AutoPilot Year 1 Results

1. University of Missouri - Kansas City AutoPilot Year 1 Results Principal Investigators: Jerry Stach E.K. Park

2. University of Missouri - Kansas City Questions Posed By Sponsor • 1. Decentralized Scaleable Trader • (a) How do you maintain global information about a set of available services without a central point of failure? • (b) What are the Query and Update costs of a decentralized Trader?

3. University of Missouri - Kansas City Questions Posed By Sponsor • 2. Agent Health Monitor • Investigate ways to monitor large numbers of mobile/distributed agents with minimal effect on overall systems performance.

4. University of Missouri - Kansas City Questions Posed By Sponsor • 3. Mobile Agent Patterns • As work on the first two categories progresses, document any design patterns that emerge.

5. University of Missouri - Kansas City Engineering Problems - Current System • Only about 103 agents can run concurrently in the current network. The answers to the Sponsor’s questions must scale at least one order of magnitude: • 104 concurrent mobile agents • 104 service nodes with 10 – 20 service instances per node • the number of Trader Places approaching 103 • 2*103 entries per directory.

6. University of Missouri - Kansas City Underlying Causes • The current limitation of 103 Agents implies Band Limiting • Band Limiting can Occur in the Service Place CPU if mean processing time is high relative to agent arrivals • Band Limiting can occur in the Network as a function of message intensity • focused overloads (agent  Trader Place) • agent collaboration or management

7. University of Missouri - Kansas City Band Limiting in the Service Place CPU • Possible Strategies • Accelerate CPUs • Increase number of CPUs • Acceleration may not place the power in the right locations • Increasing the number of Service Places increases band width demand of the network

8. University of Missouri - Kansas City Implications • If arbitrary increases in the number of Service Places are to be avoided, concurrency is implied to maximize CPU utilization • agents should have capability to function as autonomous distributed processes • mobility must include agent reasoning about local congestion and distance

9. University of Missouri - Kansas City Band Limiting in the Network • Strategy • Minimize number of messages in the Agent Colony • messages associated with accessing the Trader Place regarding Service offerings [Sponsor Question 1] • messages associated with managing the colony of agents [Sponsor Question 2]

10. University of Missouri - Kansas City Implication • Minimizing the number of messages to the Trader Places implies some intermediate process in the Architecture that can parse the Trader Place vector for multiple agents at a Service Place. The agent must then be able to interpret the vector relative to its own preferences. The mobility decision is implied at the agent (lowest Architecture level) not at the Trader (highest Architecture level)

11. University of Missouri - Kansas City Implication • Minimizing the number of messages associated with population management implies being able to anticipate the location of a given agent to eliminate exhaustive search or suspension of the population. This implies higher levels of the Architecture must understand the mobility decision in order to locate agents in the network

12. University of Missouri - Kansas City Problem Synthesis • A multi - level Architecture is implied Agent Service Planner Trader

13. University of Missouri - Kansas City Problem Synthesis • Migration is fundamental to the answers to Questions 1, 2 • agents are situated and must move to a subsequent Service Place based upon • service attributes of time, cost, quality (perception) • a context of total moves that satisfies their service sequences (local optimization)

14. University of Missouri - Kansas City Problem Synthesis • Agent migrations should not adversely effect the network system • moves should be sensitive to network dynamics such as local congestion and path length (global optimization) • in the large, individual agent migration decisions should cause load leveling at Service Places and traffic distribution without a central authority (emergent behavior)

15. University of Missouri - Kansas City Proposed AutoPilot Architecture AutoPilot Network Router Agent Service Place Service Planner Topologist Trader Place Trader

16. University of Missouri - Kansas City Research Implications:Distributed Artificial Intelligence - there is an Artificial Intelligence Sub Problem - there is a Distributed Processing sub problem - there is a Network sub problem

17. University of Missouri - Kansas City Artificial Intelligence Sub Problem • Given a set of agent preferences for time to service, cost of service and quality of service, select the most desirable location from a set of possible locations that conform to the agent’s preferences This is a multi-attribute programming problem

18. University of Missouri - Kansas City Distributed Processing Sub Problem • Given a set of Service Places and the service set of each Service Place, find an optimal assignment of Services to the Service Places subject to the Service Place environments This is a multi-processor task assignment problem

19. University of Missouri - Kansas City Network Sub Problem • Given a sequence of Services specified by the agent’s work flow signature and a set of feasible Service Places, construct a optimal itinerary that minimizes total trip time This is a graph theory problem (trip planning)

20. University of Missouri - Kansas City Strategy • Solve academic problems in a manner that produces engineering solutions as well as new knowledge • Select solution techniques that integrate the three classes of sub problems

21. University of Missouri - Kansas City Research Approach: Composition not Decomposition • 1. Obtain a solution for the assignment of Services to Service Places • 2. Obtain a solution to for agent's attribute based perception of Service Places • 3. Integrate the results from (1) and (2) forming a mobility heuristic • 4. Validate the heuristics by simulating situated multi-agents in a network of Service Places. • 5. Formulate Trader Place Inquiry/Update Costs

22. University of Missouri - Kansas City Overview of Assignment of Services to Service Places • Each agent carries a work flow signature for the possible processing sequences of its task graph B D A E C Work Flow Signature = A;B;(C+D);E

23. University of Missouri - Kansas City Assignment of Services continued • Build an interior graph of the signature. Weight interior edges with the payload size from taskI to taskJ 2.8 1.0 B D 3.6 A .8 E C 1.1 Input is initial agent payload and a scaling matrix

