167,970 research outputs found
A Critical Look at the Abstraction Based on Macro-Operators
Abstraction can be an effective technique for dealing with
the complexity of planning tasks. This paper is aimed at assessing and
identifying in which cases abstraction can actually speed-up the overall
search. In fact, it is well known that the impact of abstraction on the
time spent to search for a solution of a planning problem can be positive
or negative, depending on several factors -including the number of objects
defined in the domain, the branching factor, and the plan length.
Experimental results highlight the role of such aspects on the overall performance
of an algorithm that performs the search at the ground-level
only, and compares them with the ones obtained by enforcing abstraction
Hybrid STAN: Identifying and managing combinatorial optimisation sub-problems in planning
It is well-known that planning is hard but it is less well-known how to approach the hard parts of a problem instance effectively. Using static domain analysis techniques we can identify and abstract certain combinatorial sub-problems from a planning instance, and deploy specialised technology to solve these sub-problems in a way that is integrated with the broader planning activities. We have developed a hybrid planning system (STAN4) which brings together alternative planning strategies and specialised algorithms and selects them according to the structure of the planning domain. STAN4 participated successfully in the AIPS-2000 planning competition. We describe how sub-problem abstraction is done, with particular reference to route-planning abstraction, and present some of the competition data to demonstrate the potential power of the hybrid approach
Magnifying Lens Abstraction for Stochastic Games with Discounted and Long-run Average Objectives
Turn-based stochastic games and its important subclass Markov decision
processes (MDPs) provide models for systems with both probabilistic and
nondeterministic behaviors. We consider turn-based stochastic games with two
classical quantitative objectives: discounted-sum and long-run average
objectives. The game models and the quantitative objectives are widely used in
probabilistic verification, planning, optimal inventory control, network
protocol and performance analysis. Games and MDPs that model realistic systems
often have very large state spaces, and probabilistic abstraction techniques
are necessary to handle the state-space explosion. The commonly used
full-abstraction techniques do not yield space-savings for systems that have
many states with similar value, but does not necessarily have similar
transition structure. A semi-abstraction technique, namely Magnifying-lens
abstractions (MLA), that clusters states based on value only, disregarding
differences in their transition relation was proposed for qualitative
objectives (reachability and safety objectives). In this paper we extend the
MLA technique to solve stochastic games with discounted-sum and long-run
average objectives. We present the MLA technique based abstraction-refinement
algorithm for stochastic games and MDPs with discounted-sum objectives. For
long-run average objectives, our solution works for all MDPs and a sub-class of
stochastic games where every state has the same value
- …