MAS course Lect13 industrial applications


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MAS course at URV, lecture 13, industrial aplications of MAS

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MAS course Lect13 industrial applications

  1. 1. LECTURE 13: Industrial applications of Multi-Agent Systems Artificial Intelligence II – Multi-Agent Systems Introduction to Multi-Agent Systems URV, Winter-Spring 2010
  2. 2. Outline of the talk Adoption of agent technology in real industrial applications Application domain properties Bottlenecks Usual agent technology concepts Some domains with industrial applications Future challenges Conclusions More details: M.Pechoucek, V.Marik: Industrial deployment of multi-agent technologies: review and selected case studies (AAMAS Journal, 2008)
  3. 3. Suitable domain properties for agent-based solutions (I) Distributed and decentralized scenarios Geographical distribution of knowledge and control (e.g. logistics) Restrictions on information sharing, competition between different actors (e.g. e-commerce) Domains were a time-critical response and high robustness are needed (e.g. manufacturing)
  4. 4. Suitable domain properties for agent-based solutions (II) Simulation and modeling problems (e.g. traffic flow) Open systems (e.g. interoperability between independently-designed computer systems) Complex systems The global decision making process has to be decomposed into separate agents’ reasoning and solving problems by means of negotiation Autonomous systems, where the user delegates the decision making authority to the system
  5. 5. Main bottlenecks in the adoption of agent technology in industry (I) Limited awareness of the agent technology potential in industry Limited publicity of succesful agent-based industrial projects Misunderstandings about agent technology capabilities Over-expectations of early industry adopters
  6. 6. Main bottlenecks in the adoption of agent technology in industry (II) Risk of adopting a new technology that has not been proven in large-scale industrial applications yet “We don’t want to be the first ones to use it” Lack of mature design and development tools for industrial deployment
  7. 7. Agent concepts used in typical agent technology deployments (I) Coordination Conflict resolution, resource sharing Negotiation Agreement about joint decisions, e.g. auctions Simulation Examine global behaviour of the system when the local behaviour of each agent is known Interoperability Interaction protocols, communication semantics
  8. 8. Agent concepts used in typical agent technology deployments (II) BDI architecture Organization Agents joining in temporal or permanent social structures (e.g. coalitions) Distributed planning Task decomposition and assignment, sharing and merging of partial results Trust and reputation Models needed in non-collaborative environments
  9. 9. Some domains with industrial applications Manufacturing control Production planning Logistics Supply chain integration Traffic management Space exploration Distributed diagnostics
  10. 10. Manufacturing control Mass-production of individually customized products (e.g. cars) Frequent changes of plans and schedules Highly variable customization requirements Changes in technology Equipment failures Example: automotive industries DaimlerChrysler engine assembly plant at Stuttgart, Germany. The plant produces Mercedes-Benz V6 and V8 engines with a volume of more than 800 units per day.
  11. 11. Engine block assembly - DaimlerChrysler Problem: very small in-process buffers in the engine assembly line • The cycle time is less than 90 seconds, so the buffers last only for a few minutes • If a station breaks down or stops because of a supply shortage, soon stations up the line have to stop because workpieces cannot proceed, and stations down the line run out of workpieces.
  12. 12. Solution 1: flexible buffers Flexible buffers may be dynamically located at any position in the assembly line. Engines are taken off the main line in front of a broken station and transported to a flexible buffer. If a buffer contains engines that have previously been taken off the main line between the broken and the next station, these are transported back to the main line and put on the conveyor belt right after the broken station.
  13. 13. Solution 2: Multi-functional stations Multi-functional (MF) stations can perform the same assembly operations as a set of stations on the main assembly line, but with higher processing times as they are operated manually. In case of a disturbance/bottleneck, the MF stations can be used to replace or increase the capacity of the stations at the main line.
  14. 14. Agent-based control of manufacturing process There is an agent for each buffer, MF station, docking station (DS) and AGV (automated guided vehicles, that transport engines between docking stations and buffers). All these agents have to communicate to coordinate their actions. DS agents decide when to divert an engine from the main line. MF agents and Buffer agents decide where to send each engine (to a DS, another buffer or to another MF station). AGV agents receive transport requests from DS agents.
