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)


MOISE+ controlled reorganisation (Hubner et al. 2004)


a diagnostic system that detects the need for
reorganisation
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
S0 [a, 4, 5]
S1 [b, 3, 9]
S2 [c, 5, 2]
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
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
X

Accumulated Service Set: the union of
the service set of the agent and the
Z
Y
service sets of its subordinates.

Agents are aware of
W

the personal service sets of their peers

the accumulated service sets of their
subordinates
Organisation at work: an example
S0 [a, 4, 5]
X
S1 [b, 3, 9]
S2 [c, 5, 2]
Z
Y
S3 [a, 8, 6]
S4 [d, 2, 3]
W
Task
Organisation
Evaluation Mechanism 1/3
Agents have to perform two kinds of actions

Allocation of service instances (management)

Execution of service instances
|TxE|

Load on agent x: lx = ∑ (rix + M.mix)
i=0


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

Tx is the set of tasks being executed by x

M is the management load coefficient
E
lx <= Lx ; excess tasks will be in the waiting queue Tx
W
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
A
A
x=0
x=0
Cost of the Organisation: Costorg = C. ∑ cx + ∑ Lx

A is the set of agents
Evaluation Mechanism 3/3

Benefit from x:
 rix
|TxE|
|TxW|
i=0
i=0
Benefitx = ∑rix - ∑rix
is the amount of computation required by task Ti
being executed by x


Tx is the set of tasks being executed by x

Tx is the set of tasks waiting to be executed by x
E
W
Benefit of the Organisation:
|A|
Benefitorg = ∑ Benefitx
x=0
Reorganisation - scenario
S0 [a, 4, 5]
X
S1 [b, 3, 9]
S2 [c, 5, 2]
Z
Y
S3 [a, 8, 6]
S4 [d, 2, 3]
W
Task
Organisation
Reorganisation Method: Actions

Formulated using the decision theoretic approach

Changing the relation – denoted as actions
Peers
Subordinate
Just acquaintances
Subordinate
Subordinate
Peers
Just acquaintances
Reorganisation Method: Value function

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.
Attribute values: FORM_SUBR(x,y) action

ΔLoadx = - Asgx,Tot * M * filledx(ttotal) / ttotal

LOAD * M * filled (tsubr) * ttotal / (tsubr)2
ΔLoady = - Asgx,y
x,y
y x,y

LOAD
ΔLoadOA = OAx,y
[load on other agents]

ΔCostcomm= OAx,yCOST
[cost because of other agents]

Costreorg = - R
[reorganisation cost constant]
The attribute values are calculated on basis of past
interactions 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
Simulation Parameters 1/2

Similarity between Tasks:

could be completely
unrelated

could be composed of a
finite set of constituents
(Patterns)
Results 1/2
Dissimilar
Tasks
Similar Tasks
Results 2/2
Distinct
service sets
Highly
overlapping
service 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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