Decentralised Structural

Reorganisation in

Agent Organisations

Ramachandra Kota

Motivation Autonomic systems

computing systems with self-management

solution to the problem of maintaining large, complex

computing systems? (Kephart and Chess, 2003)

Self-organising multi-agent systems

autonomous, adaptive and robust

a paradigm to develop autonomic systems (Tesauro et al., 2004)

Self-Organisation: Characteristics

(Di Marzo Serugendo et al., 2005, 2006)

No External Control – autonomous

Dynamic Operation – continuous over time

No Central Authority – decentralised and robust

Problem Solving

Agent Organisations

We need agent systems which can be mapped

onto computing systems that perform tasks

We focus on multi-agent systems that act as a

problem solving organisation

organisations that receive inputs, perform tasks and

return results

Research Objective

“Develop a decentralised reorganisation method

that can be employed by the agents in a problem

solving agent organisation to improve the

performance of the organisation as a whole.”

can be used by any agent at any level of the

organisation, at any time.

focus on changing the organisational characteristics

rather than the agents themselves

Self-organisation approaches Stigmergic

self-organisation emerges through indirect interactions of the

agents (Mano et al., 2006)

Organisational Self Design (OSD)

splitting and merging of agents to achieve reorganisation Gasser and Ishida (1991), Kamboj and Decker (2006)

Adaptive Multi-Agent Systems theory (AMAS)

agents perceive non-cooperative situations (pre-specified) and

take rectifying measures. (Capera et al., 2003)

Other Reorganisation Approaches Diagnostic Subsystem in Agents (Horling et al. 2001)

a diagnostic system that detects the need for

reorganisation

MOISE+ controlled reorganisation (Hubner et al. 2004)

a top-down approach using specialised agents

Max-flow network approach (Hoogendoorn 2007)

a centralised solution to resolve bottle-necks

There is no existing decentralised mechanism to improve the

performance of an organisation composed of invariant agents.

Agent Organisation Model To act as a framework on which to base our

reorganisation method

Existing models:

Moise, Islander, VDT, Opera, Omni etc

We pick up ideas from several models to develop

a simple framework

Our Model: Agents Problem solving agents

receive a task

assign its dependencies and obtain the results of their execution

execute the task and return the result.

Invariant and cooperative agents

Provide a set of services (SA)

Have limited computational capacity (LA)

Example:

Agent A = < SA , LA > where SA = {a, b}, LA = 10 computational units

Agent B = < SB , LB > where SB = {b, c, d} LB = 15 computational units

Our Model: Tasks Tree structure

Every node represents a service instance

A service instance specifies

type of service

computational units per time-step

number of time-steps required

Dependency - a node can be executed only after the completion of all its child-nodes

S0 [a, 4, 5]

S1 [b, 3, 9]

S2 [c, 5, 2]

S3 [a, 8, 6] S4 [d, 2, 3]

Our Model: Organisation Structure

Structure is based on the relationships between

the agents

Relation between two agents determines the kind

of interaction possible between them

Three kinds of relationships:-

Acquaintance: no interaction

Peer: weak interaction

Authority (superior-subordinate): strong interaction

Our Model: Agent Relations

All agents are acquaintances of each

other

Accumulated Service Set: the union of

the service set of the agent and the

service sets of its subordinates.

Agents are aware of

the personal service sets of their peers

the accumulated service sets of their

subordinates

X

Y Z

W

Organisation at work: an example

X

Y

W

Z

Task Task

S0 [a, 4, 5]

S1 [b, 3, 9]

S3 [a, 8, 6] S4 [d, 2, 3]

S2 [c, 5, 2]

OrganisationOrganisation

Evaluation Mechanism 1/3

Agents have to perform two kinds of actions

Allocation of service instances (management)

Execution of service instances

Load on agent x: lx = ∑ (rix + M.mix)

rix is the amount of processing computation of x required by task Ti,

mix is the amount of management computation done by x for task Ti

TxE is the set of tasks being executed by x

M is the management load coefficient

lx <= Lx ; excess tasks will be in the waiting queue TxW

|TxE|

i=0

Evaluation Mechanism 2/3

Performance is determined by cost and benefit of the

organisation, calculated at every time step.

Cost of agent x: Costx = Lx + C.cx

Lx is capacity of agent x

cx is the number of messages sent by x

C is communication cost coefficient

Cost of the Organisation: Costorg = C. ∑ cx + ∑ Lx

A is the set of agents

x=0

AA

x=0

AA

Evaluation Mechanism 3/3

Benefit from x: Benefitx = ∑rix - ∑rix

rix is the amount of computation required by task Ti

being executed by x

TxE is the set of tasks being executed by x

TxW is the set of tasks waiting to be executed by x

Benefit of the Organisation:

Benefitorg = ∑ Benefitx

i=0 i=0

|TxE| |TxW|

x=0

|A|

Reorganisation - scenario

X

Y

W

Z

Task Task

S0 [a, 4, 5]

S1 [b, 3, 9]

S3 [a, 8, 6] S4 [d, 2, 3]

S2 [c, 5, 2]

OrganisationOrganisation

X

Y

W

Z

Reorganisation Method: Actions

Formulated using the decision theoretic approach

Changing the relation – denoted as actions

Just Just acquaintancesPeersPeersSubordinateSubordinate

Just acquaintancesJust acquaintancesSubordinateSubordinatePeersPeersSubordinateSubordinate

Pairs of agents jointly estimate the expected

utility of changing their relation

A combined Value function of the form:

Vx,y = ΔLoadx+ΔLoady+ΔLoadOA+ΔCostcomm+Costreorg

Value is calculated for every possible action in

the state and the action with maximum expected

value is chosen.

Reorganisation Method: Value function

Attribute values: FORM_SUBR(x,y) action

ΔLoadx = - Asgx,Tot * M * filledx(ttotal) / ttotal

ΔLoady = - AsgLOAD * M * filledy(tsubr) * ttotal / (tsubr)2

ΔLoadOA = OALOAD [load on other agents]

ΔCostcomm= OACOST [cost because of other agents]

Costreorg = - R [reorganisation cost constant]

x,yx,y x,yx,y x,yx,y

x,yx,y

x,yx,y

The attribute values are calculated on basis of past The attribute values are calculated on basis of past

interactions and delegations involving the two agentsinteractions and delegations involving the two agents

Experimental Evaluation

Compare our method with a random

reorganisation strategy.

Random strategy: An agent randomly chooses to

change some of its relations

Performance is evaluated on basis of the average

cost and benefit obtained from the simulation

runs

Simulation Parameters 1/2

Distribution of Services:

agents may have distinct service sets or overlapping

service sets

determined by ‘service probability’ (sp)

sp = 0 : every agent has a unique service set

sp = 1 : every agent can perform all services

Similarity between Tasks:

could be completely

unrelated

could be composed of a

finite set of constituents

(Patterns)

Simulation Parameters 1/2

Results 1/2

Dissimilar Dissimilar

TasksTasks

Similar TasksSimilar Tasks

Results 2/2

Distinct Distinct

service setsservice sets

Highly Highly

overlapping overlapping

service setsservice sets

Future Work

Upper bound:

an oracle organisation with complete information of the future

tasks

a centralised reorganiser/allocator

Efficient Reorganisation

compute utilities for a selective set of relations only, at a given

time

Dynamic agents, organisation norms etc.

Thank you!!

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