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.

[Q]: Encoding finite linear CSPs as Boolean formulas and solving them by using modern SAT solvers has proven to be highly effective, as exemplified by the award-winning sugar system. We here develop an alternative approach based on ASP. This allows us to use first-order encodings providing us with a high degree of flexibility for easy experimentation with different implementations. The resulting system aspartame re-uses parts of sugar for parsing and normalizing CSPs. The obtained set of facts is then combined with an ASP encoding that can be grounded and solved by off-the-shelf ASP systems. We establish the competitiveness of our approach by empirically contrasting aspartame and sugar.
[A]: Aspartame: Solving Constraint Satisfaction Problems with Answer Set Programming


[Q]: 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.
[A]: Artificial Decision Making Under Uncertainty in Intelligent Buildings


[Q]: The problem of sparse rewards is one of the hardest challenges in contemporary reinforcement learning. Hierarchical reinforcement learning (HRL) tackles this problem by using a set of temporally-extended actions, or options, each of which has its own subgoal. These subgoals are normally handcrafted for specific tasks. Here, though, we introduce a generic class of subgoals with broad applicability in the visual domain. Underlying our approach (in common with work using “auxiliary tasks”) is the hypothesis that the ability to control aspects of the environment is an inherently useful skill to have. We incorporate such subgoals in an end-to-end hierarchical reinforcement learning system and test two variants of our algorithm on a number of games from the Atari suite. We highlight the advantage of our approach in one of the hardest games – Montezuma’s revenge – for which the ability to handle sparse rewards is key. Our agent learns several times faster than the current state-of-the-art HRL agent in this game, reaching a similar level of performance.
[A]:
Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning