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.

The First-Order Variable Elimination (FOVE) algorithm allows exact inference to be applied directly to probabilistic relational models, and has proven to be vastly superior to the application of standard inference methods on a grounded propositional model. Still, FOVE operators can be applied under restricted conditions, often forcing one to resort to propositional inference. This paper aims to extend the applicability of FOVE by providing two new model conversion operators: the first and the primary is joint formula conversion and the second is just-different counting conversion. These new operations allow efficient inference methods to be applied directly on relational models, where no existing efficient method could be applied hitherto. In addition, aided by these capabilities, we show how to adapt FOVE to provide exact solutions to Maximum Expected Utility (MEU) queries over relational models for decision under uncertainty. Experimental evaluations show our algorithms to provide significant speedup over the alternatives.
Extended Lifted Inference with Joint Formulas

We present a method for solving implicit (factored) Markov decision processes (MDPs) with very large state spaces. We intro­ duce a property of state space partitions which we call f-homogeneity. Intuitively, an f-homogeneous partition groups together states that behave approximately the same under all or some subset of policies. Borrow­ ing from recent work on model minimization in computer-aided software verification, we present an algorithm that takes a factored representation of an MDP and an 0 � f � I and computes a factored f-homogeneous par­ tition of the state space. This partition defines a family of related MOPs-those MOP's with state space equal to the blocks of the partition, and transition probabilities "appro:X:imately" like those of any (original MDP) state in the source block. To formally study such families of MDPs, we introduce the new notion of a "bounded parameter MDP" (BMDP), which is a fam­ ily of (traditional) MOPs defined by speci­ fying upper and lower bounds on the transi­ tion probabilities and rewards. We describe algorithms that operate on BMDPs to find policies that are approximately optimal with respect to the original MDP. In combination, our method for reducing a large implicit MDP to a possibly much smaller BMDP using an f-homogeneous par­ tition, and our methods for selecting actions in BMDP's constitute a new approach for an­ alyzing large implicit MOP's. Among its ad­ vantages, this new approach provides insight into existing algorithms to solving implicit MDPs, provides useful connections to work in automata theory and model minimization, and suggests methods, which involve vary­ ing f, to trade time and space (specifically in terms of the size of the corresponding state space) for solution quality.
Model Reduction Techniques for Computing Approximately Optimal Solutions for Markov Decision Processes

Weather affects our mood and behaviors, and many aspects of our life. When it is sunny, most people become happier; but when it rains, some people get depressed. Despite this evidence and the abundance of data, weather has mostly been overlooked in the machine learning and data science research. This work presents a causal analysis of how weather affects TV watching patterns. We show that some weather attributes, such as pressure and precipitation, cause major changes in TV watching patterns. To the best of our knowledge, this is the first large-scale causal study of the impact of weather on TV watching patterns.

Does Weather Matter? Causal Analysis of TV Logs