Course Overview

Agent Technology

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Course information

Instructors– Software Agent: Dr. Ping YU (yuping@nju.edu.cn)– Intelligent Agent: Dr. Hao WANG (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/

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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

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Reference books

Michael Wooldridge

An Introduction to Multiagent Systems (2nd edition)

John Wiley & Sons, 2009 [link]

Yoav Shoham & Kevin Leyton-Brown

Multiagent Systems: Algorithmic, Game-theoretic, and Logical Foundations [link]

Cambridge University Press, 2009

史忠植智能主体及其应用 , 科学出版社 , 2000

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Schedule – Part A

Week Topic(s) Instructor

1 Overview / introduction Hao Wang

2 Agent mobility (1) Ping Yu

3 Agent mobility (2) Ping Yu

4 Mobile agent communication (1) Ping Yu

5 Mobile agent communication (2) Ping Yu

6 Mobile agent security Ping Yu

7 Java agent development (1) Ping Yu

8 Java agent development (2) Ping Yu

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Schedule – Part B

Week Topic(s) Instructor

9 Social mechanisms in MAS (1) Hao Wang

10 Social mechanisms in MAS (2) Hao Wang

11 Social mechanisms in MAS (3) Hao Wang

12 Agent learning (1) Hao Wang

13 Agent learning (2) Hao Wang

14 Agent communication Hao Wang

15 Agent architectures Hao Wang

16 Agent models and theories Hao Wang

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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

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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

1 lecture

forming coalitions

recent advances

newly emerging applications bargain

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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

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Assessment

Intelligent agent experiments (50%)

– TBD

Software agent experiments (50%)

– TBD