Topic 8: Coordination andControl Applications
coordination and control applications a reference architecture ant-based coordination and control
Overview
Coordination and Control applications characteristics (2-levels, task-oriented, large scale, dynamic, requirements) software architecture decentralization
Manufacturing Control requirements
functional non-functional flexibility under disturbances and change
software architecture basic software architecture
task agents mobile units / tasks
resource agents resources environment graph
constraints: product recipes coordination
coordination through delegate MASs exploit the environment feasibility delegate MASs + appl. characteristics exploration delegate MASs intention delegate MASs
Multi-Agent Systems forCoordination and Control Applications
family of applications, characterized by control application
underlying (physical or software) system that needs to be controlled resources – static entities mobile entities
on top: software system to “control” the underlying system different order of magnitude of evolution speed
task-oriented application domain a task entails moving through the environment (mobile entities) and
performing operations using resources (static entities) large / huge scale
number of entities physical distribution
very dynamic nature resources / connections / tasks
complex functional / non-functional requirements coordination ! cooperation ! plan ahead !!
Multi-Agent Systems forCoordination and Control Applications
examples manufacturing control AGV-based warehouse management traffic control web service coordination
control application underlying (physical or software) system that needs to be controlled
resources – static entities mobile entities
on top: software system to “control” the underlying system different order of magnitude of evolution speed
task-oriented application domain a task entails moving through the environment (mobile entities) and performing operations using resources (static
entities) large / huge scale
number of entities physical distribution
very dynamic nature resources / connections / tasks
complex functional / non-functional requirements coordination ! cooperation ! plan ahead !!
Manufacturing control
generic / simplified perspective the underlying system:
manufactory for building products
resources – static entities machines (painting, milling, drilling, …) conveyor belts ..
environment: graph–like structure dynamic directed graph
nodes: resources edges: connections
embeds mobile entities
mobile entities – tasks / orders intermediate products travel over resources
Manufacturing Control Requirements
Functional Production of ordered products Coordination of resources and orders
Adaptive: Changes and disturbances (all-the-time)
Quality of service / throughput …
• Disturbances– machine break-down– missing tools– missing materials– etc.
• Changes– new technology– new markets– new organisational structures– etc.
OUT
IN
= resource / machine = connection
OUT
IN
= resource / machine = connection
Type-1
Type-1
Type-1
Type-1
Type-2
Type-2
Type-2
Type-2
Type-3
Type-3
Type-3
Type-3
Type-4
Type-4
Type-5
OUT
IN
= resource / machine = connection
OUT
= resource / machine = connection
order-1
OUT
= resource / machine = connection
order-1
order-2
OUT
= resource / machine = connection
order-1
order-2
order-3order-4
order-5order-6
OUT
= resource / machine = connection
Multi-Agent Systems forCoordination and Control Applications
Centralized approaches consider the problem to be an optimisation problem operations research / static and dynamic feasibility ...?
…
Multi-Agent Systems forCoordination and Control Applications
…
Distributed approaches local decision makers, which cooperate / coordinate...
vehicles carrying orders manufactory network - resources
decentralized architectures hold several advantages inherently adaptive Scalable? / local observation and control …
crucial problem remains: deal with scale and complexity
Solution? Distributed approaches
…
compromises ... hierarchical models
- e.g. based on geographical characteristics... compromises on flexibility, performance, complexity e.g. 2-level distribution... [Klaus Fischer ’95]
pure decentralization simple local rules + rely on emergence compromise on optimality [Tamas Mahr ’08]
What is at the heart of the problem...
local decision makers require global information for adequate decision making
What could be at the heart of the solution...
local decision makers find/isolate only that global information
that is directly relevant for adequate decision making
Multi-Agent Systems forCoordination and Control Applications
…
Reference model for Coordination and Control applications
Decentralized components / agents Environment-centric coordination model
Ant-inspired…
Basic Components of the Reference Architecture
based on PROSA reference architecture
Agent types Resource agent Task agent
Environment
Basic Components of the Reference Architecture (cont.)
Environment software environment reflects physical environment
dynamic graph nodes: resources edges: connections
support interaction / interface
with resources embeds virtual mobile entities
Basic Components of the Reference Architecture (cont.)
Resource agent Reflects a resource in the manufacturing system Autonomous for optimizing its own behaviour
schedule lifetime …
Local topology Entries and exits of the resource Exits connected to its entries and vice versa
Services Capabilities() >> resource capabilities representation Status() >> availability representation PossibleOps(setOfOps) >> possible processing steps Perform(…) >> execute an operation Set(…) >> e.g. download or select an NC
program What-if / predictive model (see further)
Basic Components of the Reference Architecture (cont.)
Task agent overall “goal”:
fulfill task by moving over resources in some
correct sequence fulfilling expected timing and quality requirements
of the order
Product recipe Reflects a product type for tasks, not product instances Knows possible ways to correctly produce instance(s) and provides this
information to … Minimizes assumptions about resource availability
E.G. Support 3 and 5 axis alternatives Methods
InitialState() >> product instance state representation
PossibleNextSteps(currentState) >> set of operations NextState(currentState, operation, result) >> state rep
Basic Components of the Reference Architecture (cont.)