  15. 15. Overview – manufacturing control Agent concepts: coordination, negotiation, distributed planning, simulation, interoperability Functionality: control, simulation, diagnostics Application maturity: agent-based software prototypes, initial plan deployments The integration with hardware is critical Rockwell Automation, DaimlerChrysler
  16. 16. Production planning Aim: elaborate a production plan in a project- driven manufacturing setting Not mass-production, as in the manufacturing case, but rather project-oriented production (e.g. space shuttle)
  17. 17. ExPlanTech system DBA:database agent ISML: external information system CA: configurator agent SAs: scheduler agents EEAs: extra- enterprise agents
  18. 18. DataBase Agent and Configurator Agent DBA: manages DB with production data, acts as a bridge between the MAS and the external information system. CA: takes two roles Planning: construct an exhaustive, partially ordered list of tasks to be carried out Production management: contract the best possible scheduler agent (in terms of operational costs, delivery time and current capacity availability) for each pending task
  19. 19. Scheduler Agents There is one SA for each manufacturing unit in the factory The main mission of a SA is to create a schedule for its manufacturing unit, checking that constraints are not violated It takes into account deadlines of each order, priorities, precedence dependencies, daily capacity of each unit, etc.
  20. 20. Extra-enterprise agents Monitor Agent Allows customers to trace their orders It also allows the factory managers to inspect the operations of all the manufacturing units Resource Agent It works on the side of each supplier, announcing the status of available services and resources, so that the production system has precise and actual data for its computations
  21. 21. Overview –production planning Agent concepts: coordination, distributed planning, simulation, interoperability Functionality: planning, scheduling Application maturity: prototypes, deployed systems It is important the integration with hardware Volkswagen, Liaz, SkodaAuto
  22. 22. Logistics Transportation problem: finding optimal routes for serving dynamic transportation orders of a large set of costumers. Orders have to be picked up and delivered at specific customer locations, within certain time windows. A limited number of trucks, of different types and capacities, are available in different locations.
  23. 23. Living Systems-Adaptive Transportation Networks (Whitestein) Order type Truck type Volume Capacity (volume) Weight Capacity (weight) Pick up location and Special equipment time window Start location Loading and unloading Tariff times … Delivery location and time window … Orders Trucks
  24. 24. Region-based solution There is an agent (called AgentRegionManager) for each geographical region, that manages all the trucks starting in that region. Incoming orders are received by an EventHandlerAgent and distributed by a centralized AgentDistributor according to their pickup location. Orders arriving at a region are first tentatively allocated and optimized within that region. If the order’s pickup or delivery location is in a different region, the other region is informed and asked to handle the order if it can do so more cheaply.
  25. 25. Another agent-based solution One agent for each truck and for each transport company Negotiation between the trucks of a company, and between transport companies
  26. 26. Contract Net Protocol with trucks
  27. 27. Negotiation between transport companies
  28. 28. Overview – logistics Agent concepts: coordination, negotiation, distributed planning, simulation Functionality: planning, scheduling Application maturity: operational systems Systems usually integrated with hardware Magenta, Whitestein
  29. 29. Supply chain integration Integrate all the steps in the supply chain Getting orders from customers Getting raw material from suppliers Producing complex goods Delivering produced goods to customers
  30. 30. Agent-based supply chain (I) Supplier Agents model each of the suppliers. They are contacted by an especialised Purchase Agent. RetailerAgents represent each of the customers A WarehouseAgent may manage the information of each warehouse The LogisticsAgent can deal with the details of sending goods to customers and warehouses For each Production Plant there may be Operation (planning) and Scheduling agents, as well as Resource Management Agents
  31. 31. Agent-based supply chain (II) A customer orders are received by a Retailer agent. The Logistics agent may check if the requested item is available in some warehouse. Otherwise, the order is sent to a Production Plant. The Operation and Scheduling agents of the production plan apply some reasoning procedures to find out the most efficient steps in the construction of the requested goods. If some raw material is needed, the Resource Management agent is informed, and a request is sent to the Purchase Agent. The Purchase Agent will make a negotiation with the Supplier Agents that represent those supplier companies that can deliver the raw materials.
