MICANTSModel-Integrated Computing and Autonomous Negotiating Teamsfor Autonomic LogisticsGabor Karsai (Vanderbilt/ISIS)

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

CACE/MICANTS [08/04/00]

Autonomous Negotiation TeamsProgram 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

CACE/MICANTS [08/04/00]

MICANTS Research Goals

•UseUse1.1. Model-Integrated Computing,Model-Integrated Computing, and and 2.2. Agent/Negotiation technologyAgent/Negotiation technology

to solve complex resource to solve complex resource management problems in (autonomic) management problems in (autonomic) logisticslogistics

•To create technology to help To create technology to help demonstrate the feasibility of the demonstrate the feasibility of the above.above.

Software/Systems Engineering Technology

Technology for Distributed Problem-solving

CACE/MICANTS [08/04/00]

BackgroundModel-Integrated Computing

Software SynthesisGenerationDomain-specific

Modeling Environment

End-userEnd-userProgrammabilityProgrammability

Domain-specificApplication

Examples:Examples:•Intelligent Test Integration System Intelligent Test Integration System (AEDC)(AEDC)•Saturn Site Production Flow Saturn Site Production Flow (GM/Saturn)(GM/Saturn)•Engine test vibration monitoring Engine test vibration monitoring System (AEDC)System (AEDC)

CACE/MICANTS [08/04/00]

BackgroundAgents/Negotiation Technology

Constraints

manages

Constraints

manages

CONFLICT

negotiation

Mutually acceptable,Negotiated solution

satisfiessatisfies

“Good enough solutions/soon enough”

CACE/MICANTS [08/04/00]

MIC for ANTSSupport 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

GeneratorGeneratorSynthesize code for negotiation engine

CoordinationEngine

NegotiatingAgent

Status: working prototype is in daily use on the project

CACE/MICANTS [08/04/00]

MIC for ANTSSupport 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

GeneratorGeneratorSynthesize code for agent database interface

DatabaseInterface

NegotiatingAgentSource of complexity:

Coordination of the agent’s data modelWith legacy database’s schema

LegacyDBase Legacy

DBase

Status: modeling environment prototype is built.Note: This approach is beneficial for systems without a data warehouse.

CACE/MICANTS [08/04/00]

Negotiation technology-1Key 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

CACE/MICANTS [08/04/00]

Negotiation technology-2Strategies and Goals

Different strategies reflect different goalsMinimizing time, personnel, facility usage, dollar costMaximizing flexibility, robustness, readiness

Goals concern different agentsNarrow self-interest, group interestGroup interest:Shoring up weakest members,build up strongest members, sacrifice

self to group goals

Dynamic Negotiation GoalsStrategic 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”

CACE/MICANTS [08/04/00]

Negotiation technology-3Dynamic 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 OrganizationRelation of agent to others depends on strategy, situation, and historyConstruct “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

CACE/MICANTS [08/04/00]

Negotiation technology-4Addressing timeliness issues

Monitor situation and progress

If needed, modify negotiation process

Negotiating agent

Messa

gin

g

CoordinationEngine

MonitoringEvaluation

Reconfigurator

Reco

nfi

gu

rati

on

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

CACE/MICANTS [08/04/00]

Demonstration domainMaintenance logistics (simplified)

discrepancy report

MMCO

Flight Schedule

Shop Maintenance

Schedule

Assign mechanicnegotiate

negotiate

negotiate

W/C OIC

Current practice:Manual process

CACE/MICANTS [08/04/00]

Demonstration domainMaintenance logistics (simplified)

discrepancy report

MMCO

Flight Schedule

Shop Maintenance

Schedule

Assign mechanic

negotiate

negotiate

W/C OIC

Goal:Assisted process

optio

ns

appro

ve

repo

rt

optio

ns

appro

ve

negotiate

optio

ns

appro

ve

Autonomic response

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

CACE/MICANTS [08/04/00]

Implementation issuesScheduling and negotiation as CSP

Negotiating agent

Messa

gin

g

CoordinationEngine

Data structures representing

domain constraints

ConstraintSAT

mapper (encoding)

Standard SATProblem Solver

(Tableau,WSAT,ISAMP)

Standard SATProblem Solver

(Tableau,WSAT,ISAMP)

Explicit management of

constraints during negotiation

“High-performance”encoding techniques

Domain-independent

SAT techniques

Standard SAT Interface (CNF, etc.)

Schedule

CACE/MICANTS [08/04/00]

First Experiments

•MSA: Maintenance Supervisor Agent•RAA: Resource Allocator Agent•PMA: Parts Manager Agent•ESA : External Supplier Agent

CACE/MICANTS [08/04/00]

First experimentsHierarchical 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

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