27,271 research outputs found
Optimality of Universal Bayesian Sequence Prediction for General Loss and Alphabet
Various optimality properties of universal sequence predictors based on
Bayes-mixtures in general, and Solomonoff's prediction scheme in particular,
will be studied. The probability of observing at time , given past
observations can be computed with the chain rule if the true
generating distribution of the sequences is known. If
is unknown, but known to belong to a countable or continuous class \M
one can base ones prediction on the Bayes-mixture defined as a
-weighted sum or integral of distributions \nu\in\M. The cumulative
expected loss of the Bayes-optimal universal prediction scheme based on
is shown to be close to the loss of the Bayes-optimal, but infeasible
prediction scheme based on . We show that the bounds are tight and that no
other predictor can lead to significantly smaller bounds. Furthermore, for
various performance measures, we show Pareto-optimality of and give an
Occam's razor argument that the choice for the weights
is optimal, where is the length of the shortest program describing
. The results are applied to games of chance, defined as a sequence of
bets, observations, and rewards. The prediction schemes (and bounds) are
compared to the popular predictors based on expert advice. Extensions to
infinite alphabets, partial, delayed and probabilistic prediction,
classification, and more active systems are briefly discussed.Comment: 34 page
Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges
Today's mobile phones are far from mere communication devices they were ten
years ago. Equipped with sophisticated sensors and advanced computing hardware,
phones can be used to infer users' location, activity, social setting and more.
As devices become increasingly intelligent, their capabilities evolve beyond
inferring context to predicting it, and then reasoning and acting upon the
predicted context. This article provides an overview of the current state of
the art in mobile sensing and context prediction paving the way for
full-fledged anticipatory mobile computing. We present a survey of phenomena
that mobile phones can infer and predict, and offer a description of machine
learning techniques used for such predictions. We then discuss proactive
decision making and decision delivery via the user-device feedback loop.
Finally, we discuss the challenges and opportunities of anticipatory mobile
computing.Comment: 29 pages, 5 figure
The role of decision confidence in advice-taking and trust formation
In a world where ideas flow freely between people across multiple platforms,
we often find ourselves relying on others' information without an objective
standard to judge whether those opinions are accurate. The present study tests
an agreement-in-confidence hypothesis of advice perception, which holds that
internal metacognitive evaluations of decision confidence play an important
functional role in the perception and use of social information, such as peers'
advice. We propose that confidence can be used, computationally, to estimate
advisors' trustworthiness and advice reliability. Specifically, these processes
are hypothesized to be particularly important in situations where objective
feedback is absent or difficult to acquire. Here, we use a judge-advisor system
paradigm to precisely manipulate the profiles of virtual advisors whose
opinions are provided to participants performing a perceptual decision making
task. We find that when advisors' and participants' judgments are independent,
people are able to discriminate subtle advice features, like confidence
calibration, whether or not objective feedback is available. However, when
observers' judgments (and judgment errors) are correlated - as is the case in
many social contexts - predictable distortions can be observed between feedback
and feedback-free scenarios. A simple model of advice reliability estimation,
endowed with metacognitive insight, is able to explain key patterns of results
observed in the human data. We use agent-based modeling to explore implications
of these individual-level decision strategies for network-level patterns of
trust and belief formation
Monitoring-Oriented Programming: A Tool-Supported Methodology for Higher Quality Object-Oriented Software
This paper presents a tool-supported methodological paradigm for object-oriented software development, called monitoring-oriented programming and abbreviated MOP, in which runtime monitoring is a basic software design principle. The general idea underlying MOP is that software developers insert specifications in their code via annotations. Actual monitoring code is automatically synthesized from these annotations before compilation and integrated at appropriate places in the program, according to user-defined configuration attributes. This way, the specification is checked at runtime against the implementation. Moreover, violations and/or validations of specifications can trigger user-defined code at any points in the program, in particular recovery code, outputting or sending messages, or raising exceptions.
The MOP paradigm does not promote or enforce any specific formalism to specify requirements: it allows the users to plug-in their favorite or domain-specific specification formalisms via logic plug-in modules. There are two major technical challenges that MOP supporting tools unavoidably face: monitor synthesis and monitor integration. The former is heavily dependent on the specification formalism and comes as part of the corresponding logic plug-in, while the latter is uniform for all specification formalisms and depends only on the target programming language. An experimental prototype tool, called Java-MOP, is also discussed, which currently supports most but not all of the desired MOP features. MOP aims at reducing the gap between formal specification and implementation, by integrating the two and allowing them together to form a system
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