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.

Input: Consider Input: The approach described here allows to use the fuzzy Object Based Representation of imprecise and uncertain knowledge. This representation has a great practical interest due to the possibility to realize reasoning on classification with a fuzzy semantic network based system. For instance, the distinction between necessary, possible and user classes allows to take into account exceptions that may appear on fuzzy knowledge-base and facilitates integration of user's Objects in the base. This approach describes the theoretical aspects of the architecture of the whole experimental A.I. system we built in order to provide effective on-line assistance to users of new technological systems: the understanding of "how it works" and "how to complete tasks" from queries in quite natural languages. In our model, procedural semantic networks are used to describe the knowledge of an "ideal" expert while fuzzy sets are used both to describe the approximative and uncertain knowledge of novice users in fuzzy semantic networks which intervene to match fuzzy labels of a query with categories from our "ideal" expert.

Output: Uncertain and Approximative Knowledge Representation to Reasoning on Classification with a Fuzzy Networks Based System


Input: Consider Input: We provide a general framework for computing lower-bounds on the sample complexity of recovering the underlying graphs of Ising models, given i.i.d. samples. While there have been recent results for specific graph classes, these involve fairly extensive technical arguments that are specialized to each specific graph class. In contrast, we isolate two key graph-structural ingredients that can then be used to specify sample complexity lower-bounds. Presence of these structural properties makes the graph class hard to learn. We derive corollaries of our main result that not only recover existing recent results, but also provide lower bounds for novel graph classes not considered previously. We also extend our framework to the random graph setting and derive corollaries for Erdős-Rényi graphs in a certain dense setting.

Output: On the Information Theoretic Limits of Learning Ising Models


Input: Consider Input: We outline a method to estimate the value of computation for a flexible algorithm using em­ pirical data. To determine a reasonable trade-off between cost and value, we build an empirical model of the value obtained through computa­ tion, and apply this model to estimate the value of computation for quite different problems. In par­ ticular, we investigate this trade-off for the prob­ lem of constructing policies for decision prob­ lems represented as influence diagrams. We show how two features of our anytime algorithm pro­ vide reasonable estimates of the value of compu­ tation in this domain.


Output: Estimating the Value of Computation in Flexible Information Refinement