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Topic 8: Coordination and Control Applications

Topic 8: Coordination and Control 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)

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Topic 8: Coordination and Control Applications

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  1. Topic 8: Coordination andControl Applications coordination and control applications a reference architecture ant-based coordination and control

  2. 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

  3. 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 !!

  4. 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 !!

  5. 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

  6. 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.

  7. IN OUT = resource / machine = connection

  8. Type-3 Type-2 Type-4 Type-1 Type-3 Type-2 Type-2 Type-4 Type-3 Type-1 Type-3 Type-1 Type-1 Type-2 Type-5 IN OUT = resource / machine = connection

  9. IN OUT = resource / machine = connection

  10. order-1 OUT = resource / machine = connection

  11. order-1 order-2 OUT = resource / machine = connection

  12. order-4 order-5 order-6 order-1 order-2 order-3 OUT = resource / machine = connection

  13. OUT = resource / machine = connection

  14. Multi-Agent Systems forCoordination and Control Applications • Centralized approaches • consider the problem to be an optimisation problem • operations research / static and dynamic • feasibility ...? • …

  15. 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

  16. 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]

  17. What is at the heart of the problem... • local decision makers • require global information for adequate decision making

  18. What could be at the heart of the solution... • local decision makers • find/isolate only that global informationthat is directly relevantfor adequate decision making

  19. Multi-Agent Systems forCoordination and Control Applications • … • Reference model for Coordination and Control applications • Decentralized components / agents • Environment-centric coordination model • Ant-inspired…

  20. Basic Components of the Reference Architecture  based on PROSA reference architecture • Agent types • Resource agent • Task agent • Environment

  21. 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

  22. 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)

  23. Basic Components of the Reference Architecture (cont.) • Task agent • overall “goal”: • fulfill task • by moving over resources in somecorrect sequence • fulfilling expected timing and quality requirementsof 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

  24. Basic Components of the Reference Architecture (cont.) • Task agent • overall “goal”: • fulfill task • by moving over resources in somecorrect sequence • fulfilling expected timing and quality requirementsof 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 • …

  25. 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 …

  26. 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

  27. 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

  28. 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

  29. 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

  30. IN OUT = resource / machine = connection

  31. 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

  32. 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

  33. 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 order-1 OUT

  34. resource schedule + what-if scenarios orders now time ??

  35. resource schedule if reserved later on orders now time

  36. 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

  37. Delegate MAS-3: Intention • Intention ants and forecasting • Task agent picks one candidate: intention • Refresh before selection/replacing • Warm-up phase

  38. 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 order-1 OUT

  39. 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!

  40. Experience • manufacturing control • testbed / simulation environment • considerable advantage w.r.t. adaptibility to dynamic environment

  41. Experience • … • traffic control?

  42. 45 Always avoid traffic jams 2 Minimize travel time Always shortest route 1 3 Prototype: Experiments Traffic Distribution Avg. Travel Time

  43. 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 • ...

  44. 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 upon the intentions of other agents’ for coordinating over resources  reduced complexity of task agent architecture

  45. 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 • …

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