439 research outputs found
Exploiting Polyhedral Symmetries in Social Choice
A large amount of literature in social choice theory deals with quantifying
the probability of certain election outcomes. One way of computing the
probability of a specific voting situation under the Impartial Anonymous
Culture assumption is via counting integral points in polyhedra. Here, Ehrhart
theory can help, but unfortunately the dimension and complexity of the involved
polyhedra grows rapidly with the number of candidates. However, if we exploit
available polyhedral symmetries, some computations become possible that
previously were infeasible. We show this in three well known examples:
Condorcet's paradox, Condorcet efficiency of plurality voting and in Plurality
voting vs Plurality Runoff.Comment: 14 pages; with minor improvements; to be published in Social Choice
and Welfar
Quantum Algorithm Implementations for Beginners
As quantum computers become available to the general public, the need has
arisen to train a cohort of quantum programmers, many of whom have been
developing classical computer programs for most of their careers. While
currently available quantum computers have less than 100 qubits, quantum
computing hardware is widely expected to grow in terms of qubit count, quality,
and connectivity. This review aims to explain the principles of quantum
programming, which are quite different from classical programming, with
straightforward algebra that makes understanding of the underlying fascinating
quantum mechanical principles optional. We give an introduction to quantum
computing algorithms and their implementation on real quantum hardware. We
survey 20 different quantum algorithms, attempting to describe each in a
succinct and self-contained fashion. We show how these algorithms can be
implemented on IBM's quantum computer, and in each case, we discuss the results
of the implementation with respect to differences between the simulator and the
actual hardware runs. This article introduces computer scientists, physicists,
and engineers to quantum algorithms and provides a blueprint for their
implementations
Techniques for the realization of ultra- reliable spaceborne computer Final report
Bibliography and new techniques for use of error correction and redundancy to improve reliability of spaceborne computer
Obtaining representations for probabilities of voting outcomes with effectively unlimited precision integer arithmetic
A procedure is developed to obtain representations for the probability of election outcomes with the Impartial Anonymous Culture Condition and the Maximal Culture Condition. The procedure is based upon a process of performing arithmetic with integers, while maintaining absolute precision with very large integer numbers. The procedure is then used to develop probability representations for a number of different voting outcomes, which have to date been considered to be intractable to obtain with the use of standard algebraic techniques.
Computer Science Principles with Python
This textbook is intended to be used for a first course in computer science, such as the College Boardâs Advanced Placement course known as AP Computer Science Principles (CSP). This book includes all the topics on the CSP exam, plus some additional topics. It takes a breadth-first approach, with an emphasis on the principles which form the foundation for hardware and software. No prior experience with programming should be required to use this book. This version of the book uses the Python programming language.https://rdw.rowan.edu/oer/1024/thumbnail.jp
Computer Science Principles with C++
This textbook is intended to be used for a first course in computer science, such as the College Boardâs Advanced Placement course known as AP Computer Science Principles (CSP). This book includes all the topics on the CSP exam, plus some additional topics. It takes a breadth-first approach, with an emphasis on the principles which form the foundation for hardware and software. No prior experience with programming should be required to use this book. This version of the book uses the C++ programming language.https://rdw.rowan.edu/oer/1025/thumbnail.jp
Formal methods and digital systems validation for airborne systems
This report has been prepared to supplement a forthcoming chapter on formal methods in the FAA Digital Systems Validation Handbook. Its purpose is as follows: to outline the technical basis for formal methods in computer science; to explain the use of formal methods in the specification and verification of software and hardware requirements, designs, and implementations; to identify the benefits, weaknesses, and difficulties in applying these methods to digital systems used on board aircraft; and to suggest factors for consideration when formal methods are offered in support of certification. These latter factors assume the context for software development and assurance described in RTCA document DO-178B, 'Software Considerations in Airborne Systems and Equipment Certification,' Dec. 1992
Advancing the Applicability of Reinforcement Learning to Autonomous Control
ï»żMit dateneffizientem Reinforcement Learning (RL) konnten
beeindruckendeErgebnisse erzielt werden, z.B. fĂŒr die Regelung von
Gasturbinen. In derPraxis erfordert die Anwendung von RL jedoch noch viel
manuelle Arbeit, wasbisher RL fĂŒr die autonome Regelung untauglich
erscheinen lieĂ. Dievorliegende Arbeit adressiert einige der verbleibenden
Probleme, insbesonderein Bezug auf die ZuverlÀssigkeit der
Policy-Erstellung.
