Handling initial uncertainty in the stochastic situation calculus by Hojjat Ghaderi

Cover of: Handling initial uncertainty in the stochastic situation calculus | Hojjat Ghaderi

Published by National Library of Canada in Ottawa .

Written in English

Read online

Edition Notes

Book details

SeriesCanadian theses = -- Th`eses canadiennes
The Physical Object
Pagination1 microfiche : negative.
ID Numbers
Open LibraryOL21241571M
ISBN 100612738833

Download Handling initial uncertainty in the stochastic situation calculus

First, the situation calculus and the basic action theory give us the power to model the dynamic world, the actions of the agent and their ef-fects. Second, concerning the uncertainty system, we need a good understanding of how probabilistic uncertainty is expressed in the situation calculus.

The Language of the Situation Calculus. Review The basic elements of the situation calculus Actions kick(x): kick object x Situations s 0, do(a,s) Objects mary, boxA The basic action theory Action precondition axi oms Successor state axioms Initial database Complex actions α;β, p?, α|β, (πx)α(x), if.

Purchase Uncertainty Quantification and Stochastic Modeling with Matlab - 1st Edition. Print Book & E-Book. ISBNPrice: $ initial condition then the uncertainty in the solution can be modelled stochastically.

–Often stochastic models are much more tractable, analytically and numerically, than their deterministic counterparts, so it can be useful to approximate a deterministic system with a stochastic one.

Suggested Citation:"9 Uncertainty and Stochastic Processes in Mechanics."National Research Council. Research Directions in Computational gton, DC: The National Academies Press.

doi: / Based on the probability distribution of the random data, and using decision theoretical concepts, optimization problems under stochastic uncertainty are converted into appropriate deterministic substitute problems. Due to the probabilities and expectations involved, the book also shows how to apply approximative solution techniques.

McShane's canonical model and alternative stochastic calculus for handling these models resolves these issues in a satisfactory manner. This paper explores the application of McShane's approach to four areas: empirical estimation and testing of stochastic models, Fischer's model of the demand for index bonds, option pricing, and optimal investment under price level uncertainty.

Hojjat Ghaderi, "Handling initial uncertainty in the Stochastic Situation Calculus", MSc thesis, Department of Computer Science, University of Toronto, August Teaching Teaching and learning go hand in hand. It is a dance, which if well executed, can be inspiring and rewarding, all at same time.

The initial setting is an arbitrage-free bond market under volatility uncertainty. invented by Geman [25], is used for pricing in the classical situation without volatility uncertainty [11, 26, 31].

Similar to the forward measure, the forward there are many attempts to a pathwise stochastic calculus, which works without any reference.

Situation calculus and event calculus allow for the representation of individuals and relations and an indefinite planning horizon, but they do not represent uncertainty. Independent choice logic is a relational representation that allows for the representation of uncertainty in the effect of actions, sensing uncertainty, and utilities; however.

STOCHASTIC MODELS OF UNCERTAINTY. To illustrate some of the concepts described in Chapter 4, two examples of stochastic models of uncertainty involved in decision-making problems related to P&R are first example concerns trade-offs among skill capacities and readiness of resources given uncertainty around the demand for such resources, which relate to P&R missions associated.

Multi-Agent Actions Under Uncertainty: Situation Calculus, Discrete Time, Plans and ICL which has more sophisticated mechanisms for handling uncertainty than the. that is the initial. This book covers topics in probability, statistics, economics, stochastic optimization, control theory, regression analysis, simulation, stochastic programming, Markov decision process, and others.

In honor of Sid Yakowitz, internationally known scholars submitted papers on modeling uncertainty. In this work, we describe a two-step framework for numerically solving semilinear elliptic partial differential equations with random coefficients: 1) reformulate the problem as a functional minimization problem based on the direct method of calculus of variation; 2) solve the minimization problem using the stochastic gradient descent method.

Stochastic Models of Uncertainties in Computational Mechanics presents the main concepts, formulations, and recent advances in the use of a mathematical-mechanical modeling process to predict the responses of a real structural system in its environment.

Computational models are subject to two types of uncertainties—variabilities in the real. In this paper, we describe a general stochastic (i.e. probabilistic) framework for handling both modeling and excitation uncertainty when predicting structural response.

It uses an interpretation of probability as a multi-valued conditional logic for plausible reasoning due to. The purpose of this chapter is to develop certain relatively mathematical discoveries known generally as stochastic calculus, or more specifically as Itô’s calculus and to also illustrate their.

Such problems become,even more complex,in practical situations where handling time dependent,uncertainties becomes,an important issue. stochastic calculus, and stochastic. However, if parameters' stochastic data is considered in dynamic optimization models, and if the handling of uncertainty is done with deterministic approaches, intractable problems arise frequently.

In this work, a simulation-based approach is used to dynamically optimize under uncertainty a brewery mashing process. agent’s initial uncertainty and updates these as the pro-gram executes. For one thing, this allows belief-level queries in programs to be evaluated e ciently in an on-line setting.

For another, sampling admits a simple strat-egy for handling the nondeterminism in noisy actions. Nonetheless, the interpreter is argued to be correct using limits. Stochastic dynamic programming is a useful tool in understanding decision making under uncertainty.

