1 / 35

Model-Integrated Computing and Autonomous Negotiating Teams for Autonomic Logistics

Model-Integrated Computing and Autonomous Negotiating Teams for Autonomic Logistics. Gabor Karsai, Benoit Dawant Institute for Software-Integrated Systems, Vanderbilt University Jon Doyle, Bob Laddaga,Russ Currer LCS/MIT George Bloor,Joan Crunk, Rick Wong Boeing Phantom Works.

juliet-joseph
Download Presentation

Model-Integrated Computing and Autonomous Negotiating Teams for Autonomic Logistics

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Model-Integrated Computingand Autonomous Negotiating Teams for Autonomic Logistics Gabor Karsai, Benoit Dawant Institute for Software-Integrated Systems, Vanderbilt University Jon Doyle, Bob Laddaga,Russ Currer LCS/MIT George Bloor,Joan Crunk, Rick Wong Boeing Phantom Works

  2. Vision: Autonomic Logisticswith Legacy Systems • Maintenance and supply systemwherein change in the health of aircraft triggers the logistics system to • Identify, locate, gather, and schedule parts, equipment, and technical personnel • Maintain stocks • Perform data analysis and provide feedback to manufacturers • Resolve conflicts and allocate scarce resources • Constraint: Utilization of existing legacy systems • Potential application: CACE,JSF

  3. Evt 02-1/2 05 11 14 01 08 Evt 05-1/2 Evt 03-1/2 Evt 04-1/2 I-Level Repair Off Aircraft Req’d 14 03 14 03 11 • Supply Status: Req • Mech Available? • Time for R&R? 03 • Radar Altimeter Inop • Supply Status 1 Hour Evt 01-1/2 EVT 02-2 13:00 - 14:20 Capt Evans 1 CA-9, 1 Tatcs PMC/Ctr Line Sta 33 29 06 10 02 14 READY 09 07 16 05 +24hrs Today 14:15 +48hrs 15 +72hrs +96hrs

  4. MC/FMC 48/42 MC/FMC 78/70 MC/FMC 92/88 MC/FMC 75/65 VMA-214 VMA-211 VMA-513 VMA-311 • One GTS/APU on hangar deck FOM on AC undergoing Phase Inspection (36 hrs) • Squadrons agree to transfer good unit and reassign supply request to AC in phase • One unit on test bench AWP for a low usage diode • ETR higher level unit: 20 days • Authorization for local purchase • ETR: 5 hours • Total time till RFI: 24hours MAG HQTRS Aviation Logistics Squadron

  5. The MICANTS Solution • A prototype Autonomic Logistics (AL) system using negotiation technology to allocate resources • Components: • Agent-based environment for building AL systems • Negotiation algorithms and technology • System modeling and integration technology

  6. MICANTS Concept MIPS Environment Models of apps, agents, etc. Negotiating a globally beneficial solution Models Model Int. “Agent Space” Adapter Adapter Adapter Adapter Logistics App/Dbase (Legacy) Logistics App/Dbase (Legacy) Logistics App/Dbase (Legacy) Logistics App/Dbase (Legacy)

  7. ISIS Effort Develop the Model-Integrated support tools for building the prototype systems Provide a testbed for trying out novel negotiating algorithms and techniques Realize demonstration scenarios

  8. Status • Agent framework package selected: Zeus (BT) • MIC tool development efforts: • Ontology modeling environment • Interaction Protocol modeling environment • Generators for synthesizing Java code (for Zeus) • External database adapter (MS-Access,ODBC) • Demonstration scenario and implementation

  9. Ontology Modeling • In Zeus: ONTOLOGY = SCHEMA • Agents share ontologies, but not all agents need all ontologies • Solution: • Global ontology models • Agent-specific ontologies

  10. Ontology Model Example

  11. Interaction Protocol Modeling • Interaction Protocol • Sequencing of messages that constitute the negotiation process • Approach • Multiple finite-state machines with coupled send-receive pairs and exceptions • Usage • Java code is synthesized that is executed under Zeus

  12. Interaction Protocol Model Example

  13. Boeing Effort Demonstration scenario

  14. Boeing Effort • Model and Simulate the Decision Support Processes of the Marine Aircraft Group at Yuma • Identify the utility of the MICANT technology to these decision support processes • Map the MICANT negotiation technology onto these decision support processes

  15. Status • A Customer Has Been Identified. • Marine Aircraft Group - 13 Yuma, Arizona • The Domain Requirements Capture Process Has Been Selected. • Model and Simulate “What if engine” • The Modeling Environment Has Been Selected • GRADE • The Modeling and Simulation Team Has Been Formed

