mas course lect13 industrial applications
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MAS course at URV, lecture 13, industrial aplications of MASTRANSCRIPT
LECTURE 13: Industrial applications of Multi-Agent Systems
Artificial Intelligence II – Multi-Agent Systems
Introduction to Multi-Agent Systems
URV, Winter-Spring 2010
Outline of the talkAdoption of agent technology in real industrial applications
Application domain propertiesBottlenecksUsual agent technology conceptsSome domains with industrial applicationsFuture challengesConclusions
More details: M.Pechoucek, V.Marik: Industrial deployment of multi-agent technologies: review and selected case studies (AAMAS Journal, 2008)
Suitable domain properties for agent-based solutions (I)
Distributed and decentralized scenariosGeographical distribution of knowledge and control (e.g. logistics)Restrictions on information sharing, competitionbetween different actors (e.g. e-commerce)Domains were a time-critical response and high robustness are needed (e.g. manufacturing)
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
Main bottlenecks in the adoption of agent technology in industry (I)
Limited awareness of the agent technology potential in industryLimited publicity of succesful agent-based industrial projectsMisunderstandings about agent technology capabilities
Over-expectations of early industry adopters
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 toolsfor industrial deployment
Agent concepts used in typical agent technology deployments (I)
CoordinationConflict resolution, resource sharing
NegotiationAgreement about joint decisions, e.g. auctions
SimulationExamine global behaviour of the system when the local behaviour of each agent is known
InteroperabilityInteraction protocols, communication semantics
Agent concepts used in typical agent technology deployments (II)
BDI architectureOrganization
Agents joining in temporal or permanent social structures (e.g. coalitions)
Distributed planningTask decomposition and assignment, sharing and merging of partial results
Trust and reputationModels needed in non-collaborative environments
Some domains with industrial applications
Manufacturing controlProduction planningLogisticsSupply chain integrationTraffic managementSpace explorationDistributed diagnostics
Manufacturing controlMass-production of individually customized products (e.g. cars)Frequent changes of plans and schedules
Highly variable customization requirementsChanges in technologyEquipment failures
Example: automotive industriesDaimlerChrysler 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.
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.
Solution 1: flexible buffersFlexible 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.
Solution 2: Multi-functional stationsMulti-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.
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.
Overview – manufacturing control
Agent concepts: coordination, negotiation, distributed planning, simulation, interoperabilityFunctionality: control, simulation, diagnosticsApplication maturity: agent-based software prototypes, initial plan deploymentsThe integration with hardware is criticalRockwell Automation, DaimlerChrysler
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)
ExPlanTech systemDBA:database agent
ISML: external information system
CA: configurator agentSAs: scheduler agentsEEAs: extra-enterprise agents
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 outProduction management: contract the best possible scheduler agent (in terms of operational costs, delivery time and current capacity availability) for each pending task
Scheduler Agents
There is one SA for each manufacturing unit in the factoryThe main mission of a SA is to create a schedule for its manufacturing unit, checking that constraints are not violatedIt takes into account deadlines of each order, priorities, precedence dependencies, daily capacity of each unit, etc.
Extra-enterprise agents
Monitor AgentAllows customers to trace their ordersIt also allows the factory managers to inspect the operations of all the manufacturing units
Resource AgentIt 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
Overview –production planning
Agent concepts: coordination, distributed planning, simulation, interoperabilityFunctionality: planning, schedulingApplication maturity: prototypes, deployed systemsIt is important the integration with hardwareVolkswagen, Liaz, SkodaAuto
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 typesand capacities, are available in different locations.
Living Systems-Adaptive Transportation Networks (Whitestein)
Order typeVolumeWeightPick up location and time windowLoading and unloading timesDelivery location and time window…
Truck typeCapacity (volume)Capacity (weight)Special equipmentStart locationTariff…
Orders Trucks
Region-based solutionThere 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 AgentDistributoraccording 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.
