142,857 research outputs found
Structural Synthesis for GXW Specifications
We define the GXW fragment of linear temporal logic (LTL) as the basis for
synthesizing embedded control software for safety-critical applications. Since
GXW includes the use of a weak-until operator we are able to specify a number
of diverse programmable logic control (PLC) problems, which we have compiled
from industrial training sets. For GXW controller specifications, we develop a
novel approach for synthesizing a set of synchronously communicating
actor-based controllers. This synthesis algorithm proceeds by means of
recursing over the structure of GXW specifications, and generates a set of
dedicated and synchronously communicating sub-controllers according to the
formula structure. In a subsequent step, 2QBF constraint solving identifies and
tries to resolve potential conflicts between individual GXW specifications.
This structural approach to GXW synthesis supports traceability between
requirements and the generated control code as mandated by certification
regimes for safety-critical software. Synthesis for GXW specifications is in
PSPACE compared to 2EXPTIME-completeness of full-fledged LTL synthesis. Indeed
our experimental results suggest that GXW synthesis scales well to
industrial-sized control synthesis problems with 20 input and output ports and
beyond.Comment: The long (including appendix) version being reviewed by CAV'16
program committee. Compared to the submitted version, one author (out of her
wish) is moved to the Acknowledgement. (v2) Corrected typos. (v3) Add an
additional remark over environment assumption and easy corner case
Monte Carlo Bayesian Reinforcement Learning
Bayesian reinforcement learning (BRL) encodes prior knowledge of the world in
a model and represents uncertainty in model parameters by maintaining a
probability distribution over them. This paper presents Monte Carlo BRL
(MC-BRL), a simple and general approach to BRL. MC-BRL samples a priori a
finite set of hypotheses for the model parameter values and forms a discrete
partially observable Markov decision process (POMDP) whose state space is a
cross product of the state space for the reinforcement learning task and the
sampled model parameter space. The POMDP does not require conjugate
distributions for belief representation, as earlier works do, and can be solved
relatively easily with point-based approximation algorithms. MC-BRL naturally
handles both fully and partially observable worlds. Theoretical and
experimental results show that the discrete POMDP approximates the underlying
BRL task well with guaranteed performance.Comment: Appears in Proceedings of the 29th International Conference on
Machine Learning (ICML 2012
2Planning for Contingencies: A Decision-based Approach
A fundamental assumption made by classical AI planners is that there is no
uncertainty in the world: the planner has full knowledge of the conditions
under which the plan will be executed and the outcome of every action is fully
predictable. These planners cannot therefore construct contingency plans, i.e.,
plans in which different actions are performed in different circumstances. In
this paper we discuss some issues that arise in the representation and
construction of contingency plans and describe Cassandra, a partial-order
contingency planner. Cassandra uses explicit decision-steps that enable the
agent executing the plan to decide which plan branch to follow. The
decision-steps in a plan result in subgoals to acquire knowledge, which are
planned for in the same way as any other subgoals. Cassandra thus distinguishes
the process of gathering information from the process of making decisions. The
explicit representation of decisions in Cassandra allows a coherent approach to
the problems of contingent planning, and provides a solid base for extensions
such as the use of different decision-making procedures.Comment: See http://www.jair.org/ for any accompanying file
Supervised Autonomous Locomotion and Manipulation for Disaster Response with a Centaur-like Robot
Mobile manipulation tasks are one of the key challenges in the field of
search and rescue (SAR) robotics requiring robots with flexible locomotion and
manipulation abilities. Since the tasks are mostly unknown in advance, the
robot has to adapt to a wide variety of terrains and workspaces during a
mission. The centaur-like robot Centauro has a hybrid legged-wheeled base and
an anthropomorphic upper body to carry out complex tasks in environments too
dangerous for humans. Due to its high number of degrees of freedom, controlling
the robot with direct teleoperation approaches is challenging and exhausting.
Supervised autonomy approaches are promising to increase quality and speed of
control while keeping the flexibility to solve unknown tasks. We developed a
set of operator assistance functionalities with different levels of autonomy to
control the robot for challenging locomotion and manipulation tasks. The
integrated system was evaluated in disaster response scenarios and showed
promising performance.Comment: In Proceedings of IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS), Madrid, Spain, October 201
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