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]: Many real-world decision-theoretic planning problems can be naturally modeled with discrete and continuous state Markov decision processes (DC-MDPs). While previous work has addressed automated decision-theoretic planning for DCMDPs, optimal solutions have only been defined so far for limited settings, e.g., DC-MDPs having hyper-rectangular piecewise linear value functions. In this work, we extend symbolic dynamic programming (SDP) techniques to provide optimal solutions for a vastly expanded class of DCMDPs. To address the inherent combinatorial aspects of SDP, we introduce the XADD — a continuous variable extension of the algebraic decision diagram (ADD) — that maintains compact representations of the exact value function. Empirically, we demonstrate an implementation of SDP with XADDs on various DC-MDPs, showing the first optimal automated solutions to DCMDPs with linear and nonlinear piecewise partitioned value functions and showing the advantages of constraint-based pruning for XADDs.
[EX A]: Symbolic Dynamic Programming for Discrete and Continuous State MDPs

[EX Q]: Recently, sequence-to-sequence model by using encoder-decoder neural network has gained popularity for automatic speech recognition (ASR). The architecture commonly uses an attentional mechanism which allows the model to learn alignments between source speech sequence and target text sequence. Most attentional mechanisms used today is based on a global attention property which requires a computation of a weighted summarization of the whole input sequence generated by encoder states. However, it is computationally expensive and often produces misalignment on the longer input sequence. Furthermore, it does not fit with monotonous or left-to-right nature in speech recognition task. In this paper, we propose a novel attention mechanism that has local and monotonic properties. Various ways to control those properties are also explored. Experimental results demonstrate that encoder-decoder based ASR with local monotonic attention could achieve significant performance improvements and reduce the computational complexity in comparison with the one that used the standard global attention architecture.
[EX A]: Local Monotonic Attention Mechanism for End-to-End Speech Recognition

[EX Q]: The problem of solving (n − 1)-puzzle and cooperative path-finding (CPF) sub-optimally by rule based algorithms is addressed in this manuscript. The task in the puzzle is to rearrange n − 1 pebbles on the square grid of the size of n×n using one vacant position to a desired goal configuration. An improvement to the existent polynomial-time algorithm is proposed and experimentally analyzed. The improved algorithm is trying to move pebbles in a more efficient way than the original algorithm by grouping them into so-called snakes and moving them jointly within the snake. An experimental evaluation showed that the algorithm using snakes produces solutions that are 8% to 9% shorter than solutions generated by the original algorithm. The snake-based relocation has been also integrated into rule-based algorithms for solving the CPF problem sub-optimally, which is a closely related task. The task in CPF is to relocate a group of abstract robots that move over an undirected graph to given goal vertices. Robots can move to unoccupied neighboring vertices and at most one robot can be placed in each vertex. The (n − 1)-puzzle is a special case of CPF where the underlying graph is represented by a 4-connected grid and there is only one vacant vertex. Two major rule-based algorithms for CPF were included in our study – BIBOX and PUSH-and-SWAP (PUSH-and-ROTATE). Improvements gained by using snakes in the BIBOX algorithm were stable around 30% in (n − 1)-puzzle solving and up to 50% in CPFs over bi-connected graphs with various ear decompositions and multiple vacant vertices. In the case of the PUSHand-SWAP algorithm the improvement achieved by snakes was around 5% to 8%. However, the improvement was unstable and hardly predictable in the case of PUSH-and-SWAP.
[EX A]:
Improvements in Sub-optimal Solving of the (N − 1)-Puzzle via Joint Relocation of Pebbles and its Applications to Rule-based Cooperative Path-Finding