  16. MIT Effort MICANTS Negotiation Approach

  17. Key Concepts • Structured change of negotiation methods • Plans and strategies • Goals, preferences, and utilities • Beliefs and arguments • Dynamic organization of negotiating parties

  18. Dynamic Negotiation Strategies • Plans specify structure of complex negotiations • Sequential and conditional orderings • Concurrent component activities • Differential diagnosis and effects of situational changes • Compose complex strategies from elemental methods

  19. Sample Elemental Strategies • Unpressured optimization • Seek best deal according to goal criteria • Sequential unpressured optimization • Order search by participant proximity groups • Panic mode • Seek quickest deal, ignoring cost • Sequential panic mode • Shape panic offers by relation to participants

  20. Autonomic Logistics Example • Start with sequential unpressured optimization • Ask sister squadrons, then service reserves, then standard suppliers, then untested suppliers • Concurrently monitor rate of progress against deadlines and expectations about negotiation characteristics • Transfer to sequential panic mode strategy when deadline nears • Make sister squadrons best offer first, pleading desperation • Use exponential bidding strategy for outside suppliers

  21. Strategies and Goals • Different strategies reflect different goals • Minimizing time, personnel, facility usage, dollar cost • Maximizing flexibility, robustness, readiness • Goals concern different agents • Narrow self-interest, group interest • Group interest • Shoring up weakest members • Build up strongest members • Sacrifice self to group goals

  22. Dynamic Negotiation Goals • Strategic progression changes goals • “Exiting information-gathering stage, entering hard-bargaining stage, abandon information goals in favor of cost-minimization goals” • Changing situation changes goals, then strategy • “Cost minimization is taking too long, give it up in favor of finishing quickly” • “People aren’t taking our offers, let’s change our cost goals” • “HQ cut our budget again, let’s economize” • “HQ changed our mission, let’s change our subgoals”

  23. Dynamic Negotiation Preferences • Invention of preferences to cover new situations • Bartering odd combinations of parts • Comparing readiness for novel missions • Toughening or liberalizing position • Strengthen or weaken thresholds • Add or remove factors from evaluation criteria

  24. Elementary Strategic Components • For group decisions • Contract nets • Market-clearing auctions • For individual decisions • Expected utility calculations • Reasoned deliberation

  25. Reasoned Negotiation and Deliberation • Formulate or construct goals and preferences through strategy-sensitive reasoning • Finding reasons for and against options • Finding reasons undercutting or buttressing other arguments • If utility representations required for efficiency, construct them from the resulting goals and preferences

  26. Dynamic Negotiation Organization • Relation of agent to others depends on strategy, situation, and history • Construct “proximity groups” along different relational dimensions • Shared or distinct missions • Known or unknown quantity in negotiation history • Authority, reliability, etc. • Structure strategies to exploit these proximity groups

  27. Dynamic Organization Examples • Deal with sister squadrons, sister groups, known suppliers, unknown suppliers, etc. • Resorting to unknown suppliers adds someone to known suppliers • Consortia among suppliers eventuate standard points of contact

  28. Theoretical Lessons • Arrow impossibility theorem says any method will break down sometimes, unless backed up by “dictatorial” fall-back policy • Market auctions produce optimal deals in ideal circumstances rare in practice

  29. Practical Expectations • Market auction approximations quickly produce reasonable feasibility estimates that can effectively guide • Progress through negotiation plans • Revision of negotiation goals and preferences • Differential diagnosis between alternative negotiation plans

  30. Demonstration scenarios

  31. Structure • MSA: Maintenance Supervisor Agent • RAA: Resource Allocator Agent • PMA: Parts Manager Agent • ESA : External Supplier Agent Website containing AVI files of demo

  32. Scenario 1 Hierarchical search for suppliers • Sequential unpressured optimization • Round 1 with known suppliers • PMA_x (squadron) and ESA-1 (trusted supplier) • Round 2 (if time is available) • ESA-2 (new supplier)

  33. Scenario 2 Changing organizational structure • ESA-2 has oversupply of parts: it lowers price • RAA monitors the deal and decides to promote ESA-2 to preferred supplier status

  34. Scenario 3 Switching strategy function • ESA-1 is delayed in responses • RAA switches strategy function during the negotiation • Speeds up the negotiation process but result is less optimal

  35. Plans • Refine scenarios with MIT, Boeing, and CACE • Technology issues • Enhance interaction protocol modeling • Finish modeling environment for legacy database interfacing • Investigate other agent frameworks/techniques • Demonstration and evaluation

More Related