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]: As a general and thus popular model for autonomous systems, partially observable Markov decision process (POMDP) can capture uncertainties from different sources like sensing noises, actuation errors, and uncertain environments. However, its comprehensiveness makes the planning and control in POMDP difficult. Traditional POMDP planning problems target to find the optimal policy to maximize the expectation of accumulated rewards. But for safety critical applications, guarantees of system performance described by formal specifications are desired, which motivates us to consider formal methods to synthesize supervisor for POMDP. With system specifications given by Probabilistic Computation Tree Logic (PCTL), we propose a supervisory control framework with a type of deterministic finite automata (DFA), za-DFA, as the controller form. While the existing work mainly relies on optimization techniques to learn fixed-size finite state controllers (FSCs), we develop an L∗ learning based algorithm to determine both space and transitions of za-DFA. Membership queries and different oracles for conjectures are defined. The learning algorithm is sound and complete. An example is given in detailed steps to illustrate the supervisor synthesis algorithm.
[A]: Supervisor Synthesis of POMDP based on Automata Learning


[Q]: Nowadays ontologies present a growing interest in Data Fusion applications. As a matter of fact, the ontologies are seen as a semantic tool for describing and reasoning about sensor data, objects, relations and general domain theories. In addition, uncertainty is perhaps one of the most important characteristics of the data and information handled by Data Fusion. However, the fundamental nature of ontologies implies that ontologies describe only asserted and veracious facts of the world. Different probabilistic, fuzzy and evidential approaches already exist to fill this gap; this paper recaps the most popular tools. However none of the tools meets exactly our purposes. Therefore, we constructed a Dempster-Shafer ontology that can be imported into any specific domain ontology and that enables us to instantiate it in an uncertain manner. We also developed a Java application that enables reasoning about these uncertain ontological instances.
[A]: Uncertainty in Ontologies: Dempster-Shafer Theory for Data Fusion Applications


[Q]: In this work, we explore a genre of puzzles (“image riddles”) which involves a set of images and a question. An-<lb>swering these puzzles require both capabilities involving vi-<lb>sual detection (including object, activity recognition) and,<lb>knowledge-based or commonsense reasoning. We compile<lb>a dataset of over 3k riddles where each riddle consists of 4<lb>images and a groundtruth answer. The annotations are validated using crowd-sourced evaluation. We also define an<lb>automatic evaluation metric to track future progress. Our<lb>task bears similarity with the commonly known IQ tasks<lb>such as analogy solving, sequence filling that are often used<lb>to test intelligence. We develop a Probabilistic Reasoning-based approach<lb>that utilizes probabilistic commonsense knowledge to an-<lb>swer these riddles with a reasonable accuracy. We demon-<lb>strate the results of our approach using both automatic and<lb>human evaluations. Our approach achieves some promising<lb>results for these riddles and provides a strong baseline for future attempts. We make the entire dataset and related ma-<lb>terials publicly available to the community in ImageRiddle<lb>Website (http://bit.ly/22f9Ala).
[A]:
Answering Image Riddles using Vision and Reasoning through Probabilistic Soft Logic