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.

In this paper we present an interface between a symbolic planner and a geometric task planner, which is different to a standard trajectory planner in that the former is able to perform geometric reasoning on abstract entities—tasks. We believe that this approach facilitates a more principled interface to symbolic planning, while also leaving more room for the geometric planner to make independent decisions. We show how the two planners could be interfaced, and how their planning and backtracking could be interleaved. We also provide insights for a methodology for using the combined system, and experimental results to use as a benchmark with future extensions to both the combined system, as well as to the geometric task planner.
Towards Combining HTN Planning and Geometric Task Planning

The Internet and online forums such as Reddit have become an increasingly popular medium for citizens to engage in political conversations. However, the online disinhibition effect resulting from the ability to use pseudonymous identities may manifest in the form of offensive speech, consequently making political discussions more aggressive and polarizing than they already are. Such environments may result in harassment and self-censorship from its targets. In this paper, we present preliminary results from a large-scale temporal measurement aimed at quantifying offensiveness in online political discussions. To enable our measurements, we develop and evaluate an offensive speech classifier. We then use this classifier to quantify and compare offensiveness in the political and general contexts. We perform our study using a database of over 168M Reddit comments made by over 7M pseudonyms between January 2015 and January 2017 – a period covering several divisive political events including the 2016 US presidential elections.
Measuring Offensive Speech in Online Political Discourse

Probabilistic independence can dramatically sim­ plify the task of eliciting, representing, and com­ puting with probabilities in large domains. A key technique in achieving these benefits is the idea of graphical modeling. We survey existing no­ tions of independence for utility functions in a multi-attribute space, and suggest that these can be used to achieve similar advantages. Our new results concern conditional additive in­ dependence, which we show always has a per­ fect representation as separation in an undirected graph (a Markov network). Conditional addi­ tive independencies entail a particular functional form for the utility function that is analogous to a product decomposition of a probability function, and confers analogous benefits. This functional form has been utilized in the Bayesian network and influence diagram literature, but generally without an explanation in terms of independence. The functional form yields a decomposition of the utility function that can greatly speed up expected utility calculations, particularly when the utility graph has a similar topology to the probabilistic network being used.

Graphical models for preference and utility