Hybrid Planning for Decision Making in Self-Adaptive Systems

@article{Pandey2016HybridPF,
  title={Hybrid Planning for Decision Making in Self-Adaptive Systems},
  author={Ashutosh Pandey and Gabriel A. Moreno and Javier C{\'a}mara and David Garlan},
  journal={2016 IEEE 10th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)},
  year={2016},
  pages={130-139}
}
Run-time generation of adaptation plans is a powerful mechanism that helps a self-adaptive system to meet its goals in a dynamically changing environment. In the past, researchers have demonstrated successful use of various automated planning techniques to generate adaptation plans at run time. However, for a planning technique, there is often a trade-off between timeliness and optimality of the solution. For some self-adaptive systems, ideally, one would like to have a planning approach that… Expand
Proposal Hybrid Planning in Self-Adaptive Systems
Self-adaptive software systems determine adaptation plans at run time that seek to change their behavior in response to faults, changing environments and attacks. Therefore, having an appropriateExpand
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References

SHOWING 1-10 OF 51 REFERENCES
Reinforcement learning-based dynamic adaptation planning method for architecture-based self-managed software
  • Dongsun Kim, Sooyong Park
  • Computer Science
  • 2009 ICSE Workshop on Software Engineering for Adaptive and Self-Managing Systems
  • 2009
TLDR
A reinforcement learning-based approach to on-line planning in architecture-based self-management that enables a software system to improve its behavior by learning the results of its behavior and by dynamically changing its plans based on the learning in the presence of environmental changes. Expand
Deployment and dynamic reconfiguration planning for distributed software systems
TLDR
This work describes a tool called Planit, which manages the deployment and reconfiguration of a software system utilizing a temporal planner, and presents the results of a case study in which Planit is applied to a system consisting of various components that communicate across an application-level overlay network. Expand
Optimal planning for architecture-based self-adaptation via model checking of stochastic games
TLDR
This paper proposes an approach to optimal adaptation plan generation for architecture-based self-adaptation via model checking of stochastic multiplayer games (SMGs), which enables trade-off analysis among different qualities by means of utility functions and preferences and explicit modeling of uncertainty in the outcome of adaptation actions and the behavior of the environment. Expand
World Modeling for the Dynamic Construction of Real-Time Control Plans
TLDR
The formal model of agent/environment interactions that CIRCA uses to build control plans is described, and it is shown how those control plans are guaranteed to meet domain requirements. Expand
Proactive self-adaptation under uncertainty: a probabilistic model checking approach
TLDR
The key idea is to use a formal model of the adaptive system in which the adaptation decision is left underspecified through nondeterminism, and have the model checker resolve the nondeterministic choices so that the accumulated utility over the horizon is maximized. Expand
The FF Planning System: Fast Plan Generation Through Heuristic Search
TLDR
A novel search strategy is introduced that combines hill-climbing with systematic search, and it is shown how other powerful heuristic information can be extracted and used to prune the search space. Expand
A Cognitive Model of Planning
TLDR
A cognitive model of the planning process that generalizes the theoretical architecture of the Hearsay-II system and illustrates its assumptions with a “thinking aloud” protocol is presented and the performance of a computer simulation of the model is described. Expand
Fast Planning Through Planning Graph Analysis
TLDR
A new approach to planning in STRIPS-like domains based on constructing and analyzing a compact structure the authors call a Planning Graph is introduced, and a new planner, Graphplan, is described that uses this paradigm. Expand
Planning as heuristic search
TLDR
A family of heuristic search planners are studied based on a simple and general heuristic that assumes that action preconditions are independent, which is used in the context of best-first and hill-climbing search algorithms, and tested over a large collection of domains. Expand
LPG-TD : a Fully Automated Planner for PDDL 2 . 2 Domains
Like the previous version of LPG, the new version is based on a stochastic local search in the space of particular “action graphs” derived from the planning problem specification. In LPG-TD, thisExpand
...
1
2
3
4
5
...