1. MICANTS Model-Integrated Computing and Autonomous Negotiating Teams for Autonomic Logistics Gabor Karsai (Vanderbilt/ISIS)

2. CACE/MICANTS [08/04/00] Roles Vanderbilt/ISIS MIC, implementation, and demonstration MIT Algorithms, scenarios Boeing Scenarios, modeling, domain knowledge http://www.isis.vanderbilt.edu/Projects/micants/micants.htm

3. CACE/MICANTS [08/04/00] Autonomous Negotiation Teams Program Goal The goal of ANTs is to autonomously negotiate the assignment and customization of resources, such as weapons, to tasks, such as moving targets. Applications include: logistics, dynamic planning, and reactive weapon control. Key Milestones 1.Negotiation experiment, determine real-time capability 2.Logistics demonstration 3.Electronic Countermeasures Demonstration ANT Technology Reasoning based Negotiation Real-time response Convergent solution methods Handling, expressing uncertainty Peer-to-peer and bottom-up organization Discovery of peers, tasks and roles Integrating access, authorization technology Contribute to plan and task coordination at higher levels 1:4Q00 2:1Q01 3:4Q03

4. CACE/MICANTS [08/04/00] MICANTS Research Goals UseUse 1.Model-Integrated Computing, and 2.Agent/Negotiation technology to solve complex resource management problems in (autonomic) logistics to solve complex resource management problems in (autonomic) logistics To create technology to help demonstrate the feasibility of the above.To create technology to help demonstrate the feasibility of the above. Software/Systems Engineering Technology Technology for Distributed Problem-solving

5. CACE/MICANTS [08/04/00] Background Model-Integrated Computing Software Synthesis Generation Domain-specific Modeling Environment End-userProgrammability Domain-specific Application Examples: Intelligent Test Integration System (AEDC)Intelligent Test Integration System (AEDC) Saturn Site Production Flow (GM/Saturn)Saturn Site Production Flow (GM/Saturn) Engine test vibration monitoring System (AEDC)Engine test vibration monitoring System (AEDC)

6. CACE/MICANTS [08/04/00] Background Agents/Negotiation Technology Constraints manages Constraints manages CONFLICT negotiation Mutually acceptable, Negotiated solution satisfies “Good enough solutions/soon enough”

7. CACE/MICANTS [08/04/00] MIC for ANTS Support for negotiation protocols Source of complexity: Coordinating agent behavior with the negotiation protocol(s) The problem: Complex agents that participate in multiple, simultaneous negotiations are hard to write The MIC solution: Model and analyze negotiation protocols Generator Synthesize code for negotiation engine Coordination Engine Negotiating Agent Status: working prototype is in daily use on the project

8. CACE/MICANTS [08/04/00] MIC for ANTS Support for legacy system integration The problem: Negotiating agents have to access legacy databases,writing access code is tedious and error-prone. The MIC solution: Model legacy database schema and agent ontology Generator Synthesize code for agent database interface Database Interface Negotiating Agent Source of complexity: Coordination of the agent’s data model With legacy database’s schema Legacy DBase Legacy DBase Status: modeling environment prototype is built. Note: This approach is beneficial for systems without a data warehouse.

9. CACE/MICANTS [08/04/00] Negotiation technology-1 Key concepts Structured change of negotiation methods Plans and strategies Goals, preferences, and utilities Beliefs and arguments Dynamic organization of negotiating parties 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

10. CACE/MICANTS [08/04/00] Negotiation technology-2 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 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”

11. CACE/MICANTS [08/04/00] Negotiation technology-3 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 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

12. CACE/MICANTS [08/04/00] Negotiation technology-4 Addressing timeliness issues Monitor situation and progress If needed, modify negotiation process Negotiating agent Messaging Coordination Engine Monitoring Evaluation Reconfigurator Reconfiguration Technology for achieving time-bounded results Flexible negotiation plans with monitored execution and reconfiguration Negotiation via distributed constraint- satisfaction:  Fast methods for evaluating complex decision functions  Anytime strategies -- incremental, reactive  Problem decomposition/solving/ and solution integration

13. CACE/MICANTS [08/04/00] Demonstration domain Maintenance logistics (simplified) discrepancy report MMCO Flight Schedule Shop Maintenance Schedule Assign mechanic negotiate W/C OIC Current practice:Manual process

14. CACE/MICANTS [08/04/00] Demonstration domain Maintenance logistics (simplified) discrepancy report MMCO Flight Schedule Shop Maintenance Schedule Assign mechanic negotiate W/C OIC Goal:Assisted process options approve report options approve negotiate options approve Autonomic response

15. CACE/MICANTS [08/04/00] Challenges “Situational awareness” Recognizing non-trivial opportunities for changes to improve operations “Reactive and incremental” scheduling Incremental changes in the schedule triggered by situations “Negotiated” scheduling Stakeholders negotiate over scheduling decisions

16. CACE/MICANTS [08/04/00] Implementation issues Scheduling and negotiation as CSP Negotiating agent Messaging Coordination Engine Data structures representing domain constraints Constraint SAT mapper (encoding) Standard SAT Problem Solver (Tableau,WSAT,ISAMP) Standard SAT Problem Solver (Tableau,WSAT,ISAMP) Explicit management of constraints during negotiation “High-performance” encoding techniques Domain-independent SAT techniques Standard SAT Interface (CNF, etc.) Schedule

17. CACE/MICANTS [08/04/00] First Experiments MSA: Maintenance Supervisor Agent RAA: Resource Allocator Agent PMA: Parts Manager Agent ESA : External Supplier Agent

18. CACE/MICANTS [08/04/00] First experiments 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) Changing organizational structure ESA-1 is delayed in responses RAA switches strategy function during the negotiation Speeds up the negotiation process but result is less optimal Switching preferences ESA-2 has oversupply of parts: it lowers price RAA monitors the deal and decides to promote ESA-2 to preferred supplier status

19. CACE/MICANTS [08/04/00] Plans Technology: Light-weight agents Scheduling and negotiation as DCSP Demonstration: Domain scenario: “A day in the life of VMA-311” Further application scenarios Cooperation: Communication with ISI’s flight scheduling agents