CAP6671 Intelligent Systems

CAP6671 Intelligent Systems

Lecture 2:Multiagent Systems

Instructor: Dr. Gita SukthankarEmail: [email protected]

Schedule: T & Th 9:00-10:15amLocation: HEC 302

Office Hours (in HEC 232):T & Th 10:30am-12

2CAP6671: Dr. Gita Sukthankar

Homework

Reading: D. Nau, Current Trends in Automated Planning, AI Magazine (posted on web site once I scan it in)


What is an agent?


What is an agent?

MAS: “loosely coupled network of problem solvers that interact to solve problems that are beyond the individual capabilities or knowledge of each problem solver”Belief-Desire-Intention model of agency:

SituatedGoal-directedReactiveSocial

Framework for constructing BDI agents: Procedural Reasoning System


Deliberative vs. Reactive

What is the difference?


Deliberative vs. Reactive

Deliberative:Attempt to attain a set of goalsThis paper only really talks about deliberative agents.

Each individual agent is an intelligent system.

Reactive: Respond to the current environment using a stimulus/response paradigmAgents are individually dumb and “intelligence” is an emergent property of the way that the system is connected.


RETSINA Agent

KQML: knowledge query machine language


Multi-Agent Organization

User 1 User 2 User u

InfoSource 1Info

Source 1

Interface Agent 1Interface Agent 1 Interface Agent 2Interface Agent 2 Interface Agent iInterface Agent i

Task Agent 1Task Agent 1 Task Agent 2Task Agent 2 Task Agent tTask Agent t

MiddleAgent 2MiddleAgent 2

Info Agent nInfo Agent n

InfoSource 2Info

Source 2

InfoSource mInfo

Source m

Goal and TaskSpecifications Results

SolutionsTasks

Info & ServiceRequests

Information IntegrationConflict Resolution Replies

Advertisements

Info Agent 1Info Agent 1

Queries

Answers

distributed adaptive collections of agents that help users by retrieving information, providing advice and anticipate user's information needs.


Matchmaker

MatchmakerRequester

Provider 1 Provider n

Request for service

Contact information of providers that match the

request Advertisementof capabilities

Delegation of service

Results of service request

Yellow-page system


MAS Coordination

Teamwork: Agents share a common set of goals and each contributes to the fulfillment of these goals through teamwork. No explicit modeling of individual agent utility

Coalitions: Agents seek to maximize individual utility and group utility (coalition stability is an issue)

Coordination: Agents pursue their individual goals and utilities; coordinationwith others is done only to avoid harmful interactions (e.g. traffic)

Negotiation: Agents seek to maximize their individual utility but are willingto compromise (i.e. better off if they reach agreement than not)

Game Theoretic Interactions: Agents seek to maximize individual utility while taking into consideration other’s options

Adversarial Interactions/Zero Sum Games: Agents seek to maximize own utility while minimizing utility of opponent


Characteristics of an MAS

Incomplete informationNo global controlData is decentralizedComputation is asynchronous


Why use an MAS?


Why use an MAS?

Solve problems too huge for one agentInteroperation of existing legacy systemsPreserve privacy of user dataUse spatially separated information sourcesDistributed expertise


Aircraft-serviceAir-Force base 3

Hangar computer

Aircraft Maintenance


Hangar computer

Formagent


Hangar computer

Historyagent

Historyagent

Historyagent

Formagent

Form202A

202DB

202DB

202DB

Manuals ManualsManuals

Manualsagent

Manualsagent

Manualsagent

Requestrelevant historicalrepairs

Requestrelevant manualpages

Engineer A’s workstation

Engineer B’s workstation

Requestrelevant historicalrepairs

Form202B

Form202A

Sent bymechanic

Sent bymechanic

Form202B

Sent to mechanic

To mechanic


Aircraft Maintenance

Shehory, Sukthankar, and Sycara 1998Why use an MAS?

User interface agents needed to be lightweight and run on either a tablet computer or desktopUtilize legacy systems that the military already had in place


Agent-based Programming

Does it improve system performance?


Claims

Computational efficiencyReliabilityExtensibilityRobustnessMaintainabilityResponsivenessFlexibilityReuseDoes agent-based programming really offer all those advantages?


Examples of MAS Applications

Sensor netsSmart buildingsShopping bots/financial agentsInformation-gatheringManufacturingPersonal information management agents

Calendar scheduling

Air traffic controlVirtual humans/embodied conversational agents


Information Fusion with MAS

Information Information AgentAgent

Middle Middle AgentAgent

Logistics Logistics (Task) (Task) AgentAgent

Interface Interface AgentAgent

Fusion Fusion (Task) (Task) AgentAgent















Logistics Logistics (Task) (Task) AgentAgent

Interface Interface AgentAgent

Fusion Fusion (Task) (Task) AgentAgent

Customized Views for

Different Roles

Fused Data from different

Battlefield regions

Reconnaissance Data

Multilevel fused intelligence reports


Research Issues

Task allocationCNP, auction-based frameworks

Multiagent planning: resolving sub-gal interactions

Decentralized MDP, PGP (partial global planning)Teamwork

Modeling other agentsPlan recognition

Resource/Communication limitationsDistributed constraint optimization

Adaptation and learningMulti-agent RL


What remains unsolved?

When this article was written:Lack of systematic methodologyIndustrial-strength MAS toolkits

Now:Larger-scale systemsHuman-agent interfacesGlobal interoperabilityDynamic planning/problem-solvingLifelong learning


Vision: 10 Years Out

•Memory augmentation •Personalized services•Nano-robots/smart matter•Self-updating media (content and context).•Ambient anytime anywhere information access (safe mobile info environments for people)•Highly realistic agents that inhabit large virtual environments

Documents

CAP6671 Intelligent Systems