Intelligent Agents - Lecture 3

Katia Sycara

The Robotics Institute

katia@cs.cmu.edu

www.cs.cmu.edu/~softagents

Course Policies• The course is based on lectures, lecture notes, and

additional materials provided either electronically or in hard copy

• There will be no exams. Instead:• Grading will be based on two projects

– mid-term project (40%):• a survey on a class-related topic

• development of an agent

• business case for agent technology in an area

– bigger final project (60%)

Example Projects:A. Projects involving programming

Write part of a Retsina information agent. The other, standard parts will be provided.NOTE: requires moderate knowledge and programming experience in PERL, or Webl or JAVA)

Implement agents that participate in an auction (e.g. sealed bid first price auction)NOTE: requires advanced programming skills in JAVA or C++

Given the Retsina matchmaker code, implement other types of middle agents.NOTE: requires advanced programming skills in JAVA.

B. Example Midterm Projects not involving programming

Comparative review of auctions (pros and cons)

Comparative survey of advanced e-commerce sites (i.e. not just simply listing a product or set of products)

Comparative survey of software tools for building agents, and what is missing.

Value added uses of agents in e-commerce.

Comparative survey of shopbots, issues raised by shopbots.

How would commerce change if everyone had personal agents available? Which types of personal agents would be useful?

Models of marketing on the Web, and use of agents in this endeavor.

Identify and document uses of agent technology in at least 25 top sites. Discuss trends. File sharing programs (e.g. gnutella)

Agent Architectures

Reactive architecturesDeliberative architecturesLayered architectures

• Belief, Desire, Intention (BDI)

• Concurrent architecture (RETSINA)

Concurrent Architectures(RETSINA: Sycara & al.)

• Include multiple functional and knowledge modules that work concurrently

• Coherence between the functional modules is achieved via shared databases

• Typical functional separation:– communication and collaboration– planning and reasoning– action scheduling – execution and monitoring

Example: RETSINA Agent Architecture

Functional Components

• Communicator: handles incoming and outgoing messages in an ACL. Converts requests into goals/objectives

• Planner: takes objectives and devises detailed plans to achieve them. Creates tasks, actions and new objectives. Uses plan fragments from libraries

• Scheduler: schedules actions for execution• Execution monitor: executes actions and monitors• Coordination/collaboration: reasons for such activities,

may be internal to planner or to communicator• Self-awareness: maintains self model: load, state, etc

Knowledge Components• Objective DB: holds the agent’s objectives

• Task DB: holds the agent’s tasks and actions, before they are scheduled for execution

• Schedule: holds scheduled actions

• Task reduction library: includes a set of possible task decompositions

• Task schema library: includes plan fragments, each provides details on how to perform a task

• Beliefs DB: holds the beliefs of the agent regarding information relevant to its activity

Architecture Attributes• Functional components do not directly

interface or synchronize with each other

• Knowledge components do not directly interface or synchronize with each other

• Functional components work concurrently

• These provide: – reusability and substitutability of components– efficient utilization of computational resources– timely task performance– reduced development effort

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Planning

• By incremental instantiation of plan fragments

• Conditional planning mechanisms

• Interleaving planning, information gathering, and execution

• Declarative description of information flow and control flow requirements

Planning by Reduction

T

T1 T2

Reduce T1

Reduce T2

T

T1 T2

A

A

A1 A2

Task decomposition

Multi-Agent Organization

• Centrally designed/closed organization

• Open organization: RETSINA

• Flat

• Modular

• Hierarchical

• Heterarchical

• Subsumed/compositional

RETSINA - REusable Task-based System of Intelligent Network Agents

• An adaptive, self-organizing collection of autonomous agents that interact with humans and each other:– integrate decision support and information management

– anticipate and satisfy human information processing and problem solving needs

– perform real-time synchronization of domain activities

– notify users and each other about significant changes in the environment

– adapt to user, task and situation

• A distributed collection of intelligent agents

• Agents collaborative and asynchronous

• Dynamic, open organization– agents may enter and leave at any time– heterogeneous agent capabilities– alternating user goals and preferences– dynamic information environment

RETSINA: System Attributes

8

Agent Types:

• Interface Agents

• Task Agents

• Information Agents

• Middle Agents

• All share same basic architecture

The RETSINA Multi-Agent Organization

User 1 User 2 User u

InfoSource 1

InfoSource 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 2

InfoSource 2

InfoSource m

InfoSource m

Goal and TaskSpecifications Results

SolutionsTasks

Info & ServiceRequests

Information IntegrationConflict Resolution Replies

Advertisements

Info Agent 1Info Agent 1

Queries

Answers

distributed adaptive collections of information agents that coordinate to retrieve, filter and fuseinformation relevant to the user, task and situation, as well as anticipate user's information needs.

