202,748 research outputs found
Combining logic and probability in tracking and scene interpretation
The paper gives a high-level overview of some ways in which logical representations and reasoning can be used in computer vision applications, such as tracking and scene interpretation. The combination of logical and statistical approaches is also considered
A Description Logic of Typicality for Conceptual Combination
We propose a nonmonotonic Description Logic of typicality able to
account for the phenomenon of combining prototypical concepts, an open problem
in the fields of AI and cognitive modelling. Our logic extends the logic of
typicality ALC + TR, based on the notion of rational closure, by inclusions
p :: T(C) v D (âwe have probability p that typical Cs are Dsâ), coming
from the distributed semantics of probabilistic Description Logics. Additionally,
it embeds a set of cognitive heuristics for concept combination. We show that the
complexity of reasoning in our logic is EXPTIME-complete as in ALC
Using Probability to Reason about Soft Deadlines
Soft deadlines are significant in systems in which a bound on the response time is important, but the failure to meet the response time is not a disaster. Soft deadlines occur, for example, in telephony and switching networks. We investigate how to put probabilistic bounds on the time-complexity of a concurrent logic program by combining (on-line) profiling with an (off-line) probabilistic complexity analysis. The profiling collects information on the likelihood of case selection and the analysis uses this information to infer the probability of an agent terminating within k steps. Although the approach does not reason about synchronization, we believe that its simplicity and good (essentially quadratic) complexity mean that it is a promising first step in reasoning about soft deadlines
Bayesian Updating, Model Class Selection and Robust Stochastic Predictions of Structural Response
A fundamental issue when predicting structural response by using mathematical models is how to treat both modeling and excitation uncertainty. A general framework for this is presented which uses probability as a multi-valued
conditional logic for quantitative plausible reasoning in the presence of uncertainty due to incomplete information. The
fundamental probability models that represent the structureâs uncertain behavior are specified by the choice of a stochastic
system model class: a set of input-output probability models for the structure and a prior probability distribution over this set
that quantifies the relative plausibility of each model. A model class can be constructed from a parameterized deterministic
structural model by stochastic embedding utilizing Jaynesâ Principle of Maximum Information Entropy. Robust predictive
analyses use the entire model class with the probabilistic predictions of each model being weighted by its prior probability, or if
structural response data is available, by its posterior probability from Bayesâ Theorem for the model class. Additional robustness
to modeling uncertainty comes from combining the robust predictions of each model class in a set of competing candidates
weighted by the prior or posterior probability of the model class, the latter being computed from Bayesâ Theorem. This higherlevel application of Bayesâ Theorem automatically applies a quantitative Ockham razor that penalizes the data-fit of more
complex model classes that extract more information from the data. Robust predictive analyses involve integrals over highdimensional spaces that usually must be evaluated numerically. Published applications have used Laplace's method of
asymptotic approximation or Markov Chain Monte Carlo algorithms
Self-Organized Complexity and Coherent Infomax from the Viewpoint of Jaynes's Probability Theory
This paper discusses concepts of self-organized complexity and the theory of Coherent Infomax in the light of Jaynes’s probability theory. Coherent Infomax, shows, in principle, how adaptively self-organized complexity can be preserved and improved by using probabilistic inference that is context-sensitive. It argues that neural systems do this by combining local reliability with flexible, holistic, context-sensitivity. Jaynes argued that the logic of probabilistic inference shows it to be based upon Bayesian and Maximum Entropy methods or special cases of them. He presented his probability theory as the logic of science; here it is considered as the logic of life. It is concluded that the theory of Coherent Infomax specifies a general objective for probabilistic inference, and that contextual interactions in neural systems perform functions required of the scientist within Jaynes’s theory
The Tower of Knowledge: a novel architecture for organising knowledge combining logic and probability
It is argued that the ability to generalise is the most important characteristic
of learning and that generalisation may be achieved only if pattern
recognition systems learn the rules of meta-knowledge rather than the labels
of objects. A structure, called "tower of knowledge\u27\u27, according to which
knowledge may be organised, is proposed. A scheme of interpreting scenes using
the tower of knowledge and aspects of utility theory is also proposed. Finally, it is argued that globally
consistent solutions of labellings are neither possible, nor desirable for an
artificial cognitive system
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