1. Decentralised Structural Reorganisation in Agent Organisations Ramachandra Kota

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 (S A )‏ Have limited computational capacity (L A )‏ Example:  Agent A = where S A = {a, b}, L A = 10 computational units  Agent B = where S B = {b, c, d} L B = 15 computational units

10. 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 S 0 [a, 4, 5] S 1 [b, 3, 9] S 2 [c, 5, 2] S 3 [a, 8, 6]S 4 [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 Y Z W

13. Organisation at work: an example X Y W Z Task S 0 [a, 4, 5] S 1 [b, 3, 9] S 3 [a, 8, 6]S 4 [d, 2, 3] S 2 [c, 5, 2] 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: l x = ∑ (r ix + M.m ix )  r ix is the amount of processing computation of x required by task T i,  m ix is the amount of management computation done by x for task T i  T x E is the set of tasks being executed by x  M is the management load coefficient l x <= L x ; excess tasks will be in the waiting queue T x W |TxE||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: Cost x = L x + C.c x  L x is capacity of agent x  c x is the number of messages sent by x  C is communication cost coefficient Cost of the Organisation: Cost org = C. ∑ c x + ∑ L x  A is the set of agents x=0 A A

16. Evaluation Mechanism 3/3 Benefit from x: Benefit x = ∑r ix - ∑r ix  r ix is the amount of computation required by task T i being executed by x  T x E is the set of tasks being executed by x  T x W is the set of tasks waiting to be executed by x Benefit of the Organisation: Benefit org = ∑ Benefit x i=0 |T x E ||T x W | x=0 |A|

17. Reorganisation - scenario X Y W Z Task S 0 [a, 4, 5] S 1 [b, 3, 9] S 3 [a, 8, 6]S 4 [d, 2, 3] S 2 [c, 5, 2] Organisation X Y W Z

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

19. Pairs of agents jointly estimate the expected utility of changing their relation A combined Value function of the form: V x,y = ΔLoad x +ΔLoad y +ΔLoad OA +ΔCost comm +Cost reorg Value is calculated for every possible action in the state and the action with maximum expected value is chosen. Reorganisation Method: Value function

20. Attribute values: FORM_SUBR (x,y) action ΔLoad x = - Asg x,Tot * M * filled x (t total ) / t total ΔLoad y = - Asg LOAD * M * filled y (t subr ) * t total / (t subr ) 2 ΔLoad OA = OA LOAD [load on other agents] ΔCost comm = OA COST [cost because of other agents] Cost reorg = - R [reorganisation cost constant] x,y x,yx,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. Similarity between Tasks:  could be completely unrelated  could be composed of a finite set of constituents (Patterns) Simulation Parameters 1/2

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!! ??