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.

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.
Artificial Decision Making Under Uncertainty in Intelligent Buildings

In natural speech, the speaker does not pause between words, yet a human listener somehow perceives this continuous stream of phonemes as a series of distinct words. The detection of boundaries between spoken words is an instance of a general capability of the human neocortex to remember and to recognize recurring sequences. This paper describes a computer algorithm that is designed to solve the problem of locating word boundaries in blocks of English text from which the spaces have been removed. This problem avoids the complexities of processing speech but requires similar capabilities for detecting recurring sequences. The algorithm that is described in this paper relies entirely on statistical relationships between letters in the input stream to infer the locations of word boundaries. The source code for a C++ version of this algorithm is presented in an appendix.
A Statistical Learning Algorithm for Word Segmentation

We investigate the use of temporally abstract actions, or macro-actions, in the solution of Markov decision processes. Unlike current mod els that combine both primitive actions and macro-actions and leave the state space un changed, we propose a hierarchical model (using an abstract MDP) that works with macro-actions only, and that significantly reduces the size of the state space. This is achieved by treating macro actions as local policies that act in certain regions of state space, and by restricting states in the ab stract MDP to those at the boundaries of regions. The abstract MDP approximates the original and can be solved more efficiently. We discuss sev eral ways in which macro-actions can be gen erated to ensure good solution quality. Finally, we consider ways in which macro-actions can be reused to solve multiple, related MDPs; and we show that this can justify the computational over head of macro-action generation.
Hierarchical Solution of Markov Decision Processes using Macro-actions