instruction:
In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
question:
This paper shows how Long Short-term Memory recurrent neural networks can be used to generate complex sequences with long-range structure, simply by predicting one data point at a time. The approach is demonstrated for text (where the data are discrete) and online handwriting (where the data are real-valued). It is then extended to handwriting synthesis by allowing the network to condition its predictions on a text sequence. The resulting system is able to generate highly realistic cursive handwriting in a wide variety of styles.
answer:
Generating Sequences With Recurrent Neural Networks

question:
Levels are a key component of many different video games, and a large body of work has been produced on how to procedurally generate game levels. Recently, Machine Learning techniques have been applied to video game level generation towards the purpose of automatically generating levels that have the properties of the training corpus. Towards that end we have made available a corpora of video game levels in an easy to parse format ideal for different machine learning and other game AI research purposes.
answer:
The VGLC: The Video Game Level Corpus

question:
Reactive (memoryless) policies are sufficient in completely observable Markov decision pro cesses (MDPs), but some kind of memory is usually necessary for optimal control of a par tially observable MDP. Policies with finite mem ory can be represented as finite-state automata. In this paper, we extend Baird and Moore's YAPS algorithm to the problem of learning gen eral finite-state automata. Because it performs stochastic gradient descent, this algorithm can be shown to converge to a locally optimal finite state controller. We provide the details of the algorithm and then consider the question of un der what conditions stochastic gradient descent will outperform exact gradient descent. We con clude with empirical results comparing the per formance of stochastic and exact gradient de scent, and showing the ability of our algorithm to extract the useful information contained in the sequence of past observations to compensate for the lack of observability at each time-step.
answer:
Learning Finite-State Controllers for Partially Observable Environments