Another agent-based solution
One agent for each truck and for each transport companyNegotiation between the trucks of a company, and between transport companies
Contract Net Protocol with trucks
Negotiation between transport companies
Overview – logistics
Agent concepts: coordination, negotiation, distributed planning, simulationFunctionality: planning, schedulingApplication maturity: operational systemsSystems usually integrated with hardwareMagenta, Whitestein
Supply chain integration
Integrate all the steps in the supply chainGetting orders from customersGetting raw material from suppliersProducing complex goodsDelivering produced goods to customers
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 customersA WarehouseAgent may manage the information of each warehouseThe LogisticsAgent can deal with the details of sending goods to customers and warehousesFor each Production Plant there may be Operation (planning) and Scheduling agents, as well as Resource Management Agents
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.
Overview –
supply chain integrated management
Agent concepts: knowledge sharing, auctioning, trust, interoperabilityFunctionality: integration, planning, coordinationApplication maturity: prototypesNo integration with hardwareSiemens, SAP, IBM
Another agent-based integration of supply-chain and logistics
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.
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
General SKADS architecture (I)
DAs: Data Agents, that receive data from sensorsAIAs: Action Implemention Agents, that execute the actions commanded by the decision makerUIAs: User Interface Agents, one for each user
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
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 operatorsOne AIA that executes the operators’decisions (display messages in road panels)
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.
Overview – traffic management
Agent concepts: coordination, simulationFunctionality: planning, scheduling, simulationApplication maturity: prototypes, deployed systemsSystems usually integrated with hardwareLabein
Space explorationSpace 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 operationaccording to the unexpected situations without consulting the ground stations. Both deliberative and reactive architectures are applicable in this domain.
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 ReliabilitySingle point failuresMultiple sequential failures
Tight resource constraints
Domain requirements (II)
Hard-time deadlinesE.g. executing an orbit insertion maneuver within a fixed time window
Limited observability of spacecraft stateLímited number of sensors
Concurrent ActivityComplex 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
Goals diversityFinal 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”
NASA-
DS1-
Remote Agent components
PS: Temporal planner and schedulerMM: Mission managerMIR: Mode Identification and ReconfigurationEXEC: Smart executive
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.
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.
EXEC: Smart ExecutiveEXEC 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 alternativemethod in that task's definition or by invoking the mode reconfiguration component of MIR.
Overview – space exploration
Agent concepts: BDI, autonomyFunctionality: control, planning, simulationApplication maturity: prototypes, deployed systemsThe integration with hardware is importantNASA
Distributed diagnosis
Diagnosis: analyze the information available from a mulfunctioning system, and determine the modules/parts/components of the system that are not working properlyDistributed: the information from the different parts of the system may not be centralised in a single Data Base
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 agentsEach 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”.
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 failureThe OIA provides a graphical interface to communicate with the human operator
Real application of MAGIC: hydraulic looper failures in metal lamination process
Overview – distributed diagnosis
Agent concepts: distributed learning, reasoning, knowledge sharing, interoperabilityFunctionality: diagnostics, simulation, data collectionApplication maturity: prototypesThe integration with hardware is importantDaimlerChrysler, Volkswagen, BMW
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.
Future trends (II)
More basic research on agent-oriented software methodologies with industrial-level techniques and toolsBetter tools for the visualization of the operations within a MASBigger efforts on semantic interoperabilityand knowledge sharingMore secure (intrusion detection) and safe(completeness checking) systems
Conclusions
Still many obstacles to overcomeLack of engineers especialised in distributed systemsReluctance to use distributed (rather than centralised) solutions to industry problemsCosts of agent-based solutions are usually higher than those of a centralised systemEnd users are not aware of agent technology and are not able to maintain these systems
Extra material for this week
M.Pechoucek, V.Marik: Industrial deployment of multi-agent technologies: review and selected case studies(AAMAS Journal, 2008)