31 research outputs found
On the Computational Complexity of Stochastic Controller Optimization in POMDPs
We show that the problem of finding an optimal stochastic 'blind' controller
in a Markov decision process is an NP-hard problem. The corresponding decision
problem is NP-hard, in PSPACE, and SQRT-SUM-hard, hence placing it in NP would
imply breakthroughs in long-standing open problems in computer science. Our
result establishes that the more general problem of stochastic controller
optimization in POMDPs is also NP-hard. Nonetheless, we outline a special case
that is convex and admits efficient global solutions.Comment: Corrected error in the proof of Theorem 2, and revised Section
Stochastic Control via Entropy Compression
We consider an agent trying to bring a system to an acceptable state by
repeated probabilistic action. Several recent works on algorithmizations of the
Lovasz Local Lemma (LLL) can be seen as establishing sufficient conditions for
the agent to succeed. Here we study whether such stochastic control is also
possible in a noisy environment, where both the process of state-observation
and the process of state-evolution are subject to adversarial perturbation
(noise). The introduction of noise causes the tools developed for LLL
algorithmization to break down since the key LLL ingredient, the sparsity of
the causality (dependence) relationship, no longer holds. To overcome this
challenge we develop a new analysis where entropy plays a central role, both to
measure the rate at which progress towards an acceptable state is made and the
rate at which noise undoes this progress. The end result is a sufficient
condition that allows a smooth tradeoff between the intensity of the noise and
the amenability of the system, recovering an asymmetric LLL condition in the
noiseless case.Comment: 18 page
INDEMICS: An Interactive High-Performance Computing Framework for Data Intensive Epidemic Modeling
We describe the design and prototype implementation of Indemics (_Interactive; Epi_demic; _Simulation;)âa modeling environment utilizing high-performance computing technologies for supporting complex epidemic simulations. Indemics can support policy analysts and epidemiologists interested in planning and control of pandemics. Indemics goes beyond traditional epidemic simulations by providing a simple and powerful way to represent and analyze policy-based as well as individual-based adaptive interventions. Users can also stop the simulation at any point, assess the state of the simulated system, and add additional interventions. Indemics is available to end-users via a web-based interface.
Detailed performance analysis shows that Indemics greatly enhances the capability and productivity of simulating complex intervention strategies with a marginal decrease in performance. We also demonstrate how Indemics was applied in some real case studies where complex interventions were implemented
Landscape of Machine Implemented Ethics
This paper surveys the state-of-the-art in machine ethics, that is,
considerations of how to implement ethical behaviour in robots, unmanned
autonomous vehicles, or software systems. The emphasis is on covering the
breadth of ethical theories being considered by implementors, as well as the
implementation techniques being used. There is no consensus on which ethical
theory is best suited for any particular domain, nor is there any agreement on
which technique is best placed to implement a particular theory. Another
unresolved problem in these implementations of ethical theories is how to
objectively validate the implementations. The paper discusses the dilemmas
being used as validating 'whetstones' and whether any alternative validation
mechanism exists. Finally, it speculates that an intermediate step of creating
domain-specific ethics might be a possible stepping stone towards creating
machines that exhibit ethical behaviour.Comment: 25 page