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.

Example Input: The interest in the combination of probability with logics for modeling the world has rapidly increased in the last few years. One of the most effective approaches is the Distribution Semantics which was adopted by many logic programming languages and in Descripion Logics. In this paper, we illustrate the work we have done in this research field by presenting a probabilistic semantics for description logics and reasoning and learning algorithms. In particular, we present in detail the system TRILL , which computes the probability of queries w.r.t. probabilistic knowledge bases, which has been implemented in Prolog. Note: An extended abstract / full version of a paper accepted to be presented at the Doctoral Consortium of the 30th International Conference on Logic Programming (ICLP 2014), July 19-22, Vienna, Austria
Example Output: Reasoning with Probabilistic Logics

Example Input: We describe how to use propositional model counting for a quantitative analysis of product configuration data. Our approach computes valuable meta information such as the total number of valid configurations or the relative frequency of components. This information can be used to assess the severity of documentation errors or to measure documentation quality. As an application example we show how we apply these methods to product documentation formulas of the Mercedes-Benz line of vehicles. In order to process these large formulas we developed and implemented a new model counter for non-CNF formulas. Our model counter can process formulas, whose CNF representations could not be processed up till now.
Example Output: Model Counting in Product Configuration

Example Input: Classical higher-order logic, when utilized as a meta-logic in which various other (classical and non-classical) logics can be shallowly embedded, is well suited for realising a universal logic reasoning approach. Universal logic reasoning in turn, as envisioned already by Leibniz, may support the rigorous formalisation and deep logical analysis of rational arguments within machines. A respective universal logic reasoning framework is described and a range of exemplary applications are discussed. In the future, universal logic reasoning in combination with appropriate, controlled forms of rational argumentation may serve as a communication layer between humans and intelligent machines. 1 Rational Argumentation – Communication Interface between Humans and Machines The ambition to understand, model and implement rational argumentation and universal logical reasoning independent of the human brain has a long tradition in the history of humankind. It reaches back at least to the prominent study of syllogistic arguments by Aristoteles. Today, with the event of increasingly intelligent computer technology, the question is more topical than ever: if humans and intelligent machines are supposed to amicably coexists, interact and collaborate, appropriate forms of communication between them are required. For example, machines should be able to depict, assess and defend their (options for) actions and decisions in a form that is accessible to human understanding and judgement. This will be crucial for achieving a reconcilable and socially accepted integration of intelligent machines into everyday (human) life. The communication means between machines and humans should ideally be based on human-level, rational argumentation, which since ages forms the fundament of our social, juridical and scientific processes. Current developments in artificial intelligence, in contrast, put a strong focus on statistical information, machine learning and subsymbolic representations, all of which are rather detached from human-level rational explanation, understanding and judgement. The challenge thus is to complement and enhance these human-unfriendly forms of reasoning and knowledge representation in todays artificial intelligence systems with suitable explanations amenable to human cognition, that is, rational arguments. Via exchange of rational arguments at human-intuitive level the much needed mutual understanding and acceptance between humans and intelligent machines can eventually be guaranteed. This is particularly relevant for the assessment of machine actions in terms of legal, ethical, moral, social and cultural norms purported by humans. But what formalisms are available that could serve as a most general basis for the modeling of human-level rational arguments in machines?
Example Output:
Universal Reasoning, Rational Argumentation and Human-Machine Interaction