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(1)

Risk management in dynamic logistic

systems by agent based autonomous

objects

Boris Bemeleit

Jens Schumacher

Applied Information and Communication

Technology for Production

Faculty of Production Engineering

Martin Lorenz

Otthein Herzog

Expert System and Fundamentals of

Artificial Intelligence

Faculty of Mathematics and Computer

Science

(2)

Agenda

Motivation: Effects of Autonomy

Problem: Risk in and through Autonomy

Approach: Risk Management

Architecture of Application

(3)

Effects of Autonomy

Autonomy leads to...

...a transfer of responsibility and competencies of decisions from a central instance

to the autonomous objects

...different ways of communication and exchange of information

Central instance

for planning

Central Control

Intelligent

logistic object

Autonomy

To realize autonomy in logistic processes…

Factory Agent

Transport Agent

Storage Agent

Package Agent

will be represented by:

will be distributed:

Central

Intelligence

(4)

Management in dynamic logistic systems

ª

Complex and dynamic environment

ª

Local decision making reduces local complexity (for the individuum)

ª

BUT: Introducing local decisionmakers (i.e., autonomy) introduces more

complexity

ª

Even harder decision making problem

ª

New mechanisms for decision making are needed

Central instance

for planning

Central Control

Intelligent

logistic object

Autonomy

Risk Management:

The risk management with its containing parts risk identification, risk evaluation

& option generation should be able to identify existing and potential risk an by

establishing a relationship between the information and the goals of the logistic

objects (matching of risk patterns).

(5)

Risk in and through Autonomy

Customer

New opportunities result from autonomy:

...local changes of situations or information can be considered online, because no

exchange of information with other objects is required.

Transport Agent

Package Agent

Package Agent

Package Agent

Transport Agent

ª

The logistic objects can faster act and react but they have to consider

possible risk which may influence the success of logistic processes.

(6)

Autonomous Acting and Risk

Main

characteristic of

autonomous

logistic objects is

the independent

and autonomous

acting to reach

given goals

Autonomy

Risk management

Consideration of

possible risks and

uncertainties by a

suitable decision

making system

which realizes its

decisions via an

information system

for execution

presupposes

presupposes

supports

supports

(7)

Parameters of Risk

Parameters of risk which can appear in autonomous logistic processes

and for autonomous objects:

AO

may influence

E

External Risk

Event (E)

Autonomous Object (AO)

AO

AO

Autonomous Object (AO)

interaction

Internal Risk

Autonomous Object (AO)

Autonomous Object (AO)

AO

Information about

E

Information risk

(8)

Combined Parameters of Risk

AO

AO

E

Autonomous Object (AO)

Information about

may influence

Autonomous Object (AO)

Event (E)

interaction

External Risk

Internal Risk

Information risk

E (for example freezing rain, snowfall) could cause a jam

(possible accident, sudden brake)

The information could be out-dated (movement of

snow- or rain front)

Information exchange

could be interrupted

(9)

Estimated influence by risk management

ª

Enabling autonomous objects to risk-aware decision making

(10)

Role of Risk Management

Autonomous

Processes

Distributed

Knowledge

Management

Risk

Management

ƒ

Just-in-time delivery

of the right knowledge

to the right consumer

ƒ

Robustness

ƒ

Reliability

ƒ

Local knowledge

ƒ

Knowledge exchange

ƒ

knowledge as tradable

good

Vision

ƒ

Reliable management

of autonomous processes

ƒ

Identification of risk factors

(11)

Risk Management Process

Process Goal

Risk

identification

Risk

evaluation

Option

generation

Defining the

specific dimensions

of the process

Identifying the

risks for the

specific processes

Determining the

option with the

lowest total risk

Evaluating the

risks for the

specific

processes

Risk Analysis

Autonomous Object

Delivery reliability

State of goods

Profit

maximization

Delay

Perished/

damaged goods

Hidden costs

Alternative routes

Other production

possibilities

Alternative

transportation types

Risk of delay in

delivery

Possible cost

overrun

Damaged goods

(12)

Architecture of planned Application

intentions

generate options

desires

belief revision

goal list

filter options

beliefs

planning

sensor input

intention reconsideration

Intentions

action output

Risk identification and

evaluation

goal-oriented planner

probabilistic

knowledgebase

(13)

Risk Identification in Possible Worlds

Spanning possible worlds

t

Î

t+1 action:

pick up a paper roll

t+1

Î

t+2 action:

pull out or stay put

t+2

Î

t+3 event:

rain starts falling

(14)

Risk Identification in Possible Worlds

Extraction of state-variables

load:

paper-roll

environment:

pier 14, ramp C

humidity:

high

floor condition:

wet

(15)

Risk Identification in Possible Worlds

Identification of risk

state extraction of

possible world

application of risk

patterns

<O,R,

ρ

,F>

O=paper

R=openField

water_from_above

ρ

=0.75

F=total_loss

(16)

Risk Identification in Possible Worlds

risk: 0.9

risk: 0.4

risk: 0.3

Annotation of a plan

risk-values

– probability of this world

– risk factor of this world

grade of goal

fulfillment of final world

Î

decide for a plan

or

Î

variation / change of

the goal

(17)

Knowledge about the World

Forecast of possible worlds and its probability requires knowledge

Acquisition of knowledge

own sensors

communication

inference

Knowledge is inherently uncertain

depending on source and

channel

Interface with knowledge management component

evaluation of information need and

(18)

Next Step in Development: Reflecting Agent

Realization of decisions

and evaluation of potential

goal changes

Planning

Decision making

Reflexion

& Adaptation

Evaluation of

achievability of

alternative goals

Generation of alternative

actions

Derivations of long term

and side effects of the

plans

Evaluation based on

strategic maxims

(19)

Concluding Remarks

Risk parameters for an autonomous object

Impact of an event (external risk)

Information about an event (information risk)

Interaction of autonomous objects (internal risk)

Autonomous identification and analysis of risk

Goal oriented

Constitutive on planning

Combined with knowledge management (knowledge base)

Enhancement of autonomy by reflexion

Identification of risk due to long range effects and side effects

Evaluation of alternative goals

(20)

Boris Bemeleit

Bremen Institute for Industrial

Technology

and Applied Work Science

(BIBA)

Boris Bemeleit

Bremen Institute for Industrial

Technology

and Applied Work Science

(BIBA)

Thank you

Martin Lorenz

Center for Computing Technologies

(TZI)

Martin Lorenz

Center for Computing Technologies

(TZI)

Hochschulring 20

28359 Bremen

Germany

+49-(0)421-218 5519

bem@biba.uni-bremen.de

Am Fallturm 1

28359 Bremen

Germany

+49-(0)421-218 7828

mlo@tzi.de

CRC Website:

www.sfb637.uni-bremen.de

CRC Website:

www.sfb637.uni-bremen.de

Research funded by:

(21)
(22)

Evaluation without Occurrence Probability

Ignorance

h

i

: hypothesis

P(„it‘s raining”)

Pl(h

i

): plausibility

P(„it is possible that rain falls”)

Supp(h

i

): support

P(„rain necessarily falls“)

φ

i

= Pl(h

i

) – Supp(h

i

)

The narrower the interval [Supp;Pl] the more the agent knows

about h

i

– the surer he is that h

i

is true.

A hypothesis with importance for the decision process and high φ

i

needs more evidence…

References

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