The Effect of Reward Interdependence of Strategies in Evolutionary Multi-Agent Systems
| Autor: | Jonathan Cagan, Lindsay Hanna |
|---|---|
| Rok vydání: | 2009 |
| Předmět: |
Potential impact
education.field_of_study Engineering Knowledge management Process management business.industry Multi-agent system media_common.quotation_subject Population Interdependence Organizational behavior Computational design Convergence (relationship) Set (psychology) business education media_common |
| Zdroj: | Volume 5: 35th Design Automation Conference, Parts A and B. |
| Popis: | This paper explores the effect of reward interdependence of strategies in a cooperative evolving team on the performance of the team. Experiments extending the Evolutionary Multi-Agent Systems (EMAS) framework to three dimensional layout are designed which examine the effect of rewarding helpful, in addition to effective strategies on the convergence of the system. Analysis of communication within the system suggests that some agents (strategies) are more effective at creating helpful solutions than creating good solutions. Despite their potential impact as enablers for other strategies, when their efforts were not rewarded, these assistant agent types were quickly removed from the population. When reward was interdependent, however, this secondary group of helpful agents remained in the population longer. As a result, effective communication channels remained open and the system converged more quickly. The results support conclusions of organizational behavior experimentation and computational modeling. The implications of this study are twofold. First, computational design teams may be made more effective by recognizing and rewarding indirect contributions of some strategies to the success of others. Secondly, EMAS may provide a platform for predicting the effectiveness of different reward structures given a set of strategies in both human and computational teams.Copyright © 2009 by ASME |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|