8,162 research outputs found
A knowledge based system for linking information to support decision making in construction
This work describes the development of a project model centred on the information and knowledge generated and used by managers. It describes a knowledge-based system designed for this purpose. A knowledge acquisition exercise was undertaken to determine the tasks of project managers and the information necessary for and used by these tasks. This information was organised into a knowledge base for use by an expert system. The form of the knowledge lent itself to organisation into a link network. The structure of the knowledge-based system, which was developed, is outlined and its use described. Conclusions are drawn as to the applicability of the model and the final system. The work undertaken shows that it is feasible to benefit from the field of artificial intelligence to develop a project manager assistant computer program that utilises the benefit of information and its link
Mean-Field Games for Distributed Caching in Ultra-Dense Small Cell Networks
In this paper, the problem of distributed caching in dense wireless small
cell networks (SCNs) is studied using mean field games (MFGs). In the
considered SCN, small base stations (SBSs) are equipped with data storage units
and cooperate to serve users' requests either from files cached in the storage
or directly from the capacity-limited backhaul. The aim of the SBSs is to
define a caching policy that reduces the load on the capacity-limited backhaul
links. This cache control problem is formulated as a stochastic differential
game (SDG). In this game, each SBS takes into consideration the storage state
of the other SBSs to decide on the fraction of content it should cache. To
solve this problem, the formulated SDG is reduced to an MFG by considering an
ultra-dense network of SBSs in which the existence and uniqueness of the
mean-field equilibrium is shown to be guaranteed. Simulation results show that
this framework allows an efficient use of the available storage space at the
SBSs while properly tracking the files' popularity. The results also show that,
compared to a baseline model in which SBSs are not aware of the instantaneous
system state, the proposed framework increases the number of served files from
the SBSs by more than 69%.Comment: Accepted for publication at American Control Conference 201
Load Shifting in the Smart Grid: To Participate or Not?
Demand-side management (DSM) has emerged as an important smart grid feature
that allows utility companies to maintain desirable grid loads. However, the
success of DSM is contingent on active customer participation. Indeed, most
existing DSM studies are based on game-theoretic models that assume customers
will act rationally and will voluntarily participate in DSM. In contrast, in
this paper, the impact of customers' subjective behavior on each other's DSM
decisions is explicitly accounted for. In particular, a noncooperative game is
formulated between grid customers in which each customer can decide on whether
to participate in DSM or not. In this game, customers seek to minimize a cost
function that reflects their total payment for electricity. Unlike classical
game-theoretic DSM studies which assume that customers are rational in their
decision-making, a novel approach is proposed, based on the framework of
prospect theory (PT), to explicitly incorporate the impact of customer behavior
on DSM decisions. To solve the proposed game under both conventional game
theory and PT, a new algorithm based on fictitious player is proposed using
which the game will reach an epsilon-mixed Nash equilibrium. Simulation results
assess the impact of customer behavior on demand-side management. In
particular, the overall participation level and grid load can depend
significantly on the rationality level of the players and their risk aversion
tendency.Comment: 9 pages, 7 figures, journal, accepte
Dynamics of Learning with Restricted Training Sets I: General Theory
We study the dynamics of supervised learning in layered neural networks, in
the regime where the size of the training set is proportional to the number
of inputs. Here the local fields are no longer described by Gaussian
probability distributions and the learning dynamics is of a spin-glass nature,
with the composition of the training set playing the role of quenched disorder.
We show how dynamical replica theory can be used to predict the evolution of
macroscopic observables, including the two relevant performance measures
(training error and generalization error), incorporating the old formalism
developed for complete training sets in the limit as a
special case. For simplicity we restrict ourselves in this paper to
single-layer networks and realizable tasks.Comment: 39 pages, LaTe
On-Line Learning Theory of Soft Committee Machines with Correlated Hidden Units - Steepest Gradient Descent and Natural Gradient Descent -
The permutation symmetry of the hidden units in multilayer perceptrons causes
the saddle structure and plateaus of the learning dynamics in gradient learning
methods. The correlation of the weight vectors of hidden units in a teacher
network is thought to affect this saddle structure, resulting in a prolonged
learning time, but this mechanism is still unclear. In this paper, we discuss
it with regard to soft committee machines and on-line learning using
statistical mechanics. Conventional gradient descent needs more time to break
the symmetry as the correlation of the teacher weight vectors rises. On the
other hand, no plateaus occur with natural gradient descent regardless of the
correlation for the limit of a low learning rate. Analytical results support
these dynamics around the saddle point.Comment: 7 pages, 6 figure
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