Decentralised Structural Reorganisation in Agent Organisations

1. Ramachandra Kota Decentralised Structural Reorganisation in Agent Organisations

2. 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)‏

3. 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

4. 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

5. 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

6. 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)

7. 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.

8. 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

9. 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

10. Our Model: Tasks S0 [a, 4, 5] • 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 S1 [b, 3, 9] S2 [c, 5, 2] S3 [a, 8, 6] S4 [d, 2, 3]

11. 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

12. 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 Z Y W

13. 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

14. 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

15. 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 A A x=0 x=0

16. Evaluation Mechanism 3/3 |TxE| |TxW| • 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 |A| x=0

17. Reorganisation - scenario S0 [a, 4, 5] X X S1 [b, 3, 9] S2 [c, 5, 2] Z Z Y Y S3 [a, 8, 6] S4 [d, 2, 3] W W Task Organisation

18. Reorganisation Method: Actions • Formulated using the decision theoretic approach • Changing the relation – denoted as actions Peers Subordinate Subordinate Subordinate Peers Just acquaintances Just acquaintances

19. 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.

20. 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,y x,y x,y x,y x,y The attribute values are calculated on basis of past interactions and delegations involving the two agents

21. 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

22. 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

23. Simulation Parameters 1/2 • Similarity between Tasks: • could be completely unrelated • could be composed of a finite set of constituents (Patterns)

24. Results 1/2 Dissimilar Tasks Similar Tasks

25. Results 2/2 Distinct service sets Highly overlapping service sets

26. 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.

27. Thank you!! ??