  32. 32. Overview – supply chain integrated management Agent concepts: knowledge sharing, auctioning, trust, interoperability Functionality: integration, planning, coordination Application maturity: prototypes No integration with hardware Siemens, SAP, IBM
  33. 33. Another agent-based integration of supply-chain and logistics
  34. 34. Traffic management Two basic kinds of problems: Make simulations with different road settings (e.g. different times and locations of traffic lights) to analyze the traffic flow in each case. Help human traffic operators to take real-time decisions about actions to perform on the basis of incoming data of traffic flow. Ask local authorities to send appropriate people to manage complex situations. Display messages in road panels to warn drivers about traffic problems or recommend alternative routes.
  35. 35. Example of a deployed application Analysis of part of the high-capacity road network in the area of Bilbao (ring road + 4 main accesses) Information received in the Mobility Management Center, where operators have to detect problems and decide the actions to undertake to solve them
  36. 36. General SKADS architecture (I) DAs: Data Agents, that receive data from sensors AIAs: Action Implemention Agents, that execute the actions commanded by the decision maker UIAs: User Interface Agents, one for each user
  37. 37. General SKADS architecture (II) PAs: third-party Peripheral Agents that provide external services (+ DF, AMS) MAs: Management Agents, that have knowledge models that allow them to reason and detect current and future states/problems and to suggest potential management actions
  38. 38. Instantiation of SKADS architecture in the road traffic management problem (I) 12 DAs, one for each problem area (defined according to geographical criteria) Collect and filter data, transform quantitative into qualitative values One UIA that interacts with traffic operators One AIA that executes the operators’ decisions (display messages in road panels)
  39. 39. Instantiation of SKADS architecture in the road traffic management problem (II) Two types of MAs: 12 Problem Detection Agents (PDAs) and 5 Control Agents (CAs) PDAs receive the data and, from their knowledge on the physical structure of the road and the dynamics of traffic, detect potential problems, which are sent to the CAs, that generate control proposals.
  40. 40. Overview – traffic management Agent concepts: coordination, simulation Functionality: planning, scheduling, simulation Application maturity: prototypes, deployed systems Systems usually integrated with hardware Labein
  41. 41. Space exploration Space exploration applications share very high requirements for intelligent systems with autonomy and ability to operate with only partial, higher level instructions provided in a non-timely fashion. Reasoning systems are expected to follow their mission objectives (regularly updated) and be able to update and revise their operation according to the unexpected situations without consulting the ground stations. Both deliberative and reactive architectures are applicable in this domain.
  42. 42. Domain requirements (I) Perform autonomous operations for long periods of time with no human intervention Cost and limitations of the deep space communication network, spacecraft occultation when it is behind a planet, and communication delays High Reliability Single point failures Multiple sequential failures Tight resource constraints
  43. 43. Domain requirements (II) Hard-time deadlines E.g. executing an orbit insertion maneuver within a fixed time window Limited observability of spacecraft state Límited number of sensors Concurrent Activity Complex networked, multi-processor system, with some flight computers communicating with sophisticated sensors, actuator subsystems, and science instruments. E.g. stop main engine when taking a picture to reduce vibration Achieve diverse goals on real spacecraft
  44. 44. Goals diversity Final state goals “Turn off the camera once you are done using it” Scheduled goals “Communicate to Earth at pre-specified times” Periodic goals “Take asteroid pictures for navigation every 2 days for 2 hours” Information-seeking goals “Ask the on-board navigation system for the thrusting profile” Continuous accumulation goals “Accumulate thrust data” Default goals “When you have nothing else to do, point High Gain Antenna to Earth”
  45. 45. NASA- DS1- Remote Agent components PS: Temporal planner and scheduler MM: Mission manager MIR: Mode Identification and Reconfiguration EXEC: Smart executive
  46. 46. Mode identification and reconfiguration Mode identification (MI): tracks the most likely spacecraft states by identifying states whose models are consistent with the sensed monitor values. MI reports all inferred state changes to EXEC, who can reason purely in terms of spacecraft states. Mode reconguration (MR): when something is wrong, it uses the spacecraft model to find an optimal recovery plan that, when executed by EXEC, restores the desired functionality by reconfiguring hardware or repairing failed components. It is a reactive agent, with fast response times.
