Publication Date:
2012-10-13
Description:
Multi-agent planning (MAP) approaches are typically oriented at solving loosely coupled problems, being ineffective to deal with more complex, strongly related problems. In most cases, agents work under complete information, building complete knowledge bases. The present article introduces a general-purpose MAP framework designed to tackle problems of any coupling levels under incomplete information. Agents in our MAP model are partially unaware of the information managed by the rest of agents and share only the critical information that affects other agents, thus maintaining a distributed vision of the task. Agents solve MAP tasks through the adoption of an iterative refinement planning procedure that uses single-agent planning technology. In particular, agents will devise refinements through the partial-order planning paradigm, a flexible framework to build refinement plans leaving unsolved details that will be gradually completed by means of new refinements. Our proposal is supported with the implementation of a fully operative MAP system and we show various experiments when running our system over different types of MAP problems, from the most strongly related to the most loosely coupled. Content Type Journal Article Category Regular Paper Pages 1-38 DOI 10.1007/s10115-012-0569-7 Authors Alejandro Torreño, Departamento Sistemas Informáticos y Computación, Universitat Politècnica de València, Valencia, Spain Eva Onaindia, Departamento Sistemas Informáticos y Computación, Universitat Politècnica de València, Valencia, Spain Óscar Sapena, Departamento Sistemas Informáticos y Computación, Universitat Politècnica de València, Valencia, Spain Journal Knowledge and Information Systems Online ISSN 0219-3116 Print ISSN 0219-1377
Print ISSN:
0219-1377
Electronic ISSN:
0219-3116
Topics:
Computer Science