RETSINA: Design Goals

• The agents and the system should:– gather information from distributed, multi-modal

sources, provide decision support– access, filter, integrate info. and make decisions– run across different machines– use models of users, tasks, agents to deal with tasks

(decompose, pass, etc)– allow code reusability and modularity– be flexible and robust

Locating Other Agents

• Open, dynamic MAS - agents appear and disappear

• Agents must be able to locate others

• Broadcast on WWW - inappropriate

• Solution - middle agents - advertising

• Remaining problem - whom to choose to maximize system performance

Middle Agents• Specialize in making connection between agents• Store, maintain and provide connection

information• Provide translation and interoperation services• Some middle agent types:

– Matchmakers match advertised capabilities

– Blackboard agents collect requests

– Broker agents process both

Brokering:

Requester

Provider

BrokerRequest-for-Service

Advertise/Unadvertise-Services

Reply-Result-of-Service

Request-for-Service

Requester

Matchmaking:

Provider

MatchmakerRequest-for-Service

Advertise/Unadvertise-ServicesRequest-for-Service

Reply-Provider-Agents-Names

Reply-Result-of-Service

Service Brokering vs. Matchmaking in Multi-Agent Systems

Locating Agents

• Agents advertise capabilities to middle agents

• Use functionally-specific vocabulary

• Agents ask middle agents for services

• Middle agents finds providers, notify requesting agents (or broker the service)

• Requesting agents contact providers when contact info. is given

Advertisement• Agent contact information

• Service/capability description

• Ontology

• Service availability

• Cost

• Advertisement– agent’s interface to the society– expresses agent commitment

Some RETSINA Applications

• Team aiding in joint tactical planning (using a simulated battlefield)

• Agent-aided aircraft maintenance

• E-commerce in wholesale markets

• Robot teams for de-mining

• Team Rescue Scenario (NEO)

• Warren system for financial portfolio management

Joint Tactical Mission Scenario

04/18/23 IA, Lecture 3

Air-Force Aircraft Maintenance

• Aircraft (e.g. F-15) arrive at maintenance airbase

• Periodic maintenance must be completed in 60 days (varies for different aircraft types)

• Mechanics inspect aircraft. For a discrepancy:– if unknown to mechanic:

• consults: experienced mechanics, manuals.

– for all discrepancy:• a 202A form is filled (by hand). Sent to expert engineers• reply (202B) includes repair instructions, approval

– mechanic repairs and files a report (and the 202)

Aircraft-serviceAir-Force base 3

Hangar computer

RETSINA-based solution

Aircraft-serviceAir-Force base 2

Hangar computer

Formagent

Aircraft-serviceAir-Force base 1

Hangar computer

History

agentHistory

agent

History

agent

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

Warren

• Agent Collaboration in– Information Gathering– Information integration– Information filtering– One shot & Persistent queries– Decision Support– a rapidly changing environment

Warren (cont)

Services to user

• Decision support – Evaluation– Suggestion

• Intelligent anticipation– Models & suggests based on allocation– Monitors for news/events

Warren Demo

• Each user has a portfolio• When user wants to buy (or sell) a stock

– Stock price agent is invoked

• When a new stock is added to portfolio– Risk allocation critic is invoked

• User can– Monitor a stock

– Retrieve information about finances of company

Coalitions for E-Commerce• In wholesale markets, purchasing more of the same

product reduces price per unit• Sellers benefit from selling more and spending less on

marketing/distribution• Buyers usually do not need large quantities• Can form coalitions of buyers, but:

– difficult to find other buyers

– need agreement on profit and product distribution

• Solution is simpler via buyers’ agents:– find others, form coalitions, negotiate with sellers

The Virtual Market

Monitor

CoalitionMatcher

NegotiationMatcher

seller1

seller2Catalog

Coalitionbuyer2

buyer1

buyer3

De-mining Teams of Robots

• Multiple robots for de-mining:– sophisticated searchers find mines– inexpensive exploders detonate mines– coordinators support team behavior and interaction

with human operator

• Requires coordination, team behavior– how to form and maintain a team?– when a team member fails, what to do?– how to translate human requests to team activity?

