3,592 research outputs found
Ethnographic Causality
This book explores the problem of causal inference when a sufficient number of comparative cases cannot be found, which would permit the application of frequency based models formulated in terms of explanatory causal generalizations
A Bayesian Abduction Model For Sensemaking
This research develops a Bayesian Abduction Model for Sensemaking Support (BAMSS) for information fusion in sensemaking tasks. Two methods are investigated. The first is the classical Bayesian information fusion with belief updating (using Bayesian clustering algorithm) and abductive inference. The second method uses a Genetic Algorithm (BAMSS-GA) to search for the k-best most probable explanation (MPE) in the network. Using various data from recent Iraq and Afghanistan conflicts, experimental simulations were conducted to compare the methods using posterior probability values which can be used to give insightful information for prospective sensemaking. The inference results demonstrate the utility of BAMSS as a computational model for sensemaking. The major results obtained are: (1) The inference results from BAMSS-GA gave average posterior probabilities that were 103 better than those produced by BAMSS; (2) BAMSS-GA gave more consistent posterior probabilities as measured by variances; and (3) BAMSS was able to give an MPE while BAMSS-GA was able to identify the optimal values for kMPEs. In the experiments, out of 20 MPEs generated by BAMSS, BAMSS-GA was able to identify 7 plausible network solutions resulting in less amount of information needed for sensemaking and reducing the inference search space by 7/20 (35%). The results reveal that GA can be used successfully in Bayesian information fusion as a search technique to identify those significant posterior probabilities useful for sensemaking. BAMSS-GA was also more robust in overcoming the problem of bounded search that is a constraint to Bayesian clustering and inference state space in BAMSS
Artificial Intelligence based Anomaly Detection of Energy Consumption in Buildings: A Review, Current Trends and New Perspectives
Enormous amounts of data are being produced everyday by sub-meters and smart
sensors installed in residential buildings. If leveraged properly, that data
could assist end-users, energy producers and utility companies in detecting
anomalous power consumption and understanding the causes of each anomaly.
Therefore, anomaly detection could stop a minor problem becoming overwhelming.
Moreover, it will aid in better decision-making to reduce wasted energy and
promote sustainable and energy efficient behavior. In this regard, this paper
is an in-depth review of existing anomaly detection frameworks for building
energy consumption based on artificial intelligence. Specifically, an extensive
survey is presented, in which a comprehensive taxonomy is introduced to
classify existing algorithms based on different modules and parameters adopted,
such as machine learning algorithms, feature extraction approaches, anomaly
detection levels, computing platforms and application scenarios. To the best of
the authors' knowledge, this is the first review article that discusses anomaly
detection in building energy consumption. Moving forward, important findings
along with domain-specific problems, difficulties and challenges that remain
unresolved are thoroughly discussed, including the absence of: (i) precise
definitions of anomalous power consumption, (ii) annotated datasets, (iii)
unified metrics to assess the performance of existing solutions, (iv) platforms
for reproducibility and (v) privacy-preservation. Following, insights about
current research trends are discussed to widen the applications and
effectiveness of the anomaly detection technology before deriving future
directions attracting significant attention. This article serves as a
comprehensive reference to understand the current technological progress in
anomaly detection of energy consumption based on artificial intelligence.Comment: 11 Figures, 3 Table
Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future
Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)
Biomedical applications of belief networks
Biomedicine is an area in which computers have long been expected to play a significant
role. Although many of the early claims have proved unrealistic, computers are gradually
becoming accepted in the biomedical, clinical and research environment. Within these
application areas, expert systems appear to have met with the most resistance, especially
when applied to image interpretation.In order to improve the acceptance of computerised decision support systems it is
necessary to provide the information needed to make rational judgements concerning
the inferences the system has made. This entails an explanation of what inferences
were made, how the inferences were made and how the results of the inference are to
be interpreted. Furthermore there must be a consistent approach to the combining of
information from low level computational processes through to high level expert analyses.nformation from low level computational processes through to high level expert analyses.
Until recently ad hoc formalisms were seen as the only tractable approach to reasoning
under uncertainty. A review of some of these formalisms suggests that they are less
than ideal for the purposes of decision making. Belief networks provide a tractable way
of utilising probability theory as an inference formalism by combining the theoretical
consistency of probability for inference and decision making, with the ability to use the
knowledge of domain experts.nowledge of domain experts.
The potential of belief networks in biomedical applications has already been recog¬
nised and there has been substantial research into the use of belief networks for medical
diagnosis and methods for handling large, interconnected networks. In this thesis the use
of belief networks is extended to include detailed image model matching to show how,
in principle, feature measurement can be undertaken in a fully probabilistic way. The
belief networks employed are usually cyclic and have strong influences between adjacent
nodes, so new techniques for probabilistic updating based on a model of the matching
process have been developed.An object-orientated inference shell called FLAPNet has been implemented and used
to apply the belief network formalism to two application domains. The first application is
model-based matching in fetal ultrasound images. The imaging modality and biological
variation in the subject make model matching a highly uncertain process. A dynamic,
deformable model, similar to active contour models, is used. A belief network combines
constraints derived from local evidence in the image, with global constraints derived from
trained models, to control the iterative refinement of an initial model cue.In the second application a belief network is used for the incremental aggregation of
evidence occurring during the classification of objects on a cervical smear slide as part of
an automated pre-screening system. A belief network provides both an explicit domain
model and a mechanism for the incremental aggregation of evidence, two attributes
important in pre-screening systems.Overall it is argued that belief networks combine the necessary quantitative features
required of a decision support system with desirable qualitative features that will lead
to improved acceptability of expert systems in the biomedical domain
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