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.

Bayesian networks can be used to extract explanations about the observed state of a subset of variables. In this paper, we explicate the desiderata of an explanation and confront them with the concept of explanation proposed by existing methods. The necessity of taking into account causal approaches when a causal graph is available is discussed. We then introduce causal explanation trees, based on the construction of explanation trees using the measure of causal information ow (Ay and Polani, 2006). This approach is compared to several other methods on known networks.
Explanation Trees for Causal Bayesian Networks

Our hypothesis is that by equipping certain agents in a multi-agent system controlling an intelligent building with automated decision support, two important factors will be in­ creased. The first is energy saving in the building. The second is customer value-how the people in the building experience the ef­ fects of the actions of the agents. We give evi­ dence for the truth of this hypothesis through experimental findings related to tools for arti­ ficial decision making. A number of assump­ tions related to agent control, through moni­ toring and delegation of tasks to other kinds of agents, of rooms at a test site are relaxed. Each assumption controls at least one un­ certainty that complicates considerably the procedures for selecting actions part of each such agent. We show that in realistic deci­ sion situations, room-controlling agents can make bounded rational decisions even under dynamic real-time constraints. This result can be, and has been, generalized to other domains with even harsher time constraints.
Artificial Decision Making Under Uncertainty in Intelligent Buildings

More and more of the information on the web is dialogic, from Facebook newsfeeds, to forum conversations, to comment threads on news articles. In contrast to traditional, monologic resources such as news, highly social dialogue is very frequent in social media, as illustrated in the snippets in Fig. 1 from the publicly available Internet Argument Corpus (IAC) (Walker et al., 2012). Utterances are frequently sarcastic, e.g., Really? Well, when I have a kid, I’ll be sure to just leave it in the woods, since it can apparently care for itself (R2 in Fig. 1 as well as Q1 and R1), and are often nasty, (R2 in Fig. 1). Note also the frequent use of dialogue specific discourse cues, e.g. the use of No in R1, Really? Well in R2, and okay, well in Q3 in Fig. 1 (Fox Tree and Schrock, 1999; Bryant and Fox Tree, 2002; Fox Tree, 2010).

Identifying Subjective and Figurative Language in Online Dialogue