[ Please note: The project has been discontinued as of May 31, 2005 and is superseded by the projects of the ii Labs. There won't be further updates to these pages. ]

(Related publication: [PUBLink])

The feature of being able to handle dynamic changes of the problem specification is also very important for the computer-games domain because goods, their destinations and transportation facilities vary over time.

To test the approach's ability to handle dynamics, a random package is canceled every 1,000 iterations (no matter if it is already on the way or has been delivered) and a new package is inserted (its initial location and destination being decided at random). The time point for the Begin variables of the Precondition Tasks of the S_Goals's Task Constraint is increased by MaximallyNecessaryActionsPerPackage × MaximalActionLength + 1, i.e., for the 6-1 modifications, by 12 × 200 + 1 = 2401, to ensure the existence of a satisfiable plan. The figure below shows the results for Problem 6-1a. The optimization can quickly recover after a change takes place (the second figure shows the view from above).

The figures below (dynamic adaptation on the left; recomputation on the right) contrast cuttings of the dynamic solution with test runs in which the computation was restarted from scratch for a change. It is clear that the dynamic adaptation converges much more quickly. Larger problems and higher dynamics can mean that a problem cannot even be tackled by a recomputation from scratch because there is not enough time for a regeneration of the problem's representation.

For questions, comments or suggestions, please contact us.

Last update:
May 20, 2001 by Alexander Nareyek