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MAS MAS Presentation Transcript

  • Multi-AgentSystems
    A (very)brief introduction
  • Natural vs. Artificial
  • Engineering
    • 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 A
    5 , 5
    0 , 10
    Player B
    10 , 0
    1 , 1
    A related video is available on http://riteshnayak.com/xconf
  • Stag Hunt
    Hunter A
    :D 30, :D 30
    x-( 0 , 10
    Hunter B
    10 , x-( 0
     1 , 1
  • Equilibrium Concepts
    The simplest form of Nash equilibrium is one where each player makes a
    rational choice with no belief (or a least biased belief) about the other
  • 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 Forecasting
    There 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 Challenge
    Requires 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
  • 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.