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: 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: Passengers’ experience is becoming a key metric to evaluate the air transportation system’s performance. Efficient and robust tools to handle airport operations are needed along with a better understanding of passengers’ interests and concerns. Among various airport operations, this paper studies airport gate scheduling for improved passengers’ experience. Three objectives accounting for passengers, aircraft, and operation are presented. Trade-offs between these objectives are analyzed, and a balancing objective function is proposed. The results show that the balanced objective can improve the efficiency of traffic flow in passenger terminals and on ramps, as well as the robustness of gate operations.

A: Airport Gate Scheduling for Passengers, Aircraft, and Operation
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Q: In this work we present a new approach to the treatment of property graphs using neural encoding techniques derived from machine learning. Specifically, we will deal with the problem of embedding property graphs in vector spaces. Throughout this paper we will use the term embedding as an operation that allows to consider a mathematical structure, X, inside another structure Y , through a function, f : X → Y . We are interested on embeddings capable of capturing, within the characteristics of a vector space (distance, linearity, clustering, etc.), the interesting features of the graph. For example, it would be interesting to get embeddings that, when projecting the nodes of the graph into points of a vector space, keep edges with the same type of the graph into the same vectors. In this way, we can interpret that the semantic associated to the relation has been captured by the embedding. Another option is to check if the embedding verifies clustering properties with respect to the types of nodes, types of edges, properties, or some of the metrics that can be measured on the graph. Subsequently, we will use these good embedding features to try to obtain prediction / classification / discovery tools on the original graph. This paper is structured as follows: we will start by giving some preliminary definitions necessary for the presentation of our proposal and a brief introduction to the use of artificial neural networks as encoding machines. After this review, we will present our embedding proposal based on neural encoders, and we will verify if the topological and semantic characteristics of the original graph have been maintained in the new representation. After evaluating the properties of the new representation, it will be used to carry out machine learning and discovery tasks on real databases. Finally, we will present some conclusions and future work proposals that have arisen during the implementation of this work.

A:
Semantic Preserving Embeddings for Generalized Graphs
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