62,911 research outputs found
Safe Zero-Shot Model-Based Learning and Control: A Wasserstein Distributionally Robust Approach
This paper explores distributionally robust zero-shot model-based learning
and control using Wasserstein ambiguity sets. Conventional model-based
reinforcement learning algorithms struggle to guarantee feasibility throughout
the online learning process. We address this open challenge with the following
approach. Using a stochastic model-predictive control (MPC) strategy, we
augment safety constraints with affine random variables corresponding to the
instantaneous empirical distributions of modeling error. We obtain these
distributions by evaluating model residuals in real time throughout the online
learning process. By optimizing over the worst case modeling error distribution
defined within a Wasserstein ambiguity set centered about our empirical
distributions, we can approach the nominal constraint boundary in a provably
safe way. We validate the performance of our approach using a case study of
lithium-ion battery fast charging, a relevant and safety-critical energy
systems control application. Our results demonstrate marked improvements in
safety compared to a basic learning model-predictive controller, with
constraints satisfied at every instance during online learning and control.Comment: In review for CDC2
New multiple target tracking strategy using domain knowledge and optimisation
This paper proposes an environment-dependent vehicle dynamic modeling approach considering interactions between the noisy control input of a dynamic model and the environment in order to make best use of domain knowledge. Based on this modeling, a new domain knowledge-aided moving horizon estimation (DMHE) method is proposed for ground moving target tracking. The proposed method incorporates different types of domain knowledge in the estimation process considering both environmental physical constraints and interaction behaviors between targets and the environment. Furthermore, in order to deal with a data association ambiguity problem of multiple-target tracking in a cluttered environment, the DMHE is combined with a multiple-hypothesis tracking structure. Numerical simulation results show that the proposed DMHE-based method and its extension could achieve better performance than traditional tracking methods which utilize no domain knowledge or simple physical constraint information only
A Machine learning approach to POS tagging
We have applied inductive learning of statistical decision trees
and relaxation labelling to the Natural Language Processing (NLP)
task of morphosyntactic disambiguation (Part Of Speech Tagging).
The learning process is supervised and obtains a language
model oriented to resolve POS ambiguities. This model consists
of a set of statistical decision trees expressing distribution of
tags and words in some relevant contexts.
The acquired language models are complete enough to be directly
used as sets of POS disambiguation rules, and include more complex
contextual information than simple collections of n-grams usually
used in statistical taggers.
We have implemented a quite simple and fast tagger that has been
tested and evaluated on the Wall Street Journal (WSJ) corpus with
a remarkable accuracy.
However, better results can be obtained by translating the trees
into rules to feed a flexible relaxation labelling based tagger.
In this direction we describe a tagger which is able to use
information of any kind (n-grams, automatically acquired constraints,
linguistically motivated manually written constraints, etc.), and in
particular to incorporate the machine learned decision trees.
Simultaneously, we address the problem of tagging when only
small training material is available, which is crucial in any process
of constructing, from scratch, an annotated corpus. We show that quite
high accuracy can be achieved with our system in this situation.Postprint (published version
A sketching interface for 3D modeling of polyhedron
We present an intuitive and interactive freehand sketching interface for 3D polyhedrons reconstruction. The interface mimics sketching with pencil on paper and takes freehand sketches as input directly. The sketching environment is natural by allowing sketching with discontinuous, overlapping and multiple strokes. The input sketch is a natural line drawing with hidden lines removed that depicts a 3D object in an isometric view. The line drawing is interpreted by a series of 2D tidy-up processes to produce a vertex-edge graph for 3D reconstruction. A novel reconstruction approach based on three-line-junction analysis and planarity constraint is then used to approximate the 3D geometry and topology of the graph. The reconstructed object can be transformed so that it can be viewed from different viewpoints for interactive design or as immediate feedback to the designers. A new sketch can then be added to the existing 3D object, and reconstructed into 3D by referring to the existing 3D object from the current viewpoint. The incremental modeling enables a 3D object to be reconstructed from multiple sketching sessions from different viewpoints. However, the interface is limited to reconstructing trihedrons from sketches without T-junctions to avoid ambiguity in the hidden topology determination
Distributionally Robust Optimization for Sequential Decision Making
The distributionally robust Markov Decision Process (MDP) approach asks for a
distributionally robust policy that achieves the maximal expected total reward
under the most adversarial distribution of uncertain parameters. In this paper,
we study distributionally robust MDPs where ambiguity sets for the uncertain
parameters are of a format that can easily incorporate in its description the
uncertainty's generalized moment as well as statistical distance information.
In this way, we generalize existing works on distributionally robust MDP with
generalized-moment-based and statistical-distance-based ambiguity sets to
incorporate information from the former class such as moments and dispersions
to the latter class that critically depends on empirical observations of the
uncertain parameters. We show that, under this format of ambiguity sets, the
resulting distributionally robust MDP remains tractable under mild technical
conditions. To be more specific, a distributionally robust policy can be
constructed by solving a sequence of one-stage convex optimization subproblems
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