308 research outputs found
Bayesian Verification under Model Uncertainty
Machine learning enables systems to build and update domain models based on
runtime observations. In this paper, we study statistical model checking and
runtime verification for systems with this ability. Two challenges arise: (1)
Models built from limited runtime data yield uncertainty to be dealt with. (2)
There is no definition of satisfaction w.r.t. uncertain hypotheses. We propose
such a definition of subjective satisfaction based on recently introduced
satisfaction functions. We also propose the BV algorithm as a Bayesian solution
to runtime verification of subjective satisfaction under model uncertainty. BV
provides user-definable stochastic bounds for type I and II errors. We discuss
empirical results from an example application to illustrate our ideas.Comment: Accepted at SEsCPS @ ICSE 201
Stacked Thompson Bandits
We introduce Stacked Thompson Bandits (STB) for efficiently generating plans
that are likely to satisfy a given bounded temporal logic requirement. STB uses
a simulation for evaluation of plans, and takes a Bayesian approach to using
the resulting information to guide its search. In particular, we show that
stacking multiarmed bandits and using Thompson sampling to guide the action
selection process for each bandit enables STB to generate plans that satisfy
requirements with a high probability while only searching a fraction of the
search space.Comment: Accepted at SEsCPS @ ICSE 201
Scalable Multiagent Coordination with Distributed Online Open Loop Planning
We propose distributed online open loop planning (DOOLP), a general framework
for online multiagent coordination and decision making under uncertainty. DOOLP
is based on online heuristic search in the space defined by a generative model
of the domain dynamics, which is exploited by agents to simulate and evaluate
the consequences of their potential choices.
We also propose distributed online Thompson sampling (DOTS) as an effective
instantiation of the DOOLP framework. DOTS models sequences of agent choices by
concatenating a number of multiarmed bandits for each agent and uses Thompson
sampling for dealing with action value uncertainty. The Bayesian approach
underlying Thompson sampling allows to effectively model and estimate
uncertainty about (a) own action values and (b) other agents' behavior. This
approach yields a principled and statistically sound solution to the
exploration-exploitation dilemma when exploring large search spaces with
limited resources.
We implemented DOTS in a smart factory case study with positive empirical
results. We observed effective, robust and scalable planning and coordination
capabilities even when only searching a fraction of the potential search space
QoS-Aware Multi-Armed Bandits
Motivated by runtime verification of QoS requirements in self-adaptive and
self-organizing systems that are able to reconfigure their structure and
behavior in response to runtime data, we propose a QoS-aware variant of
Thompson sampling for multi-armed bandits. It is applicable in settings where
QoS satisfaction of an arm has to be ensured with high confidence efficiently,
rather than finding the optimal arm while minimizing regret. Preliminary
experimental results encourage further research in the field of QoS-aware
decision making.Comment: Accepted at IEEE Workshop on Quality Assurance for Self-adaptive
Self-organising Systems, FAS* 201
Towards self-duality of semidistributive artinian rings
AbstractLet R be a semidistributive artinian ring. An ideal I0 of R is constructed such that every factor ring of RI0 is self-dual where the ideal I0 is fairly small: for any two primitive idempotents e1 and e2 of R the e1Re1−e2Re2-bimodule e1I0e2 is simple on each side
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