Topic 2: Multi-Agent Systems

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Topic 2 Multi-Agent Systems

• a practical example
• categories of MAS
• examples
• definitions agents and MAS
• conclusion

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Think about this

• a practical exercise
• re-arranging entities in a constrained world
• problem domain
• Constrained world
• Entities
• Position
• Mobile
• Formation - new position

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Think about this

• a practical exercise
• re-arranging entities in a constrained world
• problem domain
• Constrained world
• Entities
• Position
• Mobile
• Formation - new position
• solution ?

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Think about this (cont.)

• 1. a centralized solution
• how ?
• one overall planner
• everyone gives his information (initial and
  target position)to the planner
• the planner calculates a plan
• the planner
• instructs individual entities (you move
  to position X, ) or
• distributes individual plans

collect
plan
re-distribute

• advantages ?
• simple
• easy to maintain
• planning quite well-known

• disadvantages ?
• bottleneck
• single-point-of-failure
• tractable?
• who is the planner?
• what global knowledge does planner need?
• entities, start of protocol,

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Think about this (cont.)

• 2. a distributed solution
• how ?
• different tasks in re-arranging
• e.g. per group of entities
• one planner per task/group
• one overall task manager (coordinates tasks)
• collection redistribution (per task)

• advantages ?
• more scalable (a bit)
• easy distribution

• disadvantages ?
• bottleneck / scalability ?
• many-points-of-failure
• how to designate the planners?
• what global knowledge does this require the
  sorters to have ?
• entities, groups, start of protocol, ?

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Think about this (cont.)

• 3. a distributed, functional decomposition
  solution
• how ?
• assign experts
• collector
• planner
• distributor

the experts
collector
planner
distributor

• advantages ?
• more scalable (a bit)
• easy distribution
• clear roles / responsibilities

• disadvantages ?
• bottleneck / scalability?
• many-points-of-failure
• how to designate the experts?
• what global knowledge do experts need?
• entities, start of protocol, ?

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Think about this (cont.)

• ok, but what if
• entities is not known ?
• entities may enter or leave the system at all
  times?
• target positions can be changed during execution?
• frequency of change gt time to (re)plan?
• we do this for 10.000s of entities?
• what if a movement did not happen the way it was
  supposed to?
• in general, what if it is not a single shot
  application, but a going concern ?
• its a tough world

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Think about this (cont.)

• 4. an enhanced distributed, functional
  decomposition solution
• how ?
• make the experts more intelligent
• monitor the system for change
• use knowledge to tackle changeefficiently
  (collecting/planning/distributing)

the experts
collector
planner
distributor

• extra advantages ?
• more flexible to change
• awareness of failures

• disadvantages ?
• scalability
• more complex experts
• global knowledge

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Think about this (cont.)

• 5. a decentralized solution
• how ?
• every entity tries to force its wayto target
  position
• local conflict resolution to avoiddeadlocks/starv
  ation

• advantages ?
• simple (if robots low cost)
• easy distribution
• scalable
• no single-point of failure
• only local actions
• only local knowledge (neighbours, order)

• disadvantages ?
• no central view gt not for one-shot
  applications
• no hard guarantees?

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Think about this (cont.)

• ok, but what if
• entities is not known ? OK
• entities may enter or leave the system at all
  times? OK
• target positions can be changed during
  execution? OK
• frequency of change gt time to (re)plan? OK
• we do this for 10.000s of entities? OK
• in general, what if it is not a single shot
  application,but a going concern ? OK
• its a tough world

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Whats the point ?

• Problems may have many solutions
• solutions have qualities
• pick the appropriate solution for the set of
  required qualities
• This course is about
• distributed problems in a dynamic environment
• with requirements for
• flexibility
• adaptability
• scalability

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What is a MAS ?

• in essence, a MAS is a philosophy to model
  systems
• indicating how to solve a problem
• in a complex world
• with autonomous entities
• solutions of example
• centralized
• distributed
• distributed experts
• enhanced distributed experts
• decentralized

multi-agent systems
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What is a MAS ? Categories of MAS

• 1. distributed experts
• enhanced distributed entities
• cooperating entities
• mainly functional decomposition
• focus on detailed modeling of individual agent
• mental states
• knowledge representation
• planning
• 2. decentralized systems
• collective behaviour
• local actions/interactions only
• focus on the collection and the environment

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Application domains

• distributed control applications
• e.g. AGVs, robots, virtual entities, trafic,
  logistics
• simulation
• pure algorithms

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Example a distributed control system - 1

• Control system for automated warehouse management
• Egemin N.V.
• AGVs (automated guided vehicles)
• control system
• the world
• large
• dynamic
• tasks
• AGV failure
• batteries

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• e.g. Vredestein tyres (NL)
• centralized solution ?
• towards a distributed solution in a tough world
• distributed ? enhanced distributed system (cat.
  1)
• distributed ? decentralized system (cat. 2)

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Example a distributed control system - 2

• Reactive Bubbles
• moving entities
• constraint environment
• what if
• massively distributed !?
• no global knowledge !?
• new bubbles / bubbles disappear !?
• environment changes !?
• is it efficient !?
• efficient ? NO !
• but fascinating!
• and a solution of our problem in a tough world

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Example Collective Robotics

• Collective robotics
• cooperative parts of one robot
• e.g. several servos for a single robot arm
• multiple cooperating robots
• coordinate actions
• to accomplish common task

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Example MAS simulation

• Goal study phenomena in
• physics
• ecology
• biology
• chemistry
• social sciences
• geography
• Approaches
• mathematical relationships of variables
• (differential) equations, transition matrices,
• MAS
• model individual entities / environment /

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MAS simulation (cont.)

