18,163 research outputs found
Probabilistic Default Reasoning with Conditional Constraints
We propose a combination of probabilistic reasoning from conditional
constraints with approaches to default reasoning from conditional knowledge
bases. In detail, we generalize the notions of Pearl's entailment in system Z,
Lehmann's lexicographic entailment, and Geffner's conditional entailment to
conditional constraints. We give some examples that show that the new notions
of z-, lexicographic, and conditional entailment have similar properties like
their classical counterparts. Moreover, we show that the new notions of z-,
lexicographic, and conditional entailment are proper generalizations of both
their classical counterparts and the classical notion of logical entailment for
conditional constraints.Comment: 8 pages; to appear in Proceedings of the Eighth International
Workshop on Nonmonotonic Reasoning, Special Session on Uncertainty Frameworks
in Nonmonotonic Reasoning, Breckenridge, Colorado, USA, 9-11 April 200
The stack resource protocol based on real time transactions
Current hard real time (HRT) kernels have their timely behaviour guaranteed at the cost of a rather restrictive use of the available resources. This makes current HRT scheduling techniques inadequate for use in a multimedia environment where one can profit by a better and more flexible use of the resources. It is shown that one can improve the flexibility and efficiency of real time kernels and a method is proposed for precise quality of service schedulability analysis of the stack resource protocol. This protocol is generalised by introducing real time transactions, which makes its use straightforward and efficient. Transactions can be refined to nested critical sections if the smallest estimation of blocking is desired. The method can be used for hard real time systems in general and for multimedia systems in particular
Answer Set Programming Modulo `Space-Time'
We present ASP Modulo `Space-Time', a declarative representational and
computational framework to perform commonsense reasoning about regions with
both spatial and temporal components. Supported are capabilities for mixed
qualitative-quantitative reasoning, consistency checking, and inferring
compositions of space-time relations; these capabilities combine and synergise
for applications in a range of AI application areas where the processing and
interpretation of spatio-temporal data is crucial. The framework and resulting
system is the only general KR-based method for declaratively reasoning about
the dynamics of `space-time' regions as first-class objects. We present an
empirical evaluation (with scalability and robustness results), and include
diverse application examples involving interpretation and control tasks
Maxallent: Maximizers of all Entropies and Uncertainty of Uncertainty
The entropy maximum approach (Maxent) was developed as a minimization of the
subjective uncertainty measured by the Boltzmann--Gibbs--Shannon entropy. Many
new entropies have been invented in the second half of the 20th century. Now
there exists a rich choice of entropies for fitting needs. This diversity of
entropies gave rise to a Maxent "anarchism". Maxent approach is now the
conditional maximization of an appropriate entropy for the evaluation of the
probability distribution when our information is partial and incomplete. The
rich choice of non-classical entropies causes a new problem: which entropy is
better for a given class of applications? We understand entropy as a measure of
uncertainty which increases in Markov processes. In this work, we describe the
most general ordering of the distribution space, with respect to which all
continuous-time Markov processes are monotonic (the Markov order). For
inference, this approach results in a set of conditionally "most random"
distributions. Each distribution from this set is a maximizer of its own
entropy. This "uncertainty of uncertainty" is unavoidable in analysis of
non-equilibrium systems. Surprisingly, the constructive description of this set
of maximizers is possible. Two decomposition theorems for Markov processes
provide a tool for this description.Comment: 23 pages, 4 figures, Correction in Conclusion (postprint
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