of overfitting, particularly serious for short records of noisy data, is addressed by the method of weight-elimination: a term penalizing network complexity is added to the usual cost function in back-propagation. The ultimate goal is prediction accuracy. We analyze two time series. On the benchmark sunspot series, the networks outperform traditional statistical approaches. We show that the network performance does not deteriorate when there are more input units than needed. Weight-elimination also manages to extract some part of the dynamics of the notoriously noisy currency exchange rates and makes the network solution interpretable. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #13686. 	[Title] Framework for Combining Symbolic and Neural Learning rule extraction from neural networks the KBANN algorithm [Abstract] Technical Report 1123, Computer Sciences Department, University of Wisconsin - Madison, Nov. 1992 ABSTRACT This article describes an approach to combining symbolic and connectionist approaches to machine learning. A three-stage framework is presented and the research of several groups is reviewed with respect to this framework. The first stage involves the insertion of symbolic knowledge into neural networks, the second addresses the refinement of this prior knowledge in its neural representation, while the third concerns the extraction of the refined symbolic knowledge. Experimental results and open research issues are discussed. A shorter version of this paper will appear in Machine Learning. Please summarise all the given information.

Paper #118079 has the title `The Neural Network House: An Overview  
` and the abstract `Typical home comfort systems utilize only rudimentary forms of energy management and conservation. The most sophisticated technology in common use today is an automatic setback thermostat. Tremendous potential remains for improving the efficiency of electric and gas usage. However, home residents who are ignorant of the physics of energy utilization cannot design environmental control strategies, but neither can energy management experts who are ignorant of the behavior patterns of the inhabitants. Adaptive control seems the only alternative. We have begun building an adaptive control system that can infer appropriate rules of operation for home comfort systems based on the lifestyle of the inhabitants and energy conservation goals. Recent research has demonstrated the potential of neural networks for intelligent control. We are constructing a prototype control system in an actual residence using neural network reinforcement learning and prediction techniques. The residence is equipped with sensors to provide information about environmental conditions (e.g., temperatures, ambient lighting level, sound and motion in each room) and actuators to control the gas furnace, electric space heaters, gas hot water heater, lighting, motorized blinds, ceiling fans, and dampers in the heating ducts. This paper presents an overview of the project as it now stands.
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Question: Which category should Paper #118079 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #118079 has the title `The Neural Network House: An Overview  
` and the abstract `Typical home comfort systems utilize only rudimentary forms of energy management and conservation. The most sophisticated technology in common use today is an automatic setback thermostat. Tremendous potential remains for improving the efficiency of electric and gas usage. However, home residents who are ignorant of the physics of energy utilization cannot design environmental control strategies, but neither can energy management experts who are ignorant of the behavior patterns of the inhabitants. Adaptive control seems the only alternative. We have begun building an adaptive control system that can infer appropriate rules of operation for home comfort systems based on the lifestyle of the inhabitants and energy conservation goals. Recent research has demonstrated the potential of neural networks for intelligent control. We are constructing a prototype control system in an actual residence using neural network reinforcement learning and prediction techniques. The residence is equipped with sensors to provide information about environmental conditions (e.g., temperatures, ambient lighting level, sound and motion in each room) and actuators to control the gas furnace, electric space heaters, gas hot water heater, lighting, motorized blinds, ceiling fans, and dampers in the heating ducts. This paper presents an overview of the project as it now stands.
`.
Question: Which category should Paper #118079 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