151,311 research outputs found
Stochastic Nonlinear Model Predictive Control with Efficient Sample Approximation of Chance Constraints
This paper presents a stochastic model predictive control approach for
nonlinear systems subject to time-invariant probabilistic uncertainties in
model parameters and initial conditions. The stochastic optimal control problem
entails a cost function in terms of expected values and higher moments of the
states, and chance constraints that ensure probabilistic constraint
satisfaction. The generalized polynomial chaos framework is used to propagate
the time-invariant stochastic uncertainties through the nonlinear system
dynamics, and to efficiently sample from the probability densities of the
states to approximate the satisfaction probability of the chance constraints.
To increase computational efficiency by avoiding excessive sampling, a
statistical analysis is proposed to systematically determine a-priori the least
conservative constraint tightening required at a given sample size to guarantee
a desired feasibility probability of the sample-approximated chance constraint
optimization problem. In addition, a method is presented for sample-based
approximation of the analytic gradients of the chance constraints, which
increases the optimization efficiency significantly. The proposed stochastic
nonlinear model predictive control approach is applicable to a broad class of
nonlinear systems with the sufficient condition that each term is analytic with
respect to the states, and separable with respect to the inputs, states and
parameters. The closed-loop performance of the proposed approach is evaluated
using the Williams-Otto reactor with seven states, and ten uncertain parameters
and initial conditions. The results demonstrate the efficiency of the approach
for real-time stochastic model predictive control and its capability to
systematically account for probabilistic uncertainties in contrast to a
nonlinear model predictive control approaches.Comment: Submitted to Journal of Process Contro
A Mining-Based Compression Approach for Constraint Satisfaction Problems
In this paper, we propose an extension of our Mining for SAT framework to
Constraint satisfaction Problem (CSP). We consider n-ary extensional
constraints (table constraints). Our approach aims to reduce the size of the
CSP by exploiting the structure of the constraints graph and of its associated
microstructure. More precisely, we apply itemset mining techniques to search
for closed frequent itemsets on these two representation. Using Tseitin
extension, we rewrite the whole CSP to another compressed CSP equivalent with
respect to satisfiability. Our approach contrast with previous proposed
approach by Katsirelos and Walsh, as we do not change the structure of the
constraints.Comment: arXiv admin note: substantial text overlap with arXiv:1304.441
Constrained Policy Optimization for Controlled Self-Learning in Conversational AI Systems
Recently, self-learning methods based on user satisfaction metrics and
contextual bandits have shown promising results to enable consistent
improvements in conversational AI systems. However, directly targeting such
metrics by off-policy bandit learning objectives often increases the risk of
making abrupt policy changes that break the current user experience. In this
study, we introduce a scalable framework for supporting fine-grained
exploration targets for individual domains via user-defined constraints. For
example, we may want to ensure fewer policy deviations in business-critical
domains such as shopping, while allocating more exploration budget to domains
such as music. Furthermore, we present a novel meta-gradient learning approach
that is scalable and practical to address this problem. The proposed method
adjusts constraint violation penalty terms adaptively through a meta objective
that encourages balanced constraint satisfaction across domains. We conduct
extensive experiments using data from a real-world conversational AI on a set
of realistic constraint benchmarks. Based on the experimental results, we
demonstrate that the proposed approach is capable of achieving the best balance
between the policy value and constraint satisfaction rate
A Parallel and Distributed Framework for Constraint Solving
With the increased availability of affordable parallel and distributed hardware, programming models for these architectures has become the focus of significant attention. Constraint programming, which can be seen as the encoding of processes as a Constraint Satisfaction Problem, because of its data-driven and control-insensitive approach is a prime candidate to serve as the basis for a framework which effectively exploits parallel architectures.
To effectually apply the power of distributed computational systems, there must be an effective sharing of the work involved in the search for a solution to a Constraint Satisfaction Problem (CSP) between all the participating agents, and it must happen dynamically, as it is hard to predict the effort associated with the exploration of some part of the search space.
We describe and provide an initial experimental assessment of an implementation of a work stealing-based approach to distributed CSP solving, which relies on multiple back-ends for the distributed computing mechanisms -- from the multicore CPU to supercomputer clusters running MPI or other interprocess communication platforms
Confidence-based Reasoning in Stochastic Constraint Programming
In this work we introduce a novel approach, based on sampling, for finding
assignments that are likely to be solutions to stochastic constraint
satisfaction problems and constraint optimisation problems. Our approach
reduces the size of the original problem being analysed; by solving this
reduced problem, with a given confidence probability, we obtain assignments
that satisfy the chance constraints in the original model within prescribed
error tolerance thresholds. To achieve this, we blend concepts from stochastic
constraint programming and statistics. We discuss both exact and approximate
variants of our method. The framework we introduce can be immediately employed
in concert with existing approaches for solving stochastic constraint programs.
A thorough computational study on a number of stochastic combinatorial
optimisation problems demonstrates the effectiveness of our approach.Comment: 53 pages, working draf
Humanoid Motion Description Language
In this paper we propose a description language for specifying motions for humanoid robots and for allowing humanoid robots to acquire motor skills. Locomotion greatly increases our ability to interact with our environments, which in turn increases our mental abilities. This principle also applies to humanoid robots. However, there are great difficulties to specify humanoid motions and to represent motor skills, which in most cases require four-dimensional space representations. We propose a representation framework that includes the following attributes: motion description layers, egocentric reference system, progressive quantized refinement, and automatic constraint satisfaction. We also outline strategies for acquiring new motor skills by learning from trial and error, macro approach, and programming. Then, we outline the development of a new humanoid motion description language called Cybele
Margin allocation and trade-off in complex systems design and optimization
Presented is an approach for interactive margin management. Existing methods enable a fixed set of
allowable margin combinations to be identified, but these have limitations with regard to supporting
interactive exploration of the effects of: 1) margins on other margins, 2) margins on performance and
3) margins on the probabilities of constraint satisfaction. To this purpose, the concept of a margin
space is introduced. It is bi-directionally linked to the design space, to enable the designer to
understand how assigning margins on certain parameters limits the allowable margins that can be
assigned to other parameters. Also, a novel framework has been developed. It incorporates the
margin space concept as well as enablers, including interactive visualization techniques, which can
aide the designer to explore the margin and design spaces dynamically, as well as the effects of
margins on the probability of constraint satisfaction and on performance. The framework was
implemented into a prototype software tool, AirCADia, which was used for a qualitative evaluation by
practicing designers. The evaluation, conducted as part of the EU TOICA project, demonstrated the
usefulness of the approach
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