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Identifier 000408047
Title Repairing of sequential plans in dynamic environments
Alternative Title Επιδιόρθωση ακολουθιακών σχεδίων ενεργειών σε δυναμικά περιβάλλοντα
Author Γουίδης, Φίλιππος Ε.
Thesis advisor Πλεξουσάκης, Δημήτρης
Reviewer Τσαμαρδινός, Ιωάννης
Φλουρής, Γεώργιος
Abstract Planning is one of the oldest and most fundamental research areas of Artificial Intelligence. Apart from its theoretical importance, it is utilized very frequently in a wide range of practical applications that spans from space missions to factory line production. A common complication that occurs after the production of plans, is their rendering invalid or suboptimal during their execution, due to the dynamic nature of the environments where they are executed. A fast response m echanism can be proven crucial for domains where the assumption of a static environment is very optimistic, if not untenable. This thesis presents an algorithm for plan repairing that utilizes previous information and computational effort, in order to acce lerate the production of new plans that correspond to the altered conditions of their environment. This algorithm is an expansion of the A* algorithm, a standard planning algorithm of the relevant literature, upon which many of the state - of - the - art plann ers are based. This expansion is tailored to the repairing of the plans in non - static environments of certain characteristics. Namely, dynamic goal - sets and modifiable action costs can be addressed. The experimental protocol that we used for the assessment of the algorithm's performance is the following. First, a plan is produced for the initial environment's conditions. Consequently, assuming that the plan has been executed up to a certain percentage , either the problem's goal - set or some of its actions' costs is changed. Finally, the repairing and the A* algorithms are executed from the latter point. The type of domains and problems that we used for the evaluation are standard benchmarks, derived from the international planning competitions. The experimental results indicate that the performance of the algorithm depends from the following factors: the ratio of the original graph search size to the final graph search size, the branching parameter of t he problem, the density of the graph search, the percentage of the original plan already executed and the volume of the changes in the environment. For sparse search graphs and small to moderate environment changes, the algorithm outperforms A*in terms of speed by a factor of 10% to 80% in the majority of the cases, if the percentage of the plan that has been already executed is less than 40% to 50%. We consider that this thesis can provide useful insights and hints towards the development of more efficient plan repairing techniques, since the A* constitutes the backbone of many actual planners. Moreover, we believe that our work can be further improved and expanded, by incorporating new features, such as a decentralized approach and a real - time response functionality.
Language English
Subject Artificial intelligence
Σχεδιασμός ενεργειών
Τεχνητή νοημοσύνη
Issue date 2017-03-17
Collection   Faculty/Department--Faculty of Sciences and Engineering--Department of Computer Science--Post-graduate theses
  Type of Work--Post-graduate theses
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