Open MAS Demonstration• A MAS comprised of RETSINA agents, OAA

agents and agentified application are presented

• We demonstrate:– human/agent, agent/agent interaction– agents collaborate to solve a real-world problem– perform information gathering and filtering– MAS interoperability via middle agents– robustness/fault-tolerance of the system via

service/agent substitutability– automation of information-rich and time-critical

planning, decision making and execution monitoring

A NEO TIE• Non-combatant Evacuation Operation:

– requires information from multiple, multi-modal sources, may change dynamically, be unreliable

– requires time-critical (re-) planning under uncertainty– collaboration among distributed humans, machines– plan execution monitoring

• Agent Technology Integration Experiment:– demonstrating agent support for NEO activity by

• exploiting agent technology for information gathering, user interfacing and collaboration

• automating and speeding up critical planning and monitoring

– interoperation with legacy components (e.g. air lift planner such as CAMPS) for NEO

The NEO Problem Domain• Location: Kuwait city. Time: 2005• Iraq not a threat, reduced military presence in mideast• Congress in Kuwait city on ecology & oil conservation• Unrest:

– night explosion at the conference center - no casualties

– terrorist group issues threat against attendees, American, etc.

• US activity:– ambassador orders evacuations (smaller numbers)

– Air Mobility Commander plans large evacuation

– Joint Force Commander involved (plan, secure evacuation routes)

NEO Scenario• Ambassador, JFC discuss evacuation via RETSINA UI

Messenger (UIM) and OAA MMM• JFC’s UIM monitors content, requests relevant info • WebMate provides text news, Maestro - video• RETSINA flights agent begins providing schedules• OAA flights agent takes over when RETSINA’s fails• UIM receives flights’ schedule and displays• OAA weather agent begins providing weather• RETSINA weather agent takes over when OAA’s fails• RETSINA route planner plans evacuation to KWI

NEO Scenario (continued)

• OAA phone agent provides info. about roadblocks• Route planner re-plans to KWI airport• AMC rep. uses a UIM to request CAMPS to

provide airlift plan from KWI• RETSINA Visual Sensor Agent (VSA) reports an

explosion near KWI, displays it on MMM• JFC designates alternative abandoned airfield• Route planner re-plans to new destination• AMC via UIM requests a new plan from CAMPS• CAMPS returns a plan, presented by UIM

DemoDisplay Visualization Agent

NEO TIE: the contribution

• The NEO TIE– uses existing agents, MAS, services, legacy systems

– makes them inter-operate: resolve conflicts, build interoperability agents

– enhance human/MAS collaboration/interfacing

– integrate agent and legacy (re-) planning

– demonstrate robustness via agent substitutability

• The agents in the NEO TIE do not– take the critical decisions (but may advise)

– evacuate (but may plan for, monitor and display it)

1. Coherent communication of meaningful content between two heterogeneous MAS

2. Functional substitutability of agents

3. “Agentification” of legacy systems

4. Adaptation at different levels: interfaces, MAS organization, single agent

5. Collaboration:human-human, human-agent, MAS

Scientific and Technical Contribution

Lessons Learned?• Agents and MAS may be a promising technology

when:– the amount of information and pace of change are

overwhelming– users, information and resources are distributed– expertise and activity are distributed– there is no central control– there is a need for flexibility and robustness in face of

uncertainty and partial failures– incorporation of pre-existing systems is advantageous

Characteristics of RETSINA

• Open system / Organization robust & adaptive

• Service-based collaboration

• Agents are honest

• Agents “passively” volunteer

• Matchmaking preserves privacy

RETSINA - Conclusion

• The RETSINA multi-agent approach:– highly dynamic, open, robust– agents actively seek out information– specialized agents collaborate– interleaving planning and execution– reusable infrastructure and architecture– several domain specific implementations