Es werden zunÀchst RL-Probleme mit diskreten Zustands- und
AktionsrĂ€umenbetrachtet. FĂŒr solche Probleme wird hĂ€ufig ein MDP aus
BeobachtungengeschÀtzt, um dann auf Basis dieser MDP-SchÀtzung eine Policy
abzuleiten. DieArbeit beschreibt, wie die SchÀtzer-Unsicherheit des MDP in
diePolicy-Erstellung eingebracht werden kann, um mit diesem Wissen das
Risikoeiner schlechten Policy aufgrund einer fehlerhaften MDP-SchÀtzung
zuverringern. AuĂerdem wird so effiziente Exploration sowie
Policy-Bewertungermöglicht.
AnschlieĂend wendet sich die Arbeit Problemen mit
kontinuierlichenZustandsrÀumen zu und konzentriert sich auf auf
RL-Verfahren, welche aufFitted Q-Iteration (FQI) basieren, insbesondere
Neural Fitted Q-Iteration(NFQ). Zwar ist NFQ sehr dateneffizient, jedoch
nicht so zuverlĂ€ssig, wie fĂŒrdie autonome Regelung nötig wĂ€re. Die Arbeit
schlÀgt die Verwendung vonEnsembles vor, um die ZuverlÀssigkeit von NFQ zu
erhöhen. Es werden eine Reihevon Möglichkeiten der Ensemble-Nutzung
entworfen und evaluiert. Bei allenbetrachteten RL-Problemen sorgen
Ensembles fĂŒr eine zuverlĂ€ssigere Erstellungguter Policies.
Im nÀchsten Schritt werden Möglichkeiten der Policy-Bewertung
beikontinuierlichen ZustandsrÀumen besprochen. Die Arbeit schlÀgt vor,
FittedPolicy Evaluation (FPE), eine Variante von FQI fĂŒr Policy Evaluation,
mitanderen Regressionsverfahren und/oder anderen DatensÀtzen zu
kombinieren, umein MaĂ fĂŒr die Policy-QualitĂ€t zu erhalten. Experimente
zeigen, dassExtra-Tree-FPE ein realistisches QualitĂ€tsmaĂ fĂŒr
NFQ-generierte Policies liefernkann.
SchlieĂlich kombiniert die Arbeit Ensembles und Policy-Bewertung, um mit
sichÀndernden RL-Problemen umzugehen. Der wesentliche Beitrag ist das
EvolvingEnsemble, dessen Policy sich langsam Àndert, indem alte,
untaugliche Policiesentfernt und neue hinzugefĂŒgt werden. Es zeigt sich,
dass das EvolvingEnsemble deutlich besser funktioniert als einfachere
AnsÀtze.With data-efficient reinforcement learning (RL) methods impressive
resultscould be achieved, e.g., in the context of gas turbine control.
However, inpractice the application of RL still requires much human
intervention, whichhinders the application of RL to autonomous control.
This thesis addressessome of the remaining problems, particularly regarding
the reliability of thepolicy generation process.
The thesis first discusses RL problems with discrete state and action
spaces.In that context, often an MDP is estimated from observations. It is
describedhow to incorporate the estimators' uncertainties into the policy
generationprocess. This information can then be used to reduce the risk of
obtaining apoor policy due to flawed MDP estimates. Moreover, it is
discussed how to usethe knowledge of uncertainty for efficient exploration
and the assessment ofpolicy quality without requiring the policy's
execution.
The thesis then moves on to continuous state problems and focuses on
methodsbased on fitted Q-iteration (FQI), particularly neural fitted
Q-iteration(NFQ). Although NFQ has proven to be very data-efficient, it is
not asreliable as required for autonomous control. The thesis proposes to
useensembles to increase reliability. Several ways of ensemble usage in an
NFQcontext are discussed and evaluated on a number of benchmark domains. It
showsthat in all considered domains with ensembles good policies can be
producedmore reliably.
Next, policy assessment in continuous domains is discussed. The
thesisproposes to use fitted policy evaluation (FPE), an adaptation of FQI
to policyevaluation, combined with a different function approximator and/or
differentdataset to obtain a measure for policy quality. Results of
experiments showthat extra-tree FPE, applied to policies generated by NFQ,
produces valuefunctions that can well be used to reason about the true
policy quality.
Finally, the thesis combines ensembles and policy assessment to derive
methodsthat can deal with changing environments. The major contribution is
theevolving ensemble. The policy of the evolving ensemble changes slowly as
newpolicies are added and old policies removed. It turns out that the
evolvingensemble approaches work considerably better than simpler
approaches likesingle policies learned with recent observations or simple
ensembles
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