Modelling social action for AI agents
Artificial Intelligence - Special issue: artificial intelligence 40 years later
Evolutionary On-line Learning of Cooperative Behavior with Situation-Action-Pairs
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Coalition Formation for Large-Scale Electronic Markets
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Believing Others: Pros and Cons
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Methods for task allocation via agent coalition formation
Artificial Intelligence
On the synthesis of useful social laws for artificial agent societies
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Local strategy learning in networked multi-agent team formation
Autonomous Agents and Multi-Agent Systems
Agent-organized networks for multi-agent production and exchange
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Cooperative information sharing to improve distributed learning in multi-agent systems
Journal of Artificial Intelligence Research
| Hi-index | 0.00 |
Autonomous agents interacting in an open world can be considered to be primarily driven by self interests. In this paper, we evaluate the hypotheses that self-interested agents with complementary expertise can learn to recognize cooperation possibilities and develop stable, mutually beneficial partnerships that is resistant to exploitation by malevolent agents. Previous work in this area has prescribed a strategy of reciprocal behavior for promoting and sustaining cooperation among such cognitive learning agents. We develop on that work by expanding the task cost metric to include both time of completion and quality of performance. Different task types are assumed and, in contrast to previous work, we use heterogeneous agents with varying expertise for different task types. This necessitates the incorporation of cognitive abilities, including an understanding of one's own capabilities and learning about other's capabilities within the reciprocity framework.