{"type":"ScholarlyArticle","title":"Adaptation of Organizational Models for Multi-Agent Systems Based on Max Flow Networks","authors":[{"name":"Mark Hoogendoorn","slug":"mark-hoogendoorn","given":"Mark","family":"Hoogendoorn","affiliation":""}],"abstract":"Organizational models within multi-agent systems literature are of a static nature. Depending upon circumstances adaptation of the organizational model can be essential to ensure a continuous successful function of the system. This paper presents an approach based on max flow networks to dynamically adapt organizational models to environmental fluctuation. First, a formal mapping between a well-known organizational modeling framework and max flow networks is presented. Having such a mapping maintains the insightful structure of an organizational model whereas specifying efficient adaptation algorithms based on max flow networks can be done as well. Thereafter two adaptation mechanisms based on max flow networks are introduced each being appropriate for different environmental characteristics.","doi":null,"url":"https://ijcai.org/proceedings/2007/213","pdf":"https://ijcai.org/proceedings/2007/0213.pdf","markdown":"https://ijcai.org/proceedings/2007/213.md","bibtex":"https://ijcai.org/proceedings/2007/bibtex/213","volume":{"slug":"2007","title":"Proceedings of the Twentieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2007","url":"https://ijcai.org/proceedings/2007/","year":2007,"location":"Hyderabad, India","dates":"","editors":"Ramon López de Mántaras","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":213,"pages":{"first":1321,"last":1325},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}