56,374 research outputs found
Fredkin Gates for Finite-valued Reversible and Conservative Logics
The basic principles and results of Conservative Logic introduced by Fredkin
and Toffoli on the basis of a seminal paper of Landauer are extended to
d-valued logics, with a special attention to three-valued logics. Different
approaches to d-valued logics are examined in order to determine some possible
universal sets of logic primitives. In particular, we consider the typical
connectives of Lukasiewicz and Godel logics, as well as Chang's MV-algebras. As
a result, some possible three-valued and d-valued universal gates are described
which realize a functionally complete set of fundamental connectives.Comment: 57 pages, 10 figures, 16 tables, 2 diagram
The Combination of Paradoxical, Uncertain, and Imprecise Sources of Information based on DSmT and Neutro-Fuzzy Inference
The management and combination of uncertain, imprecise, fuzzy and even
paradoxical or high conflicting sources of information has always been, and
still remains today, of primal importance for the development of reliable
modern information systems involving artificial reasoning. In this chapter, we
present a survey of our recent theory of plausible and paradoxical reasoning,
known as Dezert-Smarandache Theory (DSmT) in the literature, developed for
dealing with imprecise, uncertain and paradoxical sources of information. We
focus our presentation here rather on the foundations of DSmT, and on the two
important new rules of combination, than on browsing specific applications of
DSmT available in literature. Several simple examples are given throughout the
presentation to show the efficiency and the generality of this new approach.
The last part of this chapter concerns the presentation of the neutrosophic
logic, the neutro-fuzzy inference and its connection with DSmT. Fuzzy logic and
neutrosophic logic are useful tools in decision making after fusioning the
information using the DSm hybrid rule of combination of masses.Comment: 20 page
SensibleSleep: A Bayesian Model for Learning Sleep Patterns from Smartphone Events
We propose a Bayesian model for extracting sleep patterns from smartphone
events. Our method is able to identify individuals' daily sleep periods and
their evolution over time, and provides an estimation of the probability of
sleep and wake transitions. The model is fitted to more than 400 participants
from two different datasets, and we verify the results against ground truth
from dedicated armband sleep trackers. We show that the model is able to
produce reliable sleep estimates with an accuracy of 0.89, both at the
individual and at the collective level. Moreover the Bayesian model is able to
quantify uncertainty and encode prior knowledge about sleep patterns. Compared
with existing smartphone-based systems, our method requires only screen on/off
events, and is therefore much less intrusive in terms of privacy and more
battery-efficient
Categorical invariance and structural complexity in human concept learning
An alternative account of human concept learning based on an invariance measure of the categorical\ud
stimulus is proposed. The categorical invariance model (CIM) characterizes the degree of structural\ud
complexity of a Boolean category as a function of its inherent degree of invariance and its cardinality or\ud
size. To do this we introduce a mathematical framework based on the notion of a Boolean differential\ud
operator on Boolean categories that generates the degrees of invariance (i.e., logical manifold) of the\ud
category in respect to its dimensions. Using this framework, we propose that the structural complexity\ud
of a Boolean category is indirectly proportional to its degree of categorical invariance and directly\ud
proportional to its cardinality or size. Consequently, complexity and invariance notions are formally\ud
unified to account for concept learning difficulty. Beyond developing the above unifying mathematical\ud
framework, the CIM is significant in that: (1) it precisely predicts the key learning difficulty ordering of\ud
the SHJ [Shepard, R. N., Hovland, C. L.,&Jenkins, H. M. (1961). Learning and memorization of classifications.\ud
Psychological Monographs: General and Applied, 75(13), 1-42] Boolean category types consisting of three\ud
binary dimensions and four positive examples; (2) it is, in general, a good quantitative predictor of the\ud
degree of learning difficulty of a large class of categories (in particular, the 41 category types studied\ud
by Feldman [Feldman, J. (2000). Minimization of Boolean complexity in human concept learning. Nature,\ud
407, 630-633]); (3) it is, in general, a good quantitative predictor of parity effects for this large class of\ud
categories; (4) it does all of the above without free parameters; and (5) it is cognitively plausible (e.g.,\ud
cognitively tractable)
Encoding many-valued logic in {\lambda}-calculus
We extend the well-known Church encoding of two-valued Boolean Logic in
-calculus to encodings of -valued propositional logic (for ) in well-chosen infinitary extensions in -calculus. In case
of three-valued logic we use the infinitary extension of the finite
-calculus in which all terms have their B\"ohm tree as their unique
normal form. We refine this construction for . These -valued
logics are all variants of McCarthy's left-sequential, three-valued
propositional calculus. The four- and five-valued logic have been given
complete axiomatisations by Bergstra and Van de Pol. The encodings of these
-valued logics are of interest because they can be used to calculate the
truth values of infinitary propositions. With a novel application of McCarthy's
three-valued logic we can now resolve Russell's paradox. Since B\"ohm trees are
always finite in Church's original -calculus, we believe
their construction to be within the technical means of Church. Arguably he
could have found this encoding of three-valued logic and used it to resolve
Russell's paradox.Comment: 15 page
Game Networks
We introduce Game networks (G nets), a novel representation for multi-agent
decision problems. Compared to other game-theoretic representations, such as
strategic or extensive forms, G nets are more structured and more compact; more
fundamentally, G nets constitute a computationally advantageous framework for
strategic inference, as both probability and utility independencies are
captured in the structure of the network and can be exploited in order to
simplify the inference process. An important aspect of multi-agent reasoning is
the identification of some or all of the strategic equilibria in a game; we
present original convergence methods for strategic equilibrium which can take
advantage of strategic separabilities in the G net structure in order to
simplify the computations. Specifically, we describe a method which identifies
a unique equilibrium as a function of the game payoffs, and one which
identifies all equilibria.Comment: Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000
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