Above are papers related to the following paper: [Paper_ID] Paper #115188. 	[Title] INVERSION IN TIME [Abstract] Inversion of multilayer synchronous networks is a method which tries to answer questions like "What kind of input will give a desired output?" or "Is it possible to get a desired output (under special input/output constraints)?". We will describe two methods of inverting a connectionist network. Firstly, we extend inversion via backpropagation (Linden/Kindermann [4], Williams [11]) to recurrent (El-man [1], Jordan [3], Mozer [5], Williams/Zipser [10]), time-delayed (Waibel at al. [9]) and discrete versions of continuous networks (Pineda [7], Pearlmutter [6]). The result of inversion is an input vector. The corresponding output vector is equal to the target vector except a small remainder. The knowledge of those attractors may help to understand the function and the generalization qualities of connectionist systems of this kind. Secondly, we introduce a new inversion method for proving the non-existence of an input combination under special constraints, e.g. in a subspace of the input space. This method works by iterative exclusion of invalid activation values. It might be a helpful way to judge the properties of a trained network. We conclude with simulation results of three different tasks: XOR, morse signal decoding and handwritten digit recognition. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1107728. 	[Title] ADAPTIVE LOOK-AHEAD PLANNING problem of finding good initial plans is solved by the use of [Abstract] We present a new adaptive connectionist planning method. By interaction with an environment a world model is progressively constructed using the backpropagation learning algorithm. The planner constructs a look-ahead plan by iteratively using this model to predict future reinforcements. Future reinforcement is maximized to derive suboptimal plans, thus determining good actions directly from the knowledge of the model network (strategic level). This is done by gradient descent in action space. Please summarise all the given information.

Paper #115188 has the title `INVERSION IN TIME  
` and the abstract `Inversion of multilayer synchronous networks is a method which tries to answer questions like "What kind of input will give a desired output?" or "Is it possible to get a desired output (under special input/output constraints)?". We will describe two methods of inverting a connectionist network. Firstly, we extend inversion via backpropagation (Linden/Kindermann [4], Williams [11]) to recurrent (El-man [1], Jordan [3], Mozer [5], Williams/Zipser [10]), time-delayed (Waibel at al. [9]) and discrete versions of continuous networks (Pineda [7], Pearlmutter [6]). The result of inversion is an input vector. The corresponding output vector is equal to the target vector except a small remainder. The knowledge of those attractors may help to understand the function and the generalization qualities of connectionist systems of this kind. Secondly, we introduce a new inversion method for proving the non-existence of an input combination under special constraints, e.g. in a subspace of the input space. This method works by iterative exclusion of invalid activation values. It might be a helpful way to judge the properties of a trained network. We conclude with simulation results of three different tasks: XOR, morse signal decoding and handwritten digit recognition. 
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Question: Which category should Paper #115188 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #115188 has the title `INVERSION IN TIME  
` and the abstract `Inversion of multilayer synchronous networks is a method which tries to answer questions like "What kind of input will give a desired output?" or "Is it possible to get a desired output (under special input/output constraints)?". We will describe two methods of inverting a connectionist network. Firstly, we extend inversion via backpropagation (Linden/Kindermann [4], Williams [11]) to recurrent (El-man [1], Jordan [3], Mozer [5], Williams/Zipser [10]), time-delayed (Waibel at al. [9]) and discrete versions of continuous networks (Pineda [7], Pearlmutter [6]). The result of inversion is an input vector. The corresponding output vector is equal to the target vector except a small remainder. The knowledge of those attractors may help to understand the function and the generalization qualities of connectionist systems of this kind. Secondly, we introduce a new inversion method for proving the non-existence of an input combination under special constraints, e.g. in a subspace of the input space. This method works by iterative exclusion of invalid activation values. It might be a helpful way to judge the properties of a trained network. We conclude with simulation results of three different tasks: XOR, morse signal decoding and handwritten digit recognition. 
`.
Question: Which category should Paper #115188 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: Neural_Networks