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.

[EX Q]: Reinforcement learning systems are often concerned with balancing exploration of untested actions against exploitation of actions that are known to be good. The benefit of exploration can be estimated using the classi­ cal notion of Value of Informationthe expected im­ provement in future decision quality arising from the tnformation acquired by exploration. Estimating this quantity requires an assessment of the agent's uncer­ tainty about its current value estimates for states. In this paper we investigate ways to represent and rea­ son about this uncertainty in algorithms where the sys­ tem attempts to learn a model of its environment. We explicitly represent uncertainty about the parameters of the model and build probability distributions over Q­ values based on these. These distributions are used to compute a myopic approximation to the value of infor­ mation for each action and hence to select the action that best balances exploration and exploitation.
[EX A]: Model based Bayesian Exploration

[EX Q]: Formal languages for probabilistic modeling enable re-use, modularity, and descriptive clarity, and can foster generic inference techniques. We introduce Church, a universal language for describing stochastic generative processes. Church is based on the Lisp model of lambda calculus, containing a pure Lisp as its deterministic subset. The semantics of Church is defined in terms of evaluation histories and conditional distributions on such histories. Church also includes a novel language construct, the stochastic memoizer, which enables simple description of many complex non-parametric models. We illustrate language features through several examples, including: a generalized Bayes net in which parameters cluster over trials, infinite PCFGs, planning by inference, and various non-parametric clustering models. Finally, we show how to implement query on any Church program, exactly and approximately, using Monte Carlo techniques.
[EX A]: Church: a language for generative models

[EX Q]: This report presents a plan to develop an intelligent Meta search engine,<lb>iral. This Meta search engine is aimed to provide comprehensive, efficient and<lb>relevant search results for given queries by combining results from different search<lb>engines. This will provide users to cover a larger database of World Wide Web for<lb>their queries and to get more relevant search results. On the other hand this Meta<lb>search engine will be creating a larger user base and attracting online marketing for<lb>Abster-iT. This will provide a new business set-up for Abster-iT.
[EX A]:
Intelligent Search Optimization using Artificial fuzzy logic