CMSC 477/677 Agent Architectures and Multi-Agent Systems

1. CMSC 477/677Agent Architectures andMulti-Agent Systems UMBCProf. Marie desJardins Spring 2005

2. Course information • Prof desJardins • ITE 337, x53967, mariedj@cs.umbc.edu • Class mailing list • agents-class@listproc.umbc.edu • To subscribe, send email to listproc@listproc.umbc.edu with the line: • subscribe agents-class Your Name

3. Today’s overview • Class structure and policies • What’s an agent? • Agent exercise • Next class

4. Class structure: Syllabus • Course page: http://www.cs.umbc.edu/courses/graduate/677/spring05/http://www.cs.umbc.edu/courses/undergraduate/477/spring05/

5. Class structure: 477 vs. 677 • Slightly different weights for assignments • Two problem sets for graduate students • Agent architectures project: Graduate students must do a more in-depth analysis, relating their findings to the research literature • MAS project: Graduate students must include an experimental research component, and submit a research design • In general, graduate students are expected to show greater depth in their analysis and synthesis of ideas

6. Class structure: Participation • This is a discussion class • Reading must be done in advance • Participation counts—a lot • 40/35% of grade is related to class participation • Class discussion (30/25%) • Do you attend class? • Are you prepared? Have you done the reading? Have you thought about the discussion questions? • Do you contribute to the discussion with insightful questions and comments? • Paper summaries (5%) • Discussion leaders (5%)

7. Class structure: Agent architecture project • Agent architecture project: 20/15% of grade • Download one of the architectures we learn about • Apply the architecture to a domain of your choice • Deadlines: • Proposal due Feb. 17 (5% of project grade) • Report due Mar. 17 (70% of grade) • Demonstration week of Mar. 14 (25% of grade)

8. Class structure: MAS paper/presentation • MAS paper/presentation: 25% of grade • Students will select a topic to study in greater depth, write a paper, and give a presentation on that topic. • 477: can focus primarily on one or two recent research papers • 677: can focus on one or two main papers, but should also include a bibliography of 5-10 (more is OK) papers on the topic, and a significant discussion/analysis of the work in that area. • Proposal and bibliography due Apr. 12 (10% of project grade) • Draft report due May 5 (5%) • Presentation on May 3, 5, 10, 12(?), or 19 (20%) • additional days if needed: May 13 and/or May 6 • Final report due May 19 (65%) • Paper review (of another student’s paper): 5% of grade

9. MAS competition • Multi-agent game (trading agents?) project: 10% of grade • In-class competition – probably May 12 • Short report describing design and performance of agent

10. Policies • Grading and academic honesty • Plagiarism, citations

11. Plagiarism exercise • Original passage: • I pledge allegiance to the flag of the United States of America, and to the republic for which it stands, one nation, indivisible, with liberty and justice for all. • Unacceptable summary: • I promise loyalty to the United States flag, and to the country for which it stands, one nation,with freedom and fairness for all.

12. Plagiarism exercise II • Original passage: • I pledge allegiance to the flag of the United States of America, and to the republic for which it stands, one nation, indivisible, with liberty and justice for all. • Acceptable summary: • I promise to be loyal to the United States flag and to the USA itself: One united country that provides basic rights such as liberty and justice to all citizens.

13. What’s an agent? • Weiss, p. 29 [after Wooldridge and Jennings]: • “An agent is a computer system that is situated in some environment, and that is capable of autonomous action in this environment in order to meet its design objectives.” • Russell and Norvig, p. 7: • “An agent is just something that perceives and acts.” • Rosenschein and Zlotkin, p. 4: • “The more complex the considerations that [a] machine takes into account, the more justified we are in considering our computer an ‘agent,’ who acts as our surrogate in an automated encounter.”

14. What’s an agent? II • Ferber, p. 9: • “An agent is a physical or virtual entity • Which is capable of acting in an environment, • Which can communicate directly with other agents, • Which is driven by a set of tendencies…, • Which possesses resources of its own, • Which is capable of perceiving its environment…, • Which has only a partial representation of this environment…, • Which possesses skills and can offer services, • Which may be able to reproduce itself, • Whose behavior tends towards satisfying its objectives, taking account of the resources and skills available to it and depending on its perception, its representations and the communications it receives.”

15. OK, so what’s an environment? • Isn’t any system that has inputs and outputs situated in an environment of sorts?

16. What’s autonomy, anyway? • Jennings and Wooldridge, p. 4: • “[In contrast with objects, we] think of agents as encapsulating behavior, in addition to state. An object does not encapsulate behavior: it has no control over the execution of methods – if an object x invokes a method m on an object y, then y has no control over whether m is executed or not – it just is. In this sense, object y is not autonomous, as it has no control over its own actions…. Because of this distinction, we do not think of agents as invoking methods (actions) on agents – rather, we tend to think of them requesting actions to be performed. The decision about whether to act upon the request lies with the recipient.” • Is an if-then-else statement sufficient to create autonomy?

17. So now what? • If those definitions aren’t useful, is there a useful definition? Should we bother trying to create “agents” at all?

18. Next class • Reading: Wooldridge Chapter 1; Wooldridge & Jennings 1995 • Overview by Dr. dJ • Tuesday reading: Wooldridge Chapter 3; Levesque et al. 1997 • Discussion leaders!

19. Multi-agent exercise • Getting to know you... getting to know all about you... (or at least your capabilities...)

20. After-action reviewor post-mortem, as the case may be… • What was the task completion rate? • How many agents participated in successful teams? • Who was more successful – agents who led teams, or agents who participated on teams? • Any particularly successful (or unsuccessful) strategies for forming teams? • What’s hard about this problem?