In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

Input: Consider Input: Sequential decision problems for real-world applications often need to be solved in real-time, requiring algorithms to perform well with a restricted computational budget. Width-based lookaheads have shown state-of-the-art performance in classical planning problems as well as over the Atari games with tight budgets. In this work we investigate width-based lookaheads over Stochastic Shortest paths (SSP). We analyse why width-based algorithms perform poorly over SSP problems, and overcome these pitfalls proposing a method to estimate costs-to-go. We formalize width-based lookaheads as an instance of the rollout algorithm, give a definition of width for SSP problems and explain its sample complexity. Our experimental results over a variety of SSP benchmarks show the algorithm to outperform other state-of-the-art rollout algorithms such as UCT and RTDP.

Output: We propose a new Monte Carlo Tree Search / rollout algorithm that relies on width-based search to construct a lookahead.


Input: Consider Input: Common-sense physical reasoning is an essential ingredient for any intelligent agent operating in the real-world. For example, it can be used to simulate the environment, or to infer the state of parts of the world that are currently unobserved. In order to match real-world conditions this causal knowledge must be learned without access to supervised data. To address this problem we present a novel method that learns to discover objects and model their physical interactions from raw visual images in a purely unsupervised fashion. It incorporates prior knowledge about the compositional nature of human perception to factor interactions between object-pairs and learn efficiently. On videos of bouncing balls we show the superior modelling capabilities of our method compared to other unsupervised neural approaches that do not incorporate such prior knowledge. We demonstrate its ability to handle occlusion and show that it can extrapolate learned knowledge to scenes with different numbers of objects.

Output: We introduce a novel approach to common-sense physical reasoning that learns to discover objects and model their physical interactions from raw visual images in a purely unsupervised fashion


Input: Consider Input: Automatic classification of objects is one of the most important tasks in engineering
 and data mining applications. Although using more complex and advanced
 classifiers can help to improve the accuracy of classification systems, it can be
 done by analyzing data sets and their features for a particular problem. Feature
 combination is the one which can improve the quality of the features. In this paper,
 a structure similar to Feed-Forward Neural Network (FFNN) is used to generate an
 optimized linear or non-linear combination of features for classification. Genetic
 Algorithm (GA) is applied to update weights and biases. Since nature of data sets
 and their features impact on the effectiveness of combination and classification
 system, linear and non-linear activation functions (or transfer function) are used
 to achieve more reliable system. Experiments of several UCI data sets and using
 minimum distance classifier as a simple classifier indicate that proposed linear and
 non-linear intelligent FFNN-based feature combination can present more reliable
 and promising results. By using such a feature combination method, there is no
 need to use more powerful and complex classifier anymore.


Output: A method for enriching and combining features to improve classification accuracy