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: The weighted Maximum Satisfiability problem (weighted MAX-SAT) is a NP-hard problem with numerous applications arising in artificial intelligence. As an efficient tool for heuristic design, the backbone has been applied to heuristics design for many NP-hard problems. In this paper, we investigated the computational complexity for retrieving the backbone in weighted MAX-SAT and developed a new algorithm for solving this problem. We showed that it is intractable to retrieve the full backbone under the assumption that NP P . Moreover, it is intractable to retrieve a fixed fraction of the backbone as well. And then we presented a backbone guided local search (BGLS) with Walksat operator for weighted MAX-SAT. BGLS consists of two phases: the first phase samples the backbone information from local optima and the backbone phase conducts local search under the guideline of backbone. Extensive experimental results on the benchmark showed that BGLS outperforms the existing heuristics in both solution quality and runtime.

A: Approximating the Backbone in the Weighted Maximum Satisfiability Problem
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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
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Q: Multicriteria decision analysis aims at supporting a person facing a decision problem involving conflicting criteria. We consider an additive utility model which provides robust conclusions based on preferences elicited from the decision maker. The recommendations based on these robust conclusions are even more convincing if they are complemented by explanations. We propose a general scheme, based on sequence of preference swaps, in which explanations can be computed. We show first that the length of explanations can be unbounded in the general case. However, in the case of binary reference scales, this length is bounded and we provide an algorithm to compute the corresponding explanation.

A:
Explaining robust additive utility models by sequences of preference swaps
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