20,837 research outputs found
Performance Evaluation of Components Using a Granularity-based Interface Between Real-Time Calculus and Timed Automata
To analyze complex and heterogeneous real-time embedded systems, recent works
have proposed interface techniques between real-time calculus (RTC) and timed
automata (TA), in order to take advantage of the strengths of each technique
for analyzing various components. But the time to analyze a state-based
component modeled by TA may be prohibitively high, due to the state space
explosion problem. In this paper, we propose a framework of granularity-based
interfacing to speed up the analysis of a TA modeled component. First, we
abstract fine models to work with event streams at coarse granularity. We
perform analysis of the component at multiple coarse granularities and then
based on RTC theory, we derive lower and upper bounds on arrival patterns of
the fine output streams using the causality closure algorithm. Our framework
can help to achieve tradeoffs between precision and analysis time.Comment: QAPL 201
The Qualification Problem: A solution to the problem of anomalous models
AbstractIntelligent agents in open environments inevitably face the Qualification Problem: The executability of an action can never be predicted with absolute certainty; unexpected circumstances, albeit unlikely, may at any time prevent the successful performance of an action. Reasoning agents in real-world environments rely on a solution to the Qualification Problem in order to make useful predictions but also to explain and recover from unexpected action failures. Yet the main theoretical result known today in this context is a negative one: While a solution to the Qualification Problem requires to assume away by default abnormal qualifications of actions, straightforward minimization of abnormality falls prey to the production of anomalous models. We present an approach to the Qualification Problem which resolves this anomaly. Anomalous models are shown to arise from ignoring causality, and they are avoided by appealing to just this concept. Our theory builds on the established predicate logic formalism of the Fluent Calculus as a solution to the Frame Problem and to the Ramification Problem in reasoning about actions. The monotonic Fluent Calculus is enhanced by a default theory in order to obtain the nonmonotonic approach called for by the Qualification Problem. The approach has been implemented in an action programming language based on the Fluent Calculus and successfully applied to the high-level control of robots
von Neumann-Morgenstern and Savage Theorems for Causal Decision Making
Causal thinking and decision making under uncertainty are fundamental aspects
of intelligent reasoning. Decision making under uncertainty has been well
studied when information is considered at the associative (probabilistic)
level. The classical Theorems of von Neumann-Morgenstern and Savage provide a
formal criterion for rational choice using purely associative information.
Causal inference often yields uncertainty about the exact causal structure, so
we consider what kinds of decisions are possible in those conditions. In this
work, we consider decision problems in which available actions and consequences
are causally connected. After recalling a previous causal decision making
result, which relies on a known causal model, we consider the case in which the
causal mechanism that controls some environment is unknown to a rational
decision maker. In this setting we state and prove a causal version of Savage's
Theorem, which we then use to develop a notion of causal games with its
respective causal Nash equilibrium. These results highlight the importance of
causal models in decision making and the variety of potential applications.Comment: Submitted to Journal of Causal Inferenc
Quantum Trajectories, State Diffusion and Time Asymmetric Eventum Mechanics
We show that the quantum stochastic unitary dynamics Langevin model for
continuous in time measurements provides an exact formulation of the Heisenberg
uncertainty error-disturbance principle. Moreover, as it was shown in the 80's,
this Markov model induces all stochastic linear and non-linear equations of the
phenomenological "quantum trajectories" such as quantum state diffusion and
spontaneous localization by a simple quantum filtering method. Here we prove
that the quantum Langevin equation is equivalent to a Dirac type boundary-value
problem for the second-quantized input "offer waves from future" in one extra
dimension, and to a reduction of the algebra of the consistent histories of
past events to an Abelian subalgebra for the "trajectories of the output
particles". This result supports the wave-particle duality in the form of the
thesis of Eventum Mechanics that everything in the future is constituted by
quantized waves, everything in the past by trajectories of the recorded
particles. We demonstrate how this time arrow can be derived from the principle
of quantum causality for nondemolition continuous in time measurements.Comment: 21 pages. See also relevant publications at
http://www.maths.nott.ac.uk/personal/vpb/publications.htm
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