45,735 research outputs found
Coherent frequentism
By representing the range of fair betting odds according to a pair of
confidence set estimators, dual probability measures on parameter space called
frequentist posteriors secure the coherence of subjective inference without any
prior distribution. The closure of the set of expected losses corresponding to
the dual frequentist posteriors constrains decisions without arbitrarily
forcing optimization under all circumstances. This decision theory reduces to
those that maximize expected utility when the pair of frequentist posteriors is
induced by an exact or approximate confidence set estimator or when an
automatic reduction rule is applied to the pair. In such cases, the resulting
frequentist posterior is coherent in the sense that, as a probability
distribution of the parameter of interest, it satisfies the axioms of the
decision-theoretic and logic-theoretic systems typically cited in support of
the Bayesian posterior. Unlike the p-value, the confidence level of an interval
hypothesis derived from such a measure is suitable as an estimator of the
indicator of hypothesis truth since it converges in sample-space probability to
1 if the hypothesis is true or to 0 otherwise under general conditions.Comment: The confidence-measure theory of inference and decision is explicitly
extended to vector parameters of interest. The derivation of upper and lower
confidence levels from valid and nonconservative set estimators is formalize
The Assistive Multi-Armed Bandit
Learning preferences implicit in the choices humans make is a well studied
problem in both economics and computer science. However, most work makes the
assumption that humans are acting (noisily) optimally with respect to their
preferences. Such approaches can fail when people are themselves learning about
what they want. In this work, we introduce the assistive multi-armed bandit,
where a robot assists a human playing a bandit task to maximize cumulative
reward. In this problem, the human does not know the reward function but can
learn it through the rewards received from arm pulls; the robot only observes
which arms the human pulls but not the reward associated with each pull. We
offer sufficient and necessary conditions for successfully assisting the human
in this framework. Surprisingly, better human performance in isolation does not
necessarily lead to better performance when assisted by the robot: a human
policy can do better by effectively communicating its observed rewards to the
robot. We conduct proof-of-concept experiments that support these results. We
see this work as contributing towards a theory behind algorithms for
human-robot interaction.Comment: Accepted to HRI 201
Reinforcement Learning via AIXI Approximation
This paper introduces a principled approach for the design of a scalable
general reinforcement learning agent. This approach is based on a direct
approximation of AIXI, a Bayesian optimality notion for general reinforcement
learning agents. Previously, it has been unclear whether the theory of AIXI
could motivate the design of practical algorithms. We answer this hitherto open
question in the affirmative, by providing the first computationally feasible
approximation to the AIXI agent. To develop our approximation, we introduce a
Monte Carlo Tree Search algorithm along with an agent-specific extension of the
Context Tree Weighting algorithm. Empirically, we present a set of encouraging
results on a number of stochastic, unknown, and partially observable domains.Comment: 8 LaTeX pages, 1 figur
Model and Reinforcement Learning for Markov Games with Risk Preferences
We motivate and propose a new model for non-cooperative Markov game which
considers the interactions of risk-aware players. This model characterizes the
time-consistent dynamic "risk" from both stochastic state transitions (inherent
to the game) and randomized mixed strategies (due to all other players). An
appropriate risk-aware equilibrium concept is proposed and the existence of
such equilibria is demonstrated in stationary strategies by an application of
Kakutani's fixed point theorem. We further propose a simulation-based
Q-learning type algorithm for risk-aware equilibrium computation. This
algorithm works with a special form of minimax risk measures which can
naturally be written as saddle-point stochastic optimization problems, and
covers many widely investigated risk measures. Finally, the almost sure
convergence of this simulation-based algorithm to an equilibrium is
demonstrated under some mild conditions. Our numerical experiments on a two
player queuing game validate the properties of our model and algorithm, and
demonstrate their worth and applicability in real life competitive
decision-making.Comment: 38 pages, 6 tables, 5 figure
An empirical learning-based validation procedure for simulation workflow
Simulation workflow is a top-level model for the design and control of
simulation process. It connects multiple simulation components with time and
interaction restrictions to form a complete simulation system. Before the
construction and evaluation of the component models, the validation of
upper-layer simulation workflow is of the most importance in a simulation
system. However, the methods especially for validating simulation workflow is
very limit. Many of the existing validation techniques are domain-dependent
with cumbersome questionnaire design and expert scoring. Therefore, this paper
present an empirical learning-based validation procedure to implement a
semi-automated evaluation for simulation workflow. First, representative
features of general simulation workflow and their relations with validation
indices are proposed. The calculation process of workflow credibility based on
Analytic Hierarchy Process (AHP) is then introduced. In order to make full use
of the historical data and implement more efficient validation, four learning
algorithms, including back propagation neural network (BPNN), extreme learning
machine (ELM), evolving new-neuron (eNFN) and fast incremental gaussian mixture
model (FIGMN), are introduced for constructing the empirical relation between
the workflow credibility and its features. A case study on a landing-process
simulation workflow is established to test the feasibility of the proposed
procedure. The experimental results also provide some useful overview of the
state-of-the-art learning algorithms on the credibility evaluation of
simulation models
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