• MAS simulations
• model individuals
• behaviour
• actions
• interactions
• model environment
• advantages miniature laboratory
• simulation model close to real-world entities
• allows to study consequence of individual
  behaviour
• reasoning process can be included

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Example algorithms

• edge detection / constraint satisfaction problems
  / data mining

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Why use MAS for such systems ?

• clean software abstraction for modelling such
  systems
• autonomy
• cooperation
• agents are (should be) critical by nature
• do not rely on anything
• should be designed to be flexible
• agents are (should be) adaptive by nature
• change their behaviour in this highly dynamic
  world
• MAS solves problems!
• but also introduces new challenges
• communication
• coherent behaviour
• control
• MAS is NOT a holy grail !!
• MAS is NOT suitable for all applications !!

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MAS definition

• A multi-agent system is a system consisting of
  multiple autonomous entities, called agents,
  which are situated in an environment that the
  agents can partially observe and in which they
  can act and cooperate to achieve system
  objectives.

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MAS definition

• requires a definition of
• agents
• actions
• production/consumption/manipulation of objects
• perception
• an environment
• objects in the environment
• objectives

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MAS environment

• properties
• accessible vs. inaccessible
• accessible can provide complete, up-to-date
  information about the (entire) environment
  state
• the more accessible, the easier the system
• most real-world systems inaccessible state
• deterministic vs. non-deterministic
• determinism w.r.t. result of agent actions
• e.g. up (B,C) ? ??
• most real-world systems non-deterministic
  environment
• agents do not have full control
• actions can fail

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MAS environment (cont.)

• static vs. dynamic
• static does not change between two actions
  (e.g. planning algorithms)
• most real-world systems dynamic environment
• under constant change objects in the environment
  change environment changes concurrent /
  autonomous agents ? concurrent actions
• e.g. software environment real-world
  environment
• discrete vs. continuous
• as in number of states

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Agents

• An agent is physical or virtual entity
• capable of perceiving its environment
• capable of acting in an environment (not just
  reasoning)
• capable of communicating
• driven by goals
• possessing resources
• having partial representation of the environment
• having particular capabilities (skills and
  services)
• acts towards its objectives

J. Ferber
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• an agent
• properties
• reactivity
• reacts to stimuli (changes in env.,
  communication, )
• autonomy
• does not require user interaction
• proactive-ness
• aims to achieve its own goals, therefore
  initiates appropriate actions
• social ability
• cooperates / coordinates / communicates /
• embodied
• situated in the environment
• mobile
• moves around network sites
• learning
• learn from past experiences

essential
extra
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MAS agent actions

• what is an agent action ?
• an attempt
• to bring about a state of affairs
• in the environment
• or another agent
• non-deterministic
• did anything happen ?
• did happen what agent expected ?
• did something else happen ?
• did the message arrive ?
• this is not a limitation to make things hard
  its a fact of life
• gt agents need to be flexible / adaptive /

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MAS objects in environment

• objects in the environment
• passive objects
• e.g. pallets, files,
• dynamic objects
• e.g. rolling ball, stones cooling down
• agents

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MAS objectives / behaviour

• objectives
• system objective
• e.g. find a solution to a constraint problem
  
• e.g. ongoing concern
• e.g. continuous schedule of product
  manufacturing
• e.g. ant colony survival
• e.g. network management
• individual agent objective
• directly related to system objective
• e.g. efficient expert service
• emergent behaviour
• system objective emerges from achievements of
  individual objectives
• e.g. ant foraging

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MAS definition

• A multi-agent system is a system consisting of
  multiple autonomous entities, called agents,
  which are situated in an environment that the
  agents can partially observe and in which they
  can act and cooperate to achieve system
  objectives.

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Key challenge of MAS

• imagine that you have to develop software
• which will work in an environment
• that you can only partially observe
• where the results of what you do are not
  guaranteed
• where the environment constantly changes by other
  activities
• which can achieve to perform its objective

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ConclusionMAS Issues and Challenges

• How do we decompose problems into behaviour for
  individual agents ?
• How do we ensure agents act coherently in making
  decision or taking action ?
• do local actions have harmful global effects
• avoiding unstable system behaviour
• How to enable agents to communicate and interact
  ?
• communication languages and protocols
• interoperation of heterogeneous agents
• finding useful existing agents in open
  environments
• How does agent decide what to do ?
• action selection mechanisms
• How do we build agents ?
• actions, plans, and knowledge
• coordination actions

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Conclusion

• MAS is about
• software engineering distributed applications
• engineering
• synergy of different techniques/philosophies/app
  lications/
• distributed systems
• software engineering
• robot control
• languages for programming MAS
• AI
• AI is a subfield of MAS
• Agents are 99 computer science, and 1 AI.