mas

Multi-Agent Systems

A (very)brief introduction

@itsmeritesh

Natural vs. Artificial

Engineering

Natural Artificial

-Made of autonomous agents capable of playing several roles.- Autonomous actions by agents based on self interest. - Structure is a result of evolution and local adjustments.

- Made of parts custom built for a specific purpose - Well defined functionality for each part.- Structure is designed into its present shape.

Living beings behave more like societies than machines according to the definitions.

Courtesy: Course Slide

What’s the Engineering Solution?

A hybrid model which mimics natural engineering.

A related video is available on http://riteshnayak.com/xconf

The goal of multiagent systems’ research is to find methods that allow us to build complex systems composed of autonomous agents who, while operating on local knowledge and possessing only limited abilities, are nonetheless capable of enacting the desired global behaviors.

What’s different

• Completely autonomous agents that act out of self interest.

• No constraints on inputs (Open World Programming)

• Limited knowledge (don’t think bluegene) • Interaction between autonomous agents

determined based on self interest function.

Lets See an example!!

Why is this important

• To simulate events with real life actors. • To learn about evolutionary characteristics of

a model. • Model societies, colonies, groups etc and

learn about herd behavior (ex: How do perfectly rational humans create traffic deadlocks?)

Humans are individually smart and collectively stupid.

Desirable Characteristics of Agents

• High predictability in behavior• Operability in uncertain conditions. • Resilient to failures.• Optimal performance for given situations• Mathematical tractability to the finest detail.

Emergence• Designing Objective functions

and payoffs in a way so that local decisions of agents collectively result in optimizing a global objective function.

• You cannot code for emergence.

• Emergent behaviors are not always desirable.

Remember Game of Life ??

Evolution

Prebiotic Soup Unicellular Organisms

Multicellular Organisms

A related video is available on http://riteshnayak.com/xconf

Technologies

• NetLogo: http://ccl.northwestern.edu/netlogo/ • VisualBots. http://www.visualbots.com/index.htm• MASON.

http://www.cs.gmu.edu/~eclab/projects/mason/• Repast. http://repast.sourceforge.net/• Java Agent Development Framework

http://jade.tilab.com/

NetLogo

Features

• NetLogo is a cross-platform multi-agent programmable modeling environment.

• Built and maintained out of Northwestern University.

• Uses a variant of LOGO Lets see a demonstration

Mars Rover #WIN

Termite mound

• A termite mound is work of art• The temperature inside the mound has to

remain a constant 31 deg. • Termites are autonomous beings.

They are hard wired to do only one thing!!

A related video is available on http://riteshnayak.com/xconf

Modelling

• Autonomous actions by agents based on self-interest functions like beliefs, desires, intentions, etc (Rational Choice – or utility maximization – John Stuart Mills)

• Interaction between Agents can be modeled as a game, auction (common resources), vote (master-slave setup) etc.

Prisoner’s Dilemma

Player APl

ayer

B C

D

C D

5 , 5 0 , 10

10 , 0 1 , 1

A related video is available on http://riteshnayak.com/xconf

Stag Hunt

Hunter A

Hun

ter B

Stag

Hare

Stag Hare

:D 30, :D 30 x-( 0 , 10

10 , x-( 0 1 , 1

Equilibrium ConceptsThe simplest form of Nash equilibrium is one where each player makes arational choice with no belief (or a least biased belief) about the other

players.

MAS Borrows from

• Rational Choice theory• Game theory• Stochastic Networks• Auction theory, negotiations and mechanism

design.• Chaos theory, complex systems and theory of

emergence.

Mechanism Design

• Refers to the design principles behind an auction or a voting process that can be used to favor specific outcome

• In an auction, the seller’s choice is to sell at the highest possible price. How do you get the agents to quote higher prices.

Interesting Applications

Weather ForecastingThere is a lot of work being done to model climate and implications of climate change etc. A domain that has seen a lot of action in last decade.

Self Optimizing Networks

Optimal Wifi using robot routers

Realism in Games

A related video is available on http://riteshnayak.com/xconf

The Grand DARPA ChallengeRequires teams to build an autonomous vehicle capable of

driving in traffic, performing complex maneuvers such as merging, passing, parking and negotiating intersections.

Prize money is $2 million, $1million and $500k respectively

A related video is available on http://riteshnayak.com/xconf

Disaster Recovery

• Work done by my classmates at CSTEP.in• Using technology to shape public policy • Use SimCity as a base framework for

modelling agents. A small video of the simulation

My Project • Multi-Agent based simulation of a

Normative/Incentive system for Content Aggregation on Online Forums

• Main objectives– To build a system of norms and incentives for

knowledge aggregation on an online forum– Mechanism design to increase activity on the

forum and also keep the network from saturating

Some results from this project

Loyal User

• Posts Regularly

• Forgiving• Satisfied

with answers

• Closes q’s early

• 1 category

Regular User

• Less frequently than loyal

• Less forgiving

• Waits for more answers

• At least 2 categories

Bounce User

• Participates rarely

• Reply sometimes

• Mostly simulate redirects from search engines

• Don’t close questions.

Last but one slide

• MAS research is a relatively new field for computer scientists.

• Lot of applications in many different fields. Will gain a lot of prominence very soon.

• Skeptics doubt results due to inconsistency.• Hope you figured the playing God part.

References• Fundamentals of Multiagent Systems - Jos´e M Vidal -

http://jmvidal.cse.sc.edu/papers/mas-20070824.pdf

• Course on MAS at my institute IIIT – Bangalore (course page http://osl.iiitb.ac.in/wiki/index.php/Multi-Agent_Systems)

• Prof Srinath Srinivasa for all anecdotes/ examples etc.• Evolution of Co-operation – Robert Axelrod• C.H. Papadimitriou. Algorithms, Games, and the Internet.

Proc. STOC-2001, ACM Press, 2001. Invited talk write-up.(URL:http://www.eecs.harvard.edu/~parkes/cs286r/spring02/papers/stoc01.pdf)

• Thanks to DARPA, Google Image search, wisegeek.com and Wikipedia for the images.