1,914 research outputs found
Bayesian Nonparametric Methods for Partially-Observable Reinforcement Learning
Making intelligent decisions from incomplete information is critical in many applications: for example, robots must choose actions based on imperfect sensors, and speech-based interfaces must infer a user’s needs from noisy microphone inputs. What makes these tasks hard is that often we do not have a natural representation with which to model the domain and use for choosing actions; we must learn about the domain’s properties while simultaneously performing the task. Learning a representation also involves trade-offs between modeling the data that we have seen previously and being able to make predictions about new data. This article explores learning representations of stochastic systems using Bayesian nonparametric statistics. Bayesian nonparametric methods allow the sophistication of a representation to scale gracefully with the complexity in the data. Our main contribution is a careful empirical evaluation of how representations learned using Bayesian nonparametric methods compare to other standard learning approaches, especially in support of planning and control. We show that the Bayesian aspects of the methods result in achieving state-of-the-art performance in decision making with relatively few samples, while the nonparametric aspects often result in fewer computations. These results hold across a variety of different techniques for choosing actions given a representation
Linear Parametric Model Checking of Timed Automata
We present an extension of the model checker Uppaal capable of synthesizing linear parameter constraints for the correctness ofparametric timed automata. The symbolic representation of the (parametric) state-space is shown to be correct. A second contribution of thispaper is the identification of a subclass of parametric timed automata(L/U automata), for which the emptiness problem is decidable, contraryto the full class where it is know to be undecidable. Also we present anumber of lemmas enabling the verification effort to be reduced for L/Uautomata in some cases. We illustrate our approach by deriving linearparameter constraints for a number of well-known case studies from theliterature (exhibiting a flaw in a published paper)
Applications of complex adaptive systems approaches to coastal systems
This thesis investigatesth e application of complex adaptives ystemsa pproaches
(e. g. Artificial Neural Networks and Evolutionary Computation) to the study of coastal
hydrodynamica nd morphodynamicb ehaviour.T raditionally, nearshorem orphologicalc oastal
systems tudiesh ave developeda n understandingo f thosep hysicalp rocesseso ccurringo n both
short temporal, and small spatial scales with a large degree of success. The associated
approachesa nd conceptsu sedt o study the coastals ystema t theses calesh ave Primarily been
linear in nature.H owever,w hent hesea pproachetso studyingt he coastals ystema re extendedto
investigating larger temporal and spatial scales,w hich are commensuratew ith the aims of
coastal managementr, esults have had less success.T he lack of successi n developing an
understandingo f large scalec oastalb ehaviouri s to a large extent attributablet o the complex
behavioura ssociatedw ith the coastals ystem.I bis complexity arises as a result of both the
stochastic and chaotic nature of the coastal system. This allows small scale system
understandingto be acquiredb ut preventst he Largers caleb ehaviourt o be predictede ffectively.
This thesis presentsf our hydro-morphodynamicc ase studies to demonstratet he utility of
complex adaptives ystema pproachesfo r studying coastals ystems.T he first two demonstrate
the application of Artificial Neural Networks, whilst the latter two illustrate the application of
EvolutionaryC omputation.C aseS tudy #I considerst he natureo f the discrepancyb etweent he
observedl ocation of wave breakingp atternso ver submergeds andbarsa nd the actual sandbar
locations.A rtificial Neural Networks were able to quantitativelyc orrectt he observedlo cations
to produce reliable estimates of the actual sand bar locations. Case Study #2 considers the
developmenot f an approachf or the discriminationo f shorelinel ocation in video imagesf or the
productiono f intertidal mapso f the nearshorer egion. In this caset he systemm odelledb y the
Artificial Neural Network is the nature of the discrimination model carried out by the eye in
delineating a shoreline feature between regions of sand and water. The Artificial Neural
Network approachw as shownt o robustly recognisea rangeo f shorelinef eaturesa t a variety of
beaches and hydrodynamic settings. Case Study #3 was the only purely hydrodynamic study
consideredin the thesis.I t investigatedth e use of Evolutionary Computationt o provide means
of developing a parametric description of directional wave spectra in both reflective and nonreflective
conditions. It is shown to provide a unifying approach which produces results which
surpassedth ose achievedb y traditional analysisa pproachese vent hough this may not strictly
have been considered as a fidly complex system. Case Study #4 is the most ambitious
applicationa nd addressetsh e needf or data reductiona s a precursorw hen trying to study large
scalem orphodynamicd ata sets.I t utilises EvolutionaryC omputationa pproachesto extractt he
significant morphodynamic variability evidenced in both directly and remotely sampled
nearshorem orphologiesS. ignificantd atar eductioni s achievedw hilst reWning up to 90% of the
original variability in the data sets.
