120 research outputs found
Interference Effects in Quantum Belief Networks
Probabilistic graphical models such as Bayesian Networks are one of the most
powerful structures known by the Computer Science community for deriving
probabilistic inferences. However, modern cognitive psychology has revealed
that human decisions could not follow the rules of classical probability
theory, because humans cannot process large amounts of data in order to make
judgements. Consequently, the inferences performed are based on limited data
coupled with several heuristics, leading to violations of the law of total
probability. This means that probabilistic graphical models based on classical
probability theory are too limited to fully simulate and explain various
aspects of human decision making.
Quantum probability theory was developed in order to accommodate the
paradoxical findings that the classical theory could not explain. Recent
findings in cognitive psychology revealed that quantum probability can fully
describe human decisions in an elegant framework. Their findings suggest that,
before taking a decision, human thoughts are seen as superposed waves that can
interfere with each other, influencing the final decision.
In this work, we propose a new Bayesian Network based on the psychological
findings of cognitive scientists. We made experiments with two very well known
Bayesian Networks from the literature. The results obtained revealed that the
quantum like Bayesian Network can affect drastically the probabilistic
inferences, specially when the levels of uncertainty of the network are very
high (no pieces of evidence observed). When the levels of uncertainty are very
low, then the proposed quantum like network collapses to its classical
counterpart
Finding Academic Experts on a MultiSensor Approach using Shannon's Entropy
Expert finding is an information retrieval task concerned with the search for
the most knowledgeable people, in some topic, with basis on documents
describing peoples activities. The task involves taking a user query as input
and returning a list of people sorted by their level of expertise regarding the
user query. This paper introduces a novel approach for combining multiple
estimators of expertise based on a multisensor data fusion framework together
with the Dempster-Shafer theory of evidence and Shannon's entropy. More
specifically, we defined three sensors which detect heterogeneous information
derived from the textual contents, from the graph structure of the citation
patterns for the community of experts, and from profile information about the
academic experts. Given the evidences collected, each sensor may define
different candidates as experts and consequently do not agree in a final
ranking decision. To deal with these conflicts, we applied the Dempster-Shafer
theory of evidence combined with Shannon's Entropy formula to fuse this
information and come up with a more accurate and reliable final ranking list.
Experiments made over two datasets of academic publications from the Computer
Science domain attest for the adequacy of the proposed approach over the
traditional state of the art approaches. We also made experiments against
representative supervised state of the art algorithms. Results revealed that
the proposed method achieved a similar performance when compared to these
supervised techniques, confirming the capabilities of the proposed framework
Towards a Quantum-Like Cognitive Architecture for Decision-Making
We propose an alternative and unifying framework for decision-making that, by
using quantum mechanics, provides more generalised cognitive and decision
models with the ability to represent more information than classical models.
This framework can accommodate and predict several cognitive biases reported in
Lieder & Griffiths without heavy reliance on heuristics nor on assumptions of
the computational resources of the mind
Multiple-Modality Associative Memory: a framework for Learning
Drawing from memory the face of a friend you have not seen in years is a
difficult task. However, if you happen to cross paths, you would easily
recognize each other. The biological memory is equipped with an impressive
compression algorithm that can store the essential, and then infer the details
to match perception. Willshaw's model of Associative memory is a likely
candidate for a computational model of this brain function, but its application
on real-world data is hindered by the so-called Sparse Coding Problem. Due to a
recently proposed sparse encoding prescription [31], which maps visual patterns
into binary feature maps, we were able to analyze the behavior of the Willshaw
Network (WN) on real-world data and gain key insights into the strengths of the
model. To further enhance the capabilities of the WN, we propose the
Multiple-Modality architecture. In this new setting, the memory stores several
modalities (e.g., visual, or textual) simultaneously. After training, the model
can be used to infer missing modalities when just a subset is perceived, thus
serving as a flexible framework for learning tasks. We evaluated the model on
the MNIST dataset. By storing both the images and labels as modalities, we were
able to successfully perform pattern completion, classification, and generation
with a single model.Comment: 21 pages, 15 figure
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