Responding to sneaky agents in multi-agent domains

Proceedings of the 22nd International Florida Artificial Intelligence Research Society Conference, FLAIRS-22

Volumn , Issue , 2009, Pages 99-104

  Seymour, Richard S ^a   Peterson, Gilbert L ^a  

^a Air Force Institute of Technology   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION PROCESS; GREEDY ACTION; MULTI-AGENT; MULTI-AGENT ENVIRONMENT; PARTIALLY OBSERVABLE MARKOV DECISION PROCESS; REWARD FUNCTION; TRUST LEVEL; TRUST MODELING;

MARKOV PROCESSES;

MULTI AGENT SYSTEMS;

EID: 70350439100     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. Azzedin, F.; Ridha, A.; and Rizvi, A. 2007. Fuzzy trust for peer-to-peer based systems. Proc. WASET 21:123-127.
2. Bernstein, D. S.; Givan, R.; Immerman, N.; and Zilberstein, S. 2002. The complexity of decentralized control of markov decision processes. Math. Oper Res. 27(4):819-840.
3. Doshi, P., and Gmytrasiewicz, P. J. 2005. A particle filtering based approach to approximating interactive pomdps. In Veloso, M. M., and Kambhampati, S., eds., AAAI, 969-974. AAAI Press / The MIT Press.
4. Doshi, P. 2004. A framework for optimal sequential planning in multiagent settings. In McGuinness, D. L., and Ferguson, G., eds., AAAI, 985-986. AAAI Press / The MIT Press.
5. Hansen, E. A.; Bernstein, D. S.; and Zilberstein, S. 2004. Dynamic programming for partially observable stochastic games. In McGuinness, D. L., and Ferguson, G., eds., AAAI, 709-715. AAAI Press / The MIT Press.
6. Kaelbling, L. P.; Littman, M. L.; and Cassandra, A. R. 1996. Planning and acting in partially observable stochastic domains. Technical Report CS-96-08.
7. Rathnasabapathy, B.; Doshi, P.; and Gmytrasiewicz, P. J. 2006. Exact solutions of interactive pomdps using behavioral equivalence. In Nakashima, H.; Wellman, M. P.; Weiss, G.; and Stone, P., eds.,AAMAS, 1025-1032. ACM.
8. Ray, I., and Chakraborty, S. 2004. A vector model of trust for developing trustworthy systems. In Samarati, P.; Ryan, P. Y. A.; Gollmann, D.; and Molva, R., eds., ESORICS, volume 3193 of Lecture Notes in Computer Science, 260-275. Springer.
9. Rettinger, A.; Nickles, M.; and Tresp, V. 2007. Learning initial trust among interacting agents. In Klusch, M.; Hindriks, K. V.; Papazoglou, M. P.; and Sterling, L., eds., CIA, volume 4676 of Lecture Notes in Computer Science, 313-327. Springer.
10. Roy, N.; Gordon, G.; and Thrun, S. 2004. Finding approximate POMDP solutions through belief compression. Journal of Artificial Intelligence Research. To appear.
11. Seuken, S., and Zilberstein, S. 2008. Formal models and algorithms for decentralized decision making under uncertainty. Journal of AutonomousAgents and Multi-Agent Systems (JAAMAS).
12. Song, W; Phoha, V. V.; and Xu, X. 2004. An adaptive recommendation trust model in multiagent system. In IAT, 462-465. IEEE Computer Society.
13. Szer, D.; Charpillet, F; and Zilberstein, S. 2005. Maa*: A heuristic search algorithm for solving decentralized pomdps. In UAI, 576-590. AUAI Press.
14. Wang, Y., and Singh, M. P. 2006. Trust representation and aggregation in a distributed agent system. In AAAI. AAAI Press.
15. Wong, H. C., and Sycara, K. P. 2000. Adding security and trust to multiagent systems. Applied Artificial Intelligence 14(9):927-941.