Industrial applications of multi-agent systems was discussed. Key points included: - Agent technology has been adopted in domains like manufacturing control, production planning, logistics, and supply chain integration where distributed control and open systems are needed. - Main bottlenecks to adoption are awareness, risk, and lack of mature tools. Common agent concepts used include coordination, negotiation, distributed planning, and interoperability. - Examples of deployed systems include control of engine assembly plants, production planning systems, logistical routing of transport orders, and supply chain integration platforms. Future challenges include greater integration with hardware.

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. 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. 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. 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. 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. 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. 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. 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. Some domains with industrial applications
Manufacturing control
Production planning
Logistics
Supply chain integration
Traffic management
Space exploration
Distributed diagnostics

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. 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. 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. 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. 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. 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. 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. ExPlanTech system
DBA:database
agent
ISML: external
information
system
CA: configurator
agent
SAs: scheduler
agents
EEAs: extra-
enterprise agents

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. 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. 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. 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. 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. 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. 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. 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. Contract Net Protocol with trucks

27. Negotiation between transport companies

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. 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. 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. 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. 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. Another agent-based integration of
supply-chain and logistics

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. 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. 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. 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. 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. 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. Overview – traffic management
Agent concepts: coordination, simulation
Functionality: planning, scheduling,
simulation
Application maturity: prototypes, deployed
systems
Systems usually integrated with hardware
Labein

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. 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. 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. 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. NASA- DS1- Remote Agent components
PS: Temporal
planner and
scheduler
MM: Mission
manager
MIR: Mode
Identification and
Reconfiguration
EXEC: Smart
executive

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. 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. 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. Overview – space exploration
Agent concepts: BDI, autonomy
Functionality: control, planning, simulation
Application maturity: prototypes, deployed
systems
The integration with hardware is important
NASA

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. 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. 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. Real application of MAGIC: hydraulic
looper failures in metal lamination process

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. 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. 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. 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. Extra material for this week
M.Pechoucek, V.Marik: Industrial
deployment of multi-agent technologies:
review and selected case studies
(AAMAS Journal, 2008)