These case studies clearly demonstrate the ability of complex adaptive systems to be
successfidly applied to coastal system studies. This success has been shown to equal and
sometimess urpasst he results that may be obtained by traditional approachesT. he strong
performance of Complex Adaptive System approaches is closely linked to the level of
complexity or non-linearity of the system being studied. Based on a qualitative evaluation,
Evolutionary Computation was shown to demonstrate an advantage over Artificial Neural
Networks in terms of the level of new insights which may be obtained. However, utility also
needs to consider general ease of applicability and ease of implementation of the study
approach.I n this sense,A rtificial Neural Networks demonstratem ore utility for the study of
coastals ystems.T he qualitative assessmenatp proachu sedt o evaluatet he cases tudiesi n this
thesis, may be used as a guide for choosingt he appropriatenesso f either Artificial Neural
Networks or Evolutionary Computation for future coastal system studies
Distance Oracles for Time-Dependent Networks
We present the first approximate distance oracle for sparse directed networks
with time-dependent arc-travel-times determined by continuous, piecewise
linear, positive functions possessing the FIFO property.
Our approach precomputes approximate distance summaries from
selected landmark vertices to all other vertices in the network. Our oracle
uses subquadratic space and time preprocessing, and provides two sublinear-time
query algorithms that deliver constant and approximate
shortest-travel-times, respectively, for arbitrary origin-destination pairs in
the network, for any constant . Our oracle is based only on
the sparsity of the network, along with two quite natural assumptions about
travel-time functions which allow the smooth transition towards asymmetric and
time-dependent distance metrics.Comment: A preliminary version appeared as Technical Report ECOMPASS-TR-025 of
EU funded research project eCOMPASS (http://www.ecompass-project.eu/). An
extended abstract also appeared in the 41st International Colloquium on
Automata, Languages, and Programming (ICALP 2014, track-A
Formal Methods for Autonomous Systems
Formal methods refer to rigorous, mathematical approaches to system
development and have played a key role in establishing the correctness of
safety-critical systems. The main building blocks of formal methods are models
and specifications, which are analogous to behaviors and requirements in system
design and give us the means to verify and synthesize system behaviors with
formal guarantees.
This monograph provides a survey of the current state of the art on
applications of formal methods in the autonomous systems domain. We consider
correct-by-construction synthesis under various formulations, including closed
systems, reactive, and probabilistic settings. Beyond synthesizing systems in
known environments, we address the concept of uncertainty and bound the
behavior of systems that employ learning using formal methods. Further, we
examine the synthesis of systems with monitoring, a mitigation technique for
ensuring that once a system deviates from expected behavior, it knows a way of
returning to normalcy. We also show how to overcome some limitations of formal
methods themselves with learning. We conclude with future directions for formal
methods in reinforcement learning, uncertainty, privacy, explainability of
formal methods, and regulation and certification
Automatic Gridding for DNA Microarray Image Using Image Projection Profile
DNA microarray is powerful tool and widely used in many areas.
DNA microarray is produced from control and test tissue sample cDNAs, which
are labeled with two different fluorescent dyes. After hybridization using a laser
scanner, microarray images are obtained. Image analysis play an important role
in extracting fluorescence intensity from microarray image. First step in
microarray image analysis is addressing, that is finding areas in the image on
which contain one spot using gird lines. This step can be done by either
manually or automatically. In this paper we propose an efficient and simple
automatic gridding for microarray image analysis using image projection profile,
base on fact that microarray image has local minimum and maximum intensity
at background and foreground areas respectively. Grid lines are obtained by
finding local minimum of vertical and horizontal projection profile. This
algorithm has been implemented in MATLAB and tested with several
microarray image
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