Task agent overall “goal”:
fulfill task by moving over resources in some
correct sequence fulfilling expected timing and quality requirements
of the order
BDI – Beliefs–Desires–Intentions beliefs
resources possible (feasible) paths for reaching resources other task agents ?
desires / options several paths through the infrastructure / resources
intention a chosen path
…
Coordination model Basic entities in place
environment infrastructure agents vehicle agents
Now the system should support agents fulfilling tasks tasks are trips through the manufactory, along correct sequence of
resources
...taking timing and quality requirements into account... minimize production time avoid traffic jams ...
all this in an environment that changes constantly …and in which task agents enter the system constantly …
Task agents – BDI ?
how ? first alternative: task agent responsible for gathering, reasoning upon
and distributing information about resources
capabilities / quality / … topology expected schedule
about paths: find out feasible routes (match routes against product recipe) contact resources on paths judge on the quality of these paths
about intentions: communicate own intentions to other agents negotiate with other agents to align all agents’ intentions reserve resources if suitable
all this in an environment that changes constantly …and in which task agents enter the system constantly …
complex
Task agents – BDI ?
how ? second alternative:exploit environment to relief task agents
…
delegate MAS
have simple, small-scale agents (ants) roam environment and enrich enviroment with valuable information
optional paths intentions
align intention with intentions of other task agents through resource agents through refresh
Delegate MAS:Ant-based Coordination and Control
three kinds of delegate MASs Feasibility ants Exploration ants Intention ants
1. Ant agents
2. Pheromone deposition spaces
attached to each resource/enty/exit
! evaporation and refresh
Delegate MAS-1: Feasibility
Feasibility ants Purpose
maintain “road signs” (adapted on change in environment) road sign indicates possible sequences of operations
e.g.“if you go from this node to node N1, you are able to move over resources to perform operations op1 – op2 – (op3 – op4)* – op5”
Sent out by resources (esp. out resources) independent of orders / current load / …
Constrain routing to legal paths Ensure that the process plan can be executed properly Leave resource allocation choices open when there is no technical ground to
select or forbid Other feasibility concerns exist
Deadlock, livelock, starvation Quality and reliability issues
OUT
IN
= resource / machine = connection
Delegate MAS-1: Feasibility
Feasibility ants (continued) Information usage
Task agent, moving downstream, retrieves latest information when investigating routing options
Task agent uses this information to inspect its recipe
Delegate MAS-2: Exploration
Exploration ants Task agents “delegate” the exploration of possible trajectories
create exploration ants at a regular frequency that perform Forward exploration from current position / present Backward exploration from due date / shipping point(s)
Exploration ant virtually executes a possible scenario on behalf of the task agent and reports back the route and performance estimates
Enforces feasibility constraints Uses/reflects the decision module of the task agent Cloning and other variants are possible
OUT
order-1
Tries to explore feasible solutions time (travel + queuing + processing) and quality if task agent would follow this path ?
Searching for solutions is guided by local pheromones Reports result of solution to the corresponding Task Agent
Ra
Rb
Rc
Rd
Ra
Rb
Rc
Rd
Ra
Rb
Rc
Rd
Ra
Rb
Rc
Rd
Ra
Rb
Rc
Rd
resource schedule
+ what-if scenarios
orders
time
??
now
resource schedule
if reserved later on
orders
timenow
Delegate MAS-2: Exploration
Exploration ants (information usage) Task agent keeps a set of candidate routes
Performance criteria (good performance) Complementarity (reduce vulnerability)
Candidate routes are refreshed regularly Explicit/deterministic or stochastically Old candidates evaporate
Delegate MAS-3: Intention
Intention ants and forecasting
Task agent picks one candidate: intention Refresh before selection/replacing Warm-up phase
OUT
order-1
Task agent creates intention ants at a regular frequency to virtually execute the route of the current intention of their task agent
Intention ant informs visited resources on its virtual journey about the task agent's intention Makes a reservation/booking
Resource agents adapt what-if self-model Bookings evaporate if not refreshed
Ra
Rb
Rc
Rd
Delegate MAS –Intention reconsiderations
Frequency of exploration? Strategy to decide upon changing intentions
“Socially acceptable behaviour” Too nervous >> unreliable forecasting Too calm >> no adaptation to change/disturbances Too calm >> lock-in in early explorations
No change for small gains, limits on frequent changing
Less nervous for near future choices
Probabilistic changing and high enough refresh rate!
Experience
manufacturing control testbed / simulation environment considerable advantage w.r.t. adaptibility to dynamic environment
Experience
… traffic control?
45
Prototype: ExperimentsPrototype: Experiments
2
3
1
Always shortest route
Minimize travel time
Traffic DistributionAvg. Travel Time
Always avoid traffic jams
Conclusion
Delegate MAS reference model core abstractions
environment task/vehicle agents - basically BDI resource/infrastructure agents
coordination model environment centric light-weight ‘ants’ + pheromones bring relevant global information to local task agents
spread relevant global information through environment cope with dynamics
...
Conclusion: Delegate MAS Feasability delegate MAS
distribute “beliefs” about feasible paths drop the beliefs in the environment
task agents do not need to gather and maintain these beliefs
Exploration delegate MAS use the environment to find out the quality of different options
task agents do not need to directly contact and negotiate with resources
Intention delegate MAS use the environment to propagate intentions through the environment
task agents do not need to maintain beliefs and reason uponthe intentions of other agents’ for coordinating over resources
reduced complexity of task agent architecture
Many challenges / Open Issues There’s a cost …
additional infrastructure open resources computational/communication cost
needs to be managed properly! suitable refresh rate, cloning budgets, hop limits
Emergent behaviour / qualities … ? purely selfish agents – sufficient for overall optimization ?? homogeneous or heterogeneous?
Many parameters tune ? adaptive strategy ?
Coordination between resources BDI architecture Resource agent architecture …
Top Related