Trust-Based Decision Making in a Self-Adaptive Agent Organization

Abstract

Interaction between agents is one of the key factors in multiagent societies. Using interaction, agents communicate with each other and cooperatively execute complex tasks that are beyond the capability of a single agent. Cooperatively executing tasks may endanger the success of an agent if it attempts to cooperate with peers that are not proficient or reliable. Therefore, agents need to have an evaluation mechanism to select peers for cooperation. Trust is one of the measures commonly used to evaluate the effectiveness of agents in cooperative societies. Since all interactions are subject to uncertainty, the risk behavior of agents as a contextual factor needs to be taken into account in decision making. In this research, we propose the concept of adaptive risk and agent strategy along with an algorithm that helps agents make decisions in an self-adaptive society utilizing an agent’s own experience and recommendation-based trust. Trust-based decision making increases the profit of the system along with lower task failure in comparison to a no-trust model in which agents do not utilize evaluation mechanisms for choosing their cooperation peers.

References

1. Kamilia Ahmadi. 2014. Decision Making Using Trust and Risk in Self-Adaptive Agent Organization by Kamilia Ahmadi. Master’s Thesis. Utah State University. http://digitalcommons.usu.edu/etd/2159/.
2. Kamilia Ahmadi and Vicki H. Allan. 2013. Efficient self adapting agent organizations. In Proceedings of the 5th International Conference on Agents and Artificial Intelligence. 294--303. DOI:http://dx.doi.org/10.5220/0004261902940303
3. Christopher Burnett. 2011. Trust assessment and decision-making in dynamic multi-agent systems. In Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI’11), Vol. 1. 115--120.
4. Cristiano Castelfranchi and Yao-Hua Tan. 2001. The role of trust and deception in virtual societies. In Proceedings of the 34th Annual Hawaii International Conference on System Sciences. IEEE, Los Alamitos, CA. DOI:http://dx.doi.org/10.1109/hicss.2001.927042
5. Rino Falconem, Alessandro Sapienza, and Cristiano Castelfranchi. 2015. Trusting information sources through their categories. In Advances in Practical Applications of Agents, Multi-Agent Systems, and Sustainability: The PAAMS Collection. Lecture Notes in Computer Science, Vol. 9086. Springer, 80--92. DOI:http://dx.doi.org/10.1007/978-3-319-18944-4_7
6. Carlos Gershenson. 2003. Artificial societies of intelligent agents. Social Science Research Network Working Paper Series. Available at http://ssrn.com/abstract=371641.
7. Ahmadreza Ghaffarizadeh and Vicki H. Allan. 2013. History based coalition formation in hedonic context using trust. International Journal of Artificial Intelligence and Applications 4, 4, 1--8. DOI:http://dx.doi.org/10.5121/ijaia.2013.4401
8. Tyrone Grandison and Morris Sloman. 2000. A survey of trust in Internet applications. IEEE Communication Surveys and Tutorials 3, 4, 2--16. DOI:http://dx.doi.org/10.1109/comst.2000.5340804
9. Nathan Griffiths. 2006. A fuzzy approach to reasoning with trust, distrust and insufficient trust. In Cooperative Information Agents. Lecture Notes in Computer Science, Vol. 4149. Springer, 360--374. DOI:http://dx.doi.org/10.1007/11839354_26
10. Audun Jøsang, Guibing Guo, MariaSilvia Pini, Francesco Santini, and Yue Xu. 2013. Combining recommender and reputation systems to produce better online advice. In Modeling Decisions for Artificial Intelligence. Lecture Notes in Computer Science, Vol. 8234. Springer, 126--138. DOI:http://dx.doi.org/10.1007/978-3-642-41550-0_12
11. Andrew Koster, Jordi S. Mir, and Marco Schorlemmer. 2012a. Personalizing communication about trust. In Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems—Volume 1 (AAMAS’12). 517--524. http://portal.acm.org/citation.cfm?id=2343650
12. Andrew Koster, Marco Schorlemmer, and Jordi Sabater-Mir. 2012b. Opening the black box of trust: Reasoning about trust models in a BDI agent. Journal of Logic and Computation 23, 1, 25--58. DOI:http://dx.doi.org/10.1093/logcom/exs003
13. Ramachandra Kota, Nicholas Gibbins, and Nicholas R. Jennings. 2012. Decentralized approaches for self-adaptation in agent organizations. ACM Transactions on Autonomous and Adaptive Systems 7, 1, Article No. 1. DOI:http://dx.doi.org/10.1145/2168260.2168261
14. Z. Livne. 1987. Bargaining over the division of a shrinking pie: An axiomatic approach. International Journal of Game Theory 16, 3, 223--242. DOI:http://dx.doi.org/10.1007/bf01756293
15. Lars Rasmusson and Sverker Jansson. 1996. Simulated social control for secure Internet commerce. In Proceedings of the 1996 Workshop on New Security Paradigms (NSPW’96). ACM, New York, NY, 18--25. DOI:http://dx.doi.org/10.1145/304851.304857
16. Munindar P. Singh. 2011. Trust as dependence: A logical approach. In Proceedings of the 10th International Conference on Autonomous Agents and Multiagent Systems—Volume 2 (AAMAS’11). 863--870. http://portal.acm.org/citation.cfm?id=2031741
17. Meytal Traub, Gal A. Kaminka, and Noa Agmon. 2011. Who goes there? Selecting a robot to reach a goal using social regret. In Proceedings of the 10th International Conference on Autonomous Agents and Multiagent Systems—Volume 1 (AAMAS’11). 91--98. http://portal.acm.org/citation.cfm?id=2030484
18. Matteo Venanzi, Alex Rogers, and Nicholas R. Jennings. 2013. Trust-based fusion of untrustworthy information in crowdsourcing applications. In Proceedings of the 2013 International Conference on Autonomous Agents and Multi-agent Systems (AAMAS’13). 829--836. http://portal.acm.org/citation.cfm?id=2485052
19. Michael Wooldridge. 2009. An Introduction to Multiagent Systems (2nd ed.). Wiley. http://portal.acm.org/citation.cfm?id=1695886
20. Han Yu, Zhiqi Shen, Chen Leung, Chunyan Miao, and Victor R. Lesser. 2013. A survey of multi-agent trust management systems. IEEE Access 1, 35--50. DOI:http://dx.doi.org/10.1109/access.2013.2259892

Index Terms

• Computing methodologies

  • Artificial intelligence

    • Distributed artificial intelligence

      • Multi-agent systems