jonne zutt delft university of technology information technology and systems

12/9/04 – Review TNO/TRAIL project #16 1

Jonne ZuttDelft University of Technology

Information Technology and Systems

Collective Agent Based Systems Group

Fault detection and recovery in multi-modaltransportation networks with autonomous mobile actors

TRAIL/TNO Project 16

Supervisors

Dr. C. Witteveen

Dr. ir. Z. Papp

Dr. ir. A.J.C. van Gemund


Contents

• Transportation planning• Problem description• Progress• Methods and hypotheses• Experiments


Issues in design and control of MHS

• Guide-path design• Estimating optimal

number of vehicles• Vehicle maintenance• Order allocation• Idle-vehicle positioning• Vehicle routing• Conflict-resolution


Layers

• Guide-path design• Estimating optimal

number of vehicles• Vehicle maintenance• Order allocation• Idle-vehicle positioning• Vehicle routing• Conflict-resolution

Strategic

Tactic

Operationalminutes

hours

months


Problem description

• Design a model for operational transport planning,

• Develop multi-agent routing and scheduling methods that can take into account incidents,

• Search suitable performance indicators to be used in experiments for comparing the quality of different methods taking into account properties of the environment.


Progress – previous years

• Model for operational transport planning

• Methods for operational transport planning taking into account incidents

• Transport planning simulator


Progress – last year

• Test set• Performance indicators• Experimental results• Thesis structure• Approximately two chapters written


Progress – future work

• Complete single-agent experiments [December’04]

• Coordination experiments [February’05]

• Writing [June’05]


Overview methods

fixed routing

rerouting

Arb-ci

HNZ-0HN

LPA*HNZ

no planning look-ahead

strictcommitments

loose commitments/ decommitments

hi

bj

hi b

j r

k


Conflicts1. Resources have limited capacity

A B C

2. Instantaneous exchange

ABDTime

A B C

ABTime

D


About cycles and deadlocks

A

B C

K(A)=1

P(K_sema_C)V(K_sema_B)

A

B

History: F,E,D,CCurrent: B,A


Methods – Simple/plan-based arbiter policies

• First-In-First-Out• Agent priority• Longest-Queue-First• Longest-Queue-First-Inc

• Longest-Plan-First• Most-Urgent-Deadline-First• Max-Reward-Decrease-First• Max-Reward-Decrease-Queue-First

Hypothesis:

No/very small

difference Hypothesis:

Plan-based policies

outperform the simple policies


Methods – HNZ

• Wait for a change in plan(s)• While agents are not ready– Compute traffic-aware shortest path– Agent compete who schedules first (P1)–Winner schedules n resources (P2)

• If current order rewards are below threshold, agent tries to reroute (P3)

Hypothesis: Much better than no planning

Hypothesis:Rerouting most important par


Method: agent selection functions (P1)

• RandomProvides a baseline for the others

• DelaysAgent with maximum wait time first

• DeadlinesAgent with most strict deadlines first

• PenaltiesAgent with lowest planned reward first

Hypo: All agent selection functions will outperform random


Method: resource block-size (P2)

• How many resources (fraction of route) are scheduled after the agent is selected by the agent selection function?

Hypothesis:A smaller block-size slightly increases

performance but also increases computation time


Number of reroute opportunities

Number of alternatives

Ave

rage

% o

f del

ay

Number of alternativesTa

rdin

ess

Tardiness aA Ca - a if Ca> aDelay { aA (Ca – Ma) / Ca } / |A|


Agent selectionA

vera

ge su

m o

f del

iver

y pe

nalti

es

No incidents Pfail = 0.1 Pfail = 0.20 reroutes 1 reroute 0 reroutes 1 reroute 0 reroutes 1 reroute

1. Random2. Delays3. Deadlines4. Penalties

0

500

1

000

150

0 2

000

250

0 3

000

350

0


Block size

No incidents Pfail = 0.1 Pfail = 0.2

0 1 1 1 1 1 10 0 0 0 01 1 1 1 1 1

Ave

rage

sum

of d

eliv

ery

pena

lties

2 2 4 6 ∞ 2 4∞ 2 ∞ 2 ∞6 ∞ 2 4 6 ∞

1. max. number of reroutes2. block size

0

100

0

2

000

300

0


Time for different block sizes

No incidents Pfail = 0.1 Pfail = 0.22 2 4 6 ∞ 2 4∞ 2 ∞ 2 ∞6 ∞ 2 4 6 ∞0 1 1 1 1 1 10 0 0 0 01 1 1 1 1 1

Ave

rage

cpu

tim

e

0

1

2

3

4

5

6

71. max. number of reroutes2. block size


Coordination – Coalition Formation

• Static– Different companies

• Dynamic– Based on current position– Based on source/destination locations, or

plan distance function– Grouped orders

Hypothesis:Dynamic coalitions are preferable, though static

coalitions already improve the coalition’s welfare


Coordination – How to improve welfare?

• Exchange orders with coalition members (cf. simulated trading)

• Conflict-resolution:In case of a conflict, determine Δ(C) instead of Δ(A) to determine who wins.


Questions• CABS project:

http://cabs.ewi.tudelft.nl• My homepage: http://dutiih.twi.tudelft.nl

/~jonne• My email:

[email protected]

http://cabs.ewi.tudelft.nl/

http://dutiih.twi.tudelft.nl/~jonne



mailto:[email protected]


Thesis1. Introduction

– Challenges in transportation– Problem description– Approach– Research contributions– Overview

2. A model and formalism for multi-agent transport planning

– Introduction– Building blocks– Correctness criteria– Performance criteria

3. Single-agent methods for transport planning

– Order allocation– Operational planning– Route planning– Simple arbiter policies– Revising priorities– Revising route– Lifelong Planning A*

4. Experiments on single-agent methods– Experimental setting– Description of the test set– Experimental results

5. Multi-agent methods for transport planning– Introduction– Coalition formation– Exchanging transportation orders– Conflict solving

6. Experiments on multi-agent methods– Experimental setting– Experimental results

7. Conclusions

A. Mathematical preliminariesB. Complexity of transport planning


Model

Auctioneeragent

Transportagent

Transportagent

Transportagent

Customeragent

Transportresource

Transportresource

Transportresource

speedcapacity

max. speedcapacitydistance

cooperativecompetitive


Model: incidents• Events that disrupt regular plan execution

and generally require re-planning• Examples: customers that change or retract

transportation orders, unpredictable congestion, vehicle break-down, communication failure

• Incidents are generated proportional to the resources. Pfail = 0.x means each resources is expected to fail x·10% of the time.


Method: traffic-aware shortest path

• Agents know which time-windows are in use by other agents per resource

• Run an A* algorithm: store routes on open list, check for conflict when appending to candidate route

• Process is guaranteed to terminate and find the traffic-aware shortest path


Experiments

• 10 transport networks with 25 resources, ‘random’ topology.

• 10 sets of transportation orders with 250 random orders each

• 2 different sets of agents with 25 randomly located agents each

• Incidents with failure probability 0, 0.1, …, 1.0 and impact 0.1.


Blocktime


Simple arbiter policies


HNZ-0/1 150 orders


HNZ-0/1 250 orders

jonne zutt delft university of technology information technology and systems

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