  47. 47. Planner/Scheduler and Mission Manager Mission Manager (MM): has information on the mission profile, provided at launch and updated from the ground when necessary. It contains a list of goals to be achieved during the mission. MM determines the goals that need to be achieved in the next horizon (1-2 weeks) and formulates short-term planning problems for PS. Planner/Scheduler (PS): temporal planner and resource scheduler. It takes the plan request formulated by MM and uses a heuristic-guided search to produce a executable, concurrent temporal plan. The plan constrains the activity of each spacecraft subsystem over its duration, but leaves flexibility for details to be resolved during execution.
  48. 48. EXEC: Smart Executive EXEC executes plans by decomposing high-level activities in the plan into commands to the real-time system, while respecting temporal constraints in the plan. EXEC achieves robustness in plan execution by exploiting the plan's flexibility, e.g., by being able to choose execution time within specified windows or by being able to select different task decompositions for a high-level activity. When some method to achieve a task fails, EXEC attempts to accomplish the task using an alternative method in that task's definition or by invoking the mode reconfiguration component of MIR.
  49. 49. Overview – space exploration Agent concepts: BDI, autonomy Functionality: control, planning, simulation Application maturity: prototypes, deployed systems The integration with hardware is important NASA
  50. 50. Distributed diagnosis Diagnosis: analyze the information available from a mulfunctioning system, and determine the modules/parts/components of the system that are not working properly Distributed: the information from the different parts of the system may not be centralised in a single Data Base
  51. 51. MAGIC: Multi-agent system for data acquisition, diagnosis and management of complex processes (I) PSA: characterizes the kind of process to analyze and configures the other agents Each DAA is associated to a particular physical sensor, and receives the data that it provides. The DB stores the data and all the information related to the process. Each DA applies a different method (statistical techniques, neural networks, Bayesian networks, frequency analysis) to analyze the received data in order to detect “symptoms”.
  52. 52. MAGIC: Multi-agent system for data acquisition, diagnosis and management of complex processes (II) DDA: makes a logical reasoning on the symptoms detected by the DAs to propose a diagnosis decision (a component failure) The DSA gives advice to the human operator, suggesting ways to solve the detected failure The OIA provides a graphical interface to communicate with the human operator
  53. 53. Real application of MAGIC: hydraulic looper failures in metal lamination process
  54. 54. Overview – distributed diagnosis Agent concepts: distributed learning, reasoning, knowledge sharing, interoperability Functionality: diagnostics, simulation, data collection Application maturity: prototypes The integration with hardware is important DaimlerChrysler, Volkswagen, BMW
  55. 55. Future trends (I) Use of MAS for simulation, especially for domains where the aim is to go from agent- based simulation to agent-based control. More extensive use in applications integrated with hardware devices, where decentralised solutions are needed. More autonomous systems, in fields like traffic management, defense applications, resource sharing in grid computing.
  56. 56. Future trends (II) More basic research on agent-oriented software methodologies with industrial-level techniques and tools Better tools for the visualization of the operations within a MAS Bigger efforts on semantic interoperability and knowledge sharing More secure (intrusion detection) and safe (completeness checking) systems
  57. 57. Conclusions Still many obstacles to overcome Lack of engineers especialised in distributed systems Reluctance to use distributed (rather than centralised) solutions to industry problems Costs of agent-based solutions are usually higher than those of a centralised system End users are not aware of agent technology and are not able to maintain these systems
  58. 58. Extra material for this week M.Pechoucek, V.Marik: Industrial deployment of multi-agent technologies: review and selected case studies (AAMAS Journal, 2008)