The accumulation of capital stock under uncertainty is one example; often it is used by resource economists to analyze bioeconomic problems [9] where the uncertainty enters in such as weather, etc. Stochastic Optimization Algorithms have become essential tools in solving a wide range of difficult and critical optimization problems.

Such methods are able to find the optimum solution of a problem with uncertain elements or to algorithmically incorporate uncertainty to solve a deterministic problem. They even succeed in fighting uncertainty with uncertainty.

This book discusses theoretical. Multistage Stochastic Programming: A Scenario Tree Based Approach to Planning under Uncertainty Boris Defourny, Damien Ernst, and Louis Wehenkel University of Li`ege, Systems and Modeling, B28, B Li`ege, Belgium {ny,dernst,el}@ Abstract.

In this chapter, we present the multistage stochastic pro. Further, the forward–backward calculus for stochastic flow will be discussed in Sects. and These facts will be applied in Sects. and for proving the diffeomorphic property.

A stochastic collocation method for solving linear parabolic partial differential equations with random coefficients, forcing terms, and initial conditions is analyzed.

The input data are assumed to depend on a finite number of random variables. In this paper, we propose a fresh amalgamation of a modal fragment of the situation calculus and uncertainty, where the idea will be to update the initial knowledge base, containing both ordinary.

All uncertainty of B is concentrated in its quadratic variation 〈 B 〉. It is an absolutely continuous process w.r.t. Lebesgue measure, and its density satisfies σ ¯ 2 ≤ d 〈 B 〉 t d t ≤ σ ¯ 2. The related stochastic calculus, especially Itô integral, can also be established with respect to G-Brownian motion, Peng ().

course the following books were used in the preparation [5–9], the German textbooks [10,11], elementary texts in English [12,13] and in German [14]. In addition, we mention several other bookson probability theory and stochastic processes [15–26].

More references will be given in the chapters on chemical. Constraint satisfaction problem (CSP) planning allows for pruning the search space based on both the initial situation and the goal, but at the cost of planning for only a finite stage.

Decision networks represent features, stochastic effects, partial observability, and complex preferences in terms of utilities. The Cognitive Robotics Group has a one hour informal meeting every two weeks. These meetings are an excellent opportunity to know and learn about the research of other students and.

Abstract. Some reflections on theories of decision-choice activities under uncertainty and risk will be useful at this point. The theories may be grouped into those that are concerned with probabilistic belief, stochastic uncertainty and stochastic risk on one hand and those that are concerned with possibilistic belief, fuzzy uncertainty and fuzzy risk on the other.

Mathematics of Uncertainty Modeling in the Analysis of Engineering and Science Problems. Previous / Mathematics of Uncertainty Modeling in the Analysis of Engineering and Science Problems. Posted by jano Mathematics of Uncertainty Modeling in the Analysis of Engineering and Science Problems.

In this paper, we design and implement a computational tool that can deal with uncertainty in both model structure and the values of model parameters for both deterministic and stochastic models.

The central component of our tool is the process-based modeling formalism that allows for modular, compositional specification of the space of.

Application of stochastic modelling in bioinformatics 1. APPLICATIONS OF STOCHASTIC MODELLING IN BIOINFORMATICS Information Science & Informatics Informatics and Neuroinformatics 1 Spyros Ktenas.

ity model uncertainty in finance is a typical example. We present a new type of law of large numbers and central limit theorem as well as G-Brownian motion and the corresponding stochastic calculus of Itoˆ’s type under such new sublin-ear expectation space.

This book explores recent advances in uncertainty quantification for hyperbolic, kinetic, and related problems.

The contributions address a range of different aspects, including: polynomial chaos expansions, perturbation methods, multi-level Monte Carlo. Uncertainty and Stochastic Programs If you come to a fork in the road, take it.

Berra Introduction A big reason multiperiod planning is difficult is because of uncertainty about the future. For example, next year, if the demand for your new product proves to be large and the cost of. This book aims to present several new developments on stochastic processes and operator calculus on quantum groups.

Topics which are treated include operator calculus, dual representations, stochastic processes and diffusions, Appell polynomials and systems in.

This stochastic equation, star, has a solution that is unique, of course with [. boundary?] condition, had a solution.

And given the initial points--so if you're given the initial point of a stochastic process, then a solution is unique. Just check, yes--as long as mu and sigma are reasonable. One way it can be reasonable, if it satisfies this.

Quoting your uncertainty in the units of the original measurement – for example, ± g or ± cm – gives the “absolute” uncertainty. In other words, it explicitly tells you the amount by which the original measurement could be incorrect. The relative uncertainty gives the uncertainty as a percentage of the original value.

It will be a very useful book for young researchers who want to learn about the research directions in the area, as well as experienced researchers who want to know about the latest developments in the area of stochastic analysis and mathematical finance.

Sample Chapter(s) Editorial Foreword (58 KB).Uncertainty is a key component of financial markets and thus of any asset pricing theory. This chapter provides the tools from probability theory that are being used in the asset pricing models covered in the remaining part of the book.

The mathematical representation of uncertainty and information flow is explained. Stochastic processes are introduced with numerous examples both in discrete.HOW TO TOLERATE UNCERTAINTY Dealing with uncertainty is an unavoidable part of daily life.

Because we can’t see the future, we can never be certain about what exactly is going to happen day to day. Research has found that people vary in their ability to tolerate uncertainty.

That is, some.

31518 views Thursday, November 5, 2020