Dynamic programming: deterministic and stochastic models
Dynamic programming: deterministic and stochastic models
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Learning to Be Thoughtless: Social Norms and Individual Computation
Computational Economics
A Bayesian Framework for Reinforcement Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
ATAL '98 Proceedings of the 5th International Workshop on Intelligent Agents V, Agent Theories, Architectures, and Languages
Deliberative Normative Agents: Principles and Architecture
ATAL '99 6th International Workshop on Intelligent Agents VI, Agent Theories, Architectures, and Languages (ATAL),
Emergence of Norms with Biased Interactions in Heterogeneous Agent Societies
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
Coalitional bargaining with agent type uncertainty
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Emergence of norms through social learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Model based Bayesian exploration
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
| Hi-index | 0.00 |
This paper addresses the problem of norm adaptation using Bayesian reinforcement learning. We are concerned with the effectiveness of adding prior domain knowledge when facing environments with different settings as well as with the speed of adapting to a new environment. Individuals develop their normative framework via interaction with their surrounding environment (including other individuals). An agent acquires the domain-dependent knowledge in a certain environment and later reuses them in different settings. This work is novel in that it represents normative behaviors as probabilities over belief sets. We propose a two-level learning framework to learn the values of normative actions and set them as prior knowledge, when agents are confident about them, to feed them back to their belief sets. Developing a prior belief set about a certain domain can improve an agent's learning process to adjust its norms to the new environment's dynamics. Our evaluation shows that a normative agent, having been trained in an initial environment, is able to adjust its beliefs about the dynamics and behavioral norms in a new environment. Therefore, it converges to the optimal policy more quickly, especially in the early stages of learning.