24. University of Missouri - Kansas City Assignment of Services continued • Connect each interior Service node to every Service Place supporting that service subject to the agent’s preference criteria • Weight the edges from the Services to the Service Places by agent preference for the Service Place

25. University of Missouri - Kansas City Assignmentof Services continued

26. University ofMissouri- Kansas City Assignment of Services continued Weighting of edges from Services to Service Places

27. University of Missouri - Kansas City Assignment of Services continued Wb,sp1 SP2 Wd,sp2 SP1 Cb,d Cd,e Wa,sp1 D B Ca,b Cc,e E Cb,c A C Wc,sp3 We,sp3 Wa,sp3 SP3

28. University of Missouri - Kansas City Assignment of Services continued Wb,sp1 SP1 Wd,sp2 Cb,d Wa,sp1 D Cd,e B Ca,b SP2 Cc,e We,sp3 E Cb,c A Not SP1 C SP3 Wc,sp3 Wa,sp3 Find a minimum cut to the network - Services A,B,C are assigned to SP1

29. University of Missouri - Kansas City Assignment of Services continued Final Service Assignments SP1:= A,B,C SP2 := D SP3 := E Wd,sp2 SP2 D Cd,e We,sp3 E SP3 Re-compute weights, find a new minimum cut, D is assigned to SP2, E is assigned to SP3

30. University of Missouri - Kansas City Improving Performance • We do not want to consider 10K Service Places for each agent • Observations • several locations may be equivalent by agent perception of time to service, cost of service and quality of service • if we could pick the Service Places to consider in the right order, we should assign all services in a relatively few iterations

31. University of Missouri - Kansas City Improving Performance continued • Leads to the multi attribute programming problem • An agent perceives each Service Place by its attributes (time, cost,quality) • If the agent could rank the Service Places by these attributes, we could generate equivalence classes of Service Places

32. University of Missouri - Kansas City Improving Performance continued Equivalence By Time Equivalence By Cost Equivalence By Quality Sp3 SP5 SP4 SP2 SP1 SP5 SP3 SP2 SP1 SP4 SP3 SP2 SP1 SP4 SP5 SP3 is an non-dominated Service Place in intersection of the first equivalence class for each attribute. Have the graph algorithm consider SP3 first.

33. University of Missouri - Kansas City Multi-Attribute Programming Problem • We cannot use a linear weighting scheme to rank nodes because time, cost and quality do not normalize • an agent’s constant perception of its environment is time • the Topologist can provide the Service Planner the current geodasic to a Service Place (router interface)

34. University of Missouri - Kansas City Multi-Attribute Programming continued • Humans distort time by attributes • long car ride for a bargain is viewed as acceptable to some limit of time • a one hour poor presentation is long • a two hour great movie is short • Why not let the agent distort time by the attributes of Service Places? • Need an objective function

35. University of Missouri - Kansas City Functions of Cost and Quality on Time

36. University of Missouri - Kansas City Quantifying agent perception In AutoPilot we limit max_distortion to twice the diameter of the network so an agent perceives the time to initiate service from nearly zero to twice the network diameter depending on its perception of the Service Place.

37. University of Missouri - Kansas City Demonstrations of Research Results • Description of Base Cases presented • Results viewed by visual front end • single agent simulations • link speeds are negligible • heuristic search for service - equal preferences for time, cost, quality • migration by preference for time • migration by preference for cost

38. University of Missouri - Kansas City Demonstrations of Research Resultscontinued • Multi-agent simulation • colony of 100 agents • all services offered on all nodes • arrival rates to network are high relative to processing time • transmission times are negligible • hope to see second order network effects as emergent behavior

39. London New York 2 Los Angeles 1 3 Sydney

40. NetworkSecond Order Effects as a result of multi-agent interaction • Emergent Colony Behavior • Under network loading individual agent decisions aggregate to Service Place load-leveling in the absence of any central network or Trader authority. CPU Utilization Legend Service Place Queue Length Service Place Agent Age Expected Behavior Desired Behavior

41. Accomplishments • 1. Obtain a solution for the assignment of Services to Service Places Complete • 2. Obtain a solution to for agent's attribute based perception of Service Places Complete • 3. Integrate the results from (1) and (2) forming a mobility heuristic Complete • 4. Validate the heuristics by simulating situated multi-agents in a network of Service Places. • Partially Complete, base cases only, not fully debugged • 5. Formulate Trader Place Inquiry/Update Costs • Equations presented in year end report

42. University of Missouri - Kansas City Proposed Research ActivitiesYear 2 • Focus on remaining Sponsor questions: • 1(a). Decentralized Scaleable Trader • How do you maintain global information about a set of available services without a central point of failure? • 2. Agent Health Monitor • Investigate ways to monitor large numbers of mobile/distributed agents with minimal effect on overall systems performance subject to the Trader cost formula from Year 1 results.

43. University of Missouri - Kansas City Proposed Research Activities Year 2 continued • generalize the multi-attribute function for n attributes • fully debug the simulator and extend to accommodate a definition of the Sponsor’s network (links/ number nodes) • extend the simulator to include Trader Place update policies • (interval, random..)

44. University of Missouri - Kansas City Proposed Research Activities Year 2 continued • study the relationship between emergent behavior and the Trader Place update policy • improve visualization post processor • in first half year produce two journal papers on agent mobility • multi attribute programming solution • general formulation of agent mobility

45. University of Missouri - Kansas City Proposed Research Activities Year 2 continued • study the applicability of Artificial Life principles to agent mobility in large colonies.

46. University of Missouri - Kansas City Current Status • Summary of progress against proposal • On-track in pursuing Sponsor questions with respect to research activities • Behind in debugging the simulator - not ready for delivery yet