635 research outputs found
Robust Control of Uncertain Markov Decision Processes with Temporal Logic Specifications
We present a method for designing robust controllers for dynamical systems with linear temporal logic specifications. We abstract the original system by a finite Markov Decision Process (MDP) that has transition probabilities in a specified uncertainty set. A robust control policy for the MDP is generated that maximizes the worst-case probability of satisfying the specification over all transition probabilities in the uncertainty set. To do this, we use a procedure from probabilistic model checking to combine the system model with an automaton representing the specification. This new MDP is then transformed into an equivalent form that satisfies assumptions for stochastic shortest path dynamic programming. A robust version of dynamic programming allows us to solve for a -suboptimal robust control policy with time complexity times that for the non-robust case. We then implement this control policy on the original dynamical system
Efficient parameter search for qualitative models of regulatory networks using symbolic model checking
Investigating the relation between the structure and behavior of complex
biological networks often involves posing the following two questions: Is a
hypothesized structure of a regulatory network consistent with the observed
behavior? And can a proposed structure generate a desired behavior? Answering
these questions presupposes that we are able to test the compatibility of
network structure and behavior. We cast these questions into a parameter search
problem for qualitative models of regulatory networks, in particular
piecewise-affine differential equation models. We develop a method based on
symbolic model checking that avoids enumerating all possible parametrizations,
and show that this method performs well on real biological problems, using the
IRMA synthetic network and benchmark experimental data sets. We test the
consistency between the IRMA network structure and the time-series data, and
search for parameter modifications that would improve the robustness of the
external control of the system behavior
Online Horizon Selection in Receding Horizon Temporal Logic Planning
Temporal logics have proven effective for correct-by-construction synthesis of controllers for a wide range of applications. Receding horizon frameworks mitigate the computational intractability of reactive synthesis for temporal logic, but have thus far been limited by pursuing a single sequence of short horizon problems to the current goal. We propose a receding horizon algorithm for reactive synthesis that automatically determines a path to the currently pursued goal at runtime, in response to a nondeterministic environment. This is achieved by allowing each short horizon to have multiple local goals, and determining which local goal to pursue based on the current global goal, currently perceived environment and a pre-computed invariant dependent on each global goal. We demonstrate the utility of this additional flexibility in grant-response tasks, using a search-and-rescue example. Moreover, we show that these goal-dependent invariants mitigate the conservativeness of the receding horizon approach
Dynamics-Based Reactive Synthesis and Automated Revisions for High-Level Robot Control
The aim of this work is to address issues where formal specifications cannot
be realized on a given dynamical system subjected to a changing environment.
Such failures occur whenever the dynamics of the system restrict the robot in
such a way that the environment may prevent the robot from progressing safely
to its goals. We provide a framework that automatically synthesizes revisions
to such specifications that restrict the assumed behaviors of the environment
and the behaviors of the system. We provide a means for explaining such
modifications to the user in a concise, easy-to-understand manner. Integral to
the framework is a new algorithm for synthesizing controllers for reactive
specifications that include a discrete representation of the robot's dynamics.
The new approach is demonstrated with a complex task implemented using a
unicycle model.Comment: 25 pages, 8 figure
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