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.

Q: We present a method for solving implicit (factored) Markov decision processes (MDPs) with very large state spaces. We intro­ duce a property of state space partitions which we call f-homogeneity. Intuitively, an f-homogeneous partition groups together states that behave approximately the same under all or some subset of policies. Borrow­ ing from recent work on model minimization in computer-aided software verification, we present an algorithm that takes a factored representation of an MDP and an 0 � f � I and computes a factored f-homogeneous par­ tition of the state space. This partition defines a family of related MOPs-those MOP's with state space equal to the blocks of the partition, and transition probabilities "appro:X:imately" like those of any (original MDP) state in the source block. To formally study such families of MDPs, we introduce the new notion of a "bounded parameter MDP" (BMDP), which is a fam­ ily of (traditional) MOPs defined by speci­ fying upper and lower bounds on the transi­ tion probabilities and rewards. We describe algorithms that operate on BMDPs to find policies that are approximately optimal with respect to the original MDP. In combination, our method for reducing a large implicit MDP to a possibly much smaller BMDP using an f-homogeneous par­ tition, and our methods for selecting actions in BMDP's constitute a new approach for an­ alyzing large implicit MOP's. Among its ad­ vantages, this new approach provides insight into existing algorithms to solving implicit MDPs, provides useful connections to work in automata theory and model minimization, and suggests methods, which involve vary­ ing f, to trade time and space (specifically in terms of the size of the corresponding state space) for solution quality.

A: Model Reduction Techniques for Computing Approximately Optimal Solutions for Markov Decision Processes
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Q: Encoding finite linear CSPs as Boolean formulas and solving them by using modern SAT solvers has proven to be highly effective, as exemplified by the award-winning sugar system. We here develop an alternative approach based on ASP. This allows us to use first-order encodings providing us with a high degree of flexibility for easy experimentation with different implementations. The resulting system aspartame re-uses parts of sugar for parsing and normalizing CSPs. The obtained set of facts is then combined with an ASP encoding that can be grounded and solved by off-the-shelf ASP systems. We establish the competitiveness of our approach by empirically contrasting aspartame and sugar.

A: Aspartame: Solving Constraint Satisfaction Problems with Answer Set Programming
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Q: The explosive availability of remote sensing im-<lb>ages has challenged supervised classification algorithms such as<lb>Support Vector Machines (SVM), as training samples tend to<lb>be highly limited due to the expensive and laborious task of<lb>ground truthing. The temporal correlation and spectral similarity<lb>between multitemporal images have opened up an opportunity<lb>to alleviate this problem. In this study, a SVM-based Sequen-<lb>tial Classifier Training (SCT-SVM) approach is proposed for<lb>multitemporal remote sensing image classification. The approach<lb>leverages the classifiers of previous images to reduce the required<lb>number of training samples for the classifier training of an<lb>incoming image. For each incoming image, a rough classifier<lb>is firstly predicted based on the temporal trend of a set of<lb>previous classifiers. The predicted classifier is then fine-tuned<lb>into a more accurate position with current training samples. This<lb>approach can be applied progressively to sequential image data,<lb>with only a small number of training samples being required<lb>from each image. Experiments were conducted with Sentinel-2A<lb>multitemporal data over an agricultural area in Australia. Results<lb>showed that the proposed SCT-SVM achieved better classification<lb>accuracies compared with two state-of-the-art model transfer<lb>algorithms. Compared with those obtained without the assistance<lb>from previous images, the overall classification accuracy was<lb>improved from 76.2% to 93.8%, demonstrating that the lever-<lb>age of a priori information from previous images can provide<lb>advantageous assistance for later images in multitemporal image<lb>classification.

A:
Effective Sequential Classifier Training for Multitemporal Remote Sensing Image Classification
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