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.
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Question: Cooperative games model the allocation of profit from joint actions, following considerations such as stability and fairness. We propose the reliability extension of such games, where agents may fail to participate in the game. In the reliability extension, each agent only "survives" with a certain probability, and a coalition's value is the probability that its surviving members would be a winning coalition in the base game. We study prominent solution concepts in such games, showing how to approximate the Shapley value and how to compute the core in games with few agent types. We also show that applying the reliability extension may stabilize the game, making the core non-empty even when the base game has an empty core.

Answer: Solving Cooperative Reliability Games

Question: There is a brief description of the probabilistic causal graph model for representing, reasoning with, and learn ing causal structure using Bayesian networks. It is then argued that this model is closely related to how humans reason with and learn causal structure. It is shown that studies in psychology on discounting (reasoning concern ing how the presence of one cause of an effect makes an other cause less probable) support the hypothesis that humans reach the same judgments as algorithms for do ing inference in Bayesian networks. Next, it is shown how studies by Piaget indicate that humans learn causal structure by observing the same independencies and de pendencies as those used by certain algorithms for learn ing the structure of a Bayesian network. Based on this indication, a subjective definition of causality is for warded. Finally, methods for further testing the accu racy of these claims are discussed.

Answer: THE CoGNITIVE PROCESSING OF CAUSAL KNOWLEDGE

Question: This paper takes an information visualization perspective to visual representations in the general SOM paradigm. This involves viewing SOM-based visualizations through the eyes of Bertin's and Tufte's theories on data graphics. The regular grid shape of the Self-Organizing Map (SOM), while being a virtue for linking visualizations to it, restricts representation of cluster structures. From the viewpoint of information visualization, this paper provides a general, yet simple, solution to projection-based coloring of the SOM that reveals structures. First, the proposed color space is easy to construct and customize to the purpose of use, while aiming at being perceptually correct and informative through two separable dimensions. Second, the coloring method is not dependent on any specific method of projection, but is rather modular to fit any objective function suitable for the task at hand. The cluster coloring is illustrated on two datasets: the iris data, and welfare and poverty indicators. KeywordsSelf-Organizing Maps (SOMs); cluster coloring;

Answer:
Cluster coloring of the Self-Organizing Map: An information visualization perspective