MICANTS

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

2. 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. 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. 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 Key Milestones 1. Negotiation experiment, determine real-time capability 2. Logistics demonstration 3. Electronic Countermeasures Demonstration 1:4Q00 2:1Q01 3:4Q03

4. MICANTS Research Goals Software/Systems Engineering Technology • Use • Model-Integrated Computing, and • Agent/Negotiation technology to solve complex resource management problems in (autonomic) logistics • To create technology to help demonstrate the feasibility of the above. Technology for Distributed Problem-solving

5. BackgroundModel-Integrated Computing Domain-specific Modeling Environment End-user Programmability Software Synthesis Generation Examples: • Intelligent Test Integration System (AEDC) • Saturn Site Production Flow (GM/Saturn) • Engine test vibration monitoring System (AEDC) Domain-specific Application

6. Constraints Constraints BackgroundAgents/Negotiation Technology CONFLICT manages manages negotiation Mutually acceptable, Negotiated solution satisfies satisfies “Good enough solutions/soon enough”

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

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

9. 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. 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. 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. Negotiation technology-4 Addressing timeliness issues Negotiating agent Coordination Engine Messaging Monitor situation and progress If needed, modify negotiation process Monitoring Evaluation Reconfiguration Reconfigurator 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. Demonstration domainMaintenance logistics (simplified) Current practice:Manual process MMCO negotiate discrepancy report negotiate Assign mechanic negotiate W/C OIC Flight Schedule Shop Maintenance Schedule

14. MMCO options options options approve approve approve Demonstration domainMaintenance logistics (simplified) Goal:Assisted process negotiate report W/C OIC discrepancy report Assign mechanic negotiate Autonomic response negotiate Flight Schedule Shop Maintenance Schedule

15. 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. Implementation issuesScheduling and negotiation as CSP Negotiating agent Coordination Engine Messaging Explicit management of constraints during negotiation Data structures representing domain constraints “High-performance” encoding techniques Schedule Constraint SAT mapper (encoding) Domain-independent SAT techniques Standard SAT Interface (CNF, etc.) Standard SAT Problem Solver (Tableau,WSAT,ISAMP)

17. First Experiments • MSA: Maintenance Supervisor Agent • RAA: Resource Allocator Agent • PMA: Parts Manager Agent • ESA : External Supplier Agent

18. 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. 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