VAPA
Verification Approaches for Planning in Agent programming languages
| Coordinatore | IMPERIAL COLLEGE OF SCIENCE, TECHNOLOGY AND MEDICINE
Organization address
address: SOUTH KENSINGTON CAMPUS EXHIBITION ROAD contact info |
| Nazionalità Coordinatore | United Kingdom [UK] |
| Totale costo | 209˙033 € |
| EC contributo | 209˙033 € |
| Programma | FP7-PEOPLE
Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013) |
| Code Call | FP7-PEOPLE-2011-IIF |
| Funding Scheme | MC-IIF |
| Anno di inizio | 0 |
| Periodo (anno-mese-giorno) | 0000-00-00 - 0000-00-00 |
Partecipanti
| # | ||||
|---|---|---|---|---|
| 1 |
IMPERIAL COLLEGE OF SCIENCE, TECHNOLOGY AND MEDICINE
Organization address
address: SOUTH KENSINGTON CAMPUS EXHIBITION ROAD contact info |
UK (LONDON) | coordinator | 209˙033.40 |
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Obiettivo del progetto (Objective)
'As computation becomes more distributed and the information landscape more complex with the increasing amount of information being shared and distributed online, agent-based computation has become a key technology. Recent advances in agent- based software include techniques for verification of a Multiagent Systems (MAS) using model checking, as well as the ability to generate new plans at runtime to allow a agent to cope with unforeseen circumstances. Although model-checking techniques have been employed in the context of planning within a single entity in a fully observable domain in classic AI, agents in a MAS system do not share all their information, and thus operate with only partial information about other agents within the system. The introduction of planning to MAS programming languages have opened new possibilities for the development of flexible systems, however, the planning techniques currently available for MAS planning are still relatively inefficient. In the VAPA project, we aim to improve on the effectiveness of MAS planning under incomplete information by drawing on experiences that have been learned in classical planning via model checking, namely the work of Cimati et al. We thus aim to bring these advances to the MAS setting by designing planning algorithms based on a MAS model checker by mapping MAS planning problems into a model checker such as Model Checker for Multiagent Systems (MCMAS).'