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.

[EX Q]: We investigate the use of temporally abstract actions, or macro-actions, in the solution of Markov decision processes. Unlike current mod els that combine both primitive actions and macro-actions and leave the state space un changed, we propose a hierarchical model (using an abstract MDP) that works with macro-actions only, and that significantly reduces the size of the state space. This is achieved by treating macro actions as local policies that act in certain regions of state space, and by restricting states in the ab stract MDP to those at the boundaries of regions. The abstract MDP approximates the original and can be solved more efficiently. We discuss sev eral ways in which macro-actions can be gen erated to ensure good solution quality. Finally, we consider ways in which macro-actions can be reused to solve multiple, related MDPs; and we show that this can justify the computational over head of macro-action generation.
[EX A]: Hierarchical Solution of Markov Decision Processes using Macro-actions

[EX Q]: Determining semantic textual similarity is a core research subject in natural language processing. Since vector-based models for sentence representation often use shallow information, capturing accurate semantics is difficult. By contrast, logical semantic representations capture deeper levels of sentence semantics, but their symbolic nature does not offer graded notions of textual similarity. We propose a method for determining semantic textual similarity by combining shallow features with features extracted from natural deduction proofs of bidirectional entailment relations between sentence pairs. For the natural deduction proofs, we use ccg2lambda, a higherorder automatic inference system, which converts Combinatory Categorial Grammar (CCG) derivation trees into semantic representations and conducts natural deduction proofs. Experiments show that our system was able to outperform other logicbased systems and that features derived from the proofs are effective for learning textual similarity.
[EX A]: Determining Semantic Textual Similarity using Natural Deduction Proofs

[EX Q]: We describe a graphical representation of probabilistic relationships-an alternative to the Bayesian network-called a dependency network. Like a Bayesian network, a depen dency network has a graph and a probabil ity component. The graph component is a (cyclic) directed graph such that a node's parents render that node independent of all other nodes in the network. The probabil ity component consists of the probability of a node given its parents for each node (as in a Bayesian network) . We identify several ba sic properties of this representation, and de scribe its use in collaborative filtering (the task of predicting preferences) and the visu alization of predictive relationships.
[EX A]:
Dependency Networks for Collaborative Filtering and Data Visualization