CMSC 477/677 Agent Architectures and Multi-Agent Systems UMBC Prof. Marie desJardins Spring 2007

Course information Prof desJardins – ITE 337, x53967, Class mailing list – – To subscribe, send to with the line: subscribe agents-class Your Name

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

Class structure: Syllabus Course page: 7/

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

Class structure: Prerequisite CMSC 471 or 671 is a prerequisite for the class I am flexible about this prerequisite, but if you have not had 471/671, and you wish to take the class, you must: – Attend an AI review session this Friday (Feb. 2, in ITE 346) – Be prepared to do additional background reading

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%)

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. 20 (5% of project grade) – Draft report due Mar. 15 (5% of project grade) – Demonstration week of Mar. 27 (25% of grade) – Report due Apr. 3 (65% of grade)

Class structure: MAS paper/presentation MAS paper: 25% of grade – Students will select a topic to study in greater depth, write a paper, and give a presentation on that topic. – Proposal and bibliography due Apr. 5 (10% of project grade) – Draft report due Apr. 26 (10%) – Final report due May 17 (80%) MAS presentation: 5% of grade – Presentation on May 1, 3, 10, 15, or 17 [final exam slot] (20%) Paper review (of another student’s paper, due May 3): 5% of grade

MAS competition Multi-agent tournament: 10% of grade – In-class competition April 26 (Round One: does your agent do anything?) May 8 (Round Two: does your agent do it well?) – Short report describing design and performance of agent (due May 15)

Policies Grading and academic honesty Plagiarism, citations

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. Plagiarism exercise

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.

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.”

What’s an agent? II Ferber, p. 9: – “An agent is a physical or virtual entity a) Which is capable of acting in an environment, b) Which can communicate directly with other agents, c) Which is driven by a set of tendencies…, d) Which possesses resources of its own, e) Which is capable of perceiving its environment…, f) Which has only a partial representation of this environment…, g) Which possesses skills and can offer services, h) Which may be able to reproduce itself, i) 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.”

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

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?

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

Next class Reading: Wooldridge Chapter 1 and 2; Wooldridge & Jennings 1995 Overview by Dr. dJ Tuesday reading: Wooldridge Chapter 4; Bratman et al Discussion leaders!

Multi-agent exercise Getting to know you... getting to know all about you... (or at least your label / color...)

Rules Write your name on your card You can only talk to one other agent at a time The only information you can exchange is your agent ID, your (real) name, and your current “value” At the end of class, turn in your card with: – the names of your “agent neighbors” – your agent “value” – one observation about what was hard (or easy) about each game

Game #1 (lined side of card) Your agent ID is the circled number Each agent must choose a “value” from A to F Your “value” must be after the values of your predecessors, and before the values of your successors, as indicated by the arrows on your card – e.g., A  B and C  E

Does global knowledge help?

Game #2 (unlined side of card) Your agent ID is the circled number The agent “values” are B(lue), R(ed), G(reen), and Y(ellow) You must choose a value that is different from the values of your neighbors

Global knowledge

After-action review or post-mortem, as the case may be…...