1. Course Overview Agent Technology

2. Course information Instructors – Software Agent: Dr. Ping YU (yuping@nju.edu.cn)yuping@nju.edu.cn – Intelligent Agent: Dr. Hao WANG (wanghao@nju.edu.cn)wanghao@nju.edu.cn Time – 10:10 - 12:00 every Monday morning Venue – C-502 Shao Yifu Building Homepage – http://cs.nju.edu.cn/rl/courses/agent2015/ http://cs.nju.edu.cn/rl/courses/agent2015/ 2

3. Outcomes Upon completion of this course, a student is expected to – Know what an agent and an agent system is – Have a good overview of important agent-related issues: Agent negotiation, coordination, and communication Micro and macro agent architectures Agent learning Agent model and theory Agent-oriented software engineering – Get valuable hands-on experience in developing agent system 3

4. Reference books Michael Wooldridge An Introduction to Multiagent Systems (2 nd edition) John Wiley & Sons, 2009 [link]link 4 Yoav Shoham & Kevin Leyton-Brown Multiagent Systems: Algorithmic, Game-theoretic, and Logical Foundations [link]link Cambridge University Press, 2009 史忠植 智能主体及其应用, 科学出版社, 2000

5. Schedule – Part A 5 WeekTopic(s)Instructor 1Overview / introductionHao Wang 2Agent mobility (1)Ping Yu 3Agent mobility (2)Ping Yu 4Mobile agent communication (1)Ping Yu 5Mobile agent communication (2)Ping Yu 6Mobile agent securityPing Yu 7Java agent development (1)Ping Yu 8Java agent development (2)Ping Yu

6. Schedule – Part B 6 WeekTopic(s)Instructor 9Social mechanisms in MAS (1)Hao Wang 10Social mechanisms in MAS (2)Hao Wang 11Social mechanisms in MAS (3)Hao Wang 12Agent learning (1)Hao Wang 13Agent learning (2)Hao Wang 14Agent communicationHao Wang 15Agent architecturesHao Wang 16Agent models and theoriesHao Wang

7. Contents Week 1: Overview / introduction Weeks 2-3: Agent mobility Weeks 4-5: Mobile agent communication Week 6: Mobile agent security Weeks 7-8: Java agent development 7

8. Contents Weeks 9-11: Social mechanisms in MAS – 1 Multi-agent interactions: gaming – 2 Making group decisions: voting – 3 Forming coalitions – 4 Allocating scarce resources: bidding Weeks 12-13: Agent learning – 1 Reinforcement learning (RL) – 2 Multi-agent learning / multi-agent RL with recent advances 8

9. Contents Week 14: Agent communication – 1 Speech-act theory – 2 Agent communication language: FIPA ACL; KQML – 3 Recent advances; current research topics Week 15: Agent architecture – 1 Classification: Russell & Norvig; Wooldridge; Kendall – 2 Advanced agent architectures: SOAR, ACT-R, etc. Week 16: Agent models and theories – 1 Logical background – 2 Cognitive primitives 9

10. Assessment Intelligent agent experiments (50%) – TBD Software agent experiments (50%) – TBD 10