Software Agent -applications-. Outline Overview of agent applications Agent applications –Interface agents –IBM Aglets –AgentSpace –The Open Agent Architecture.
Post on 21-Dec-2015
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Outline
• Overview of agent applications
• Agent applications– Interface agents– IBM Aglets– AgentSpace– The Open Agent Architecture– Etc.
• Some cases– Massive– RETSINA
• Summary
• Discussion
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Overview of Agent Applications
• Where are agents used?– Robotics– On the web– Movie and game production– Scientific simulations– Defense applications– Distributed computing (e.g. XGrid)– Mobile applications
• Why are agents useful?– Software engineering.
• Modularity, abstraction, complexity, management, etc.– Match many problem domains– Good surrogates for humans– Cognitive and social models– Provide intelligent behavior
• Where artificial intelligence and software engineering meet
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Agent Applications
1. Automated Office2. Unified Messaging3. Multimodal Maps4. CommandTalk5. ATIS-Web6. Spoken Dialog Summarization7. Agent Development
Tools8. InfoBroker9. Rental Finder10. InfoWiz Kiosk11. Multi-Robot Control12. MVIEWS Video Tools13. MARVEL 14. SOLVIT15. Surgical Training16. Instant Collaboration17.Crisis Response18. WebGrader19. Speech Translation20-25+ ...
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Interface Agents
User
User'sAgent
OtherAgentAsking
Interacts with
Application
User feedback &programming by example
Observes &imitates
Communication
Interacts with
User
User'sAgent
OtherAgentAsking
Interacts with
Application
User feedback &programming by example
Observes &imitates
Communication
Interacts with
Agent Role Source
Letizia WWW guide Liebermann (1995)
Remembrance Agent memory aid Rhodes & Starner (1996)
NewT UseNet news filter Sheth & Maes (1993)
Yenta matchmaking andreferrals
Foner (1996)
Kasbah buy and sell items onthe WWW
Chavez & Maes (1996)
Ringo/HOMR entertainment selection Shardanand & Maes (1995)
Calender Apprentice(CAP)
schedule meetings Dent et al. (1992)
Agent Role Source
Letizia WWW guide Liebermann (1995)
Remembrance Agent memory aid Rhodes & Starner (1996)
NewT UseNet news filter Sheth & Maes (1993)
Yenta matchmaking andreferrals
Foner (1996)
Kasbah buy and sell items onthe WWW
Chavez & Maes (1996)
Ringo/HOMR entertainment selection Shardanand & Maes (1995)
Calender Apprentice(CAP)
schedule meetings Dent et al. (1992)
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IBM Aglets: Overview
• System description :– Aglets for Agile Applets are Java mobile objects– The Aglets architecture consists of two APIs and two implementation layers
• Aglet API – Aglets Runtime Layer - The implementation of Aglet API – Agent Transport and Communication Interface (ATCI with ATP as an application-
level protocol)– Transport layer
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IBM Aglets: Applications
• The Tabican software for finding a package tour or flight ticket
• Electronic commerce
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AgentSpace (Ichiro Sato)
• System description :– AgentSpace is a Java-based middleware
for distributed environments– It runs on the Windows (9X, NT), MacO8,
Solaris 2.5, Linux
• Language : The system and related agents are written in Java
• Agent mobility
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The Open Agent Architecture
• Agent System description:– OAA is a framework for integrating a community of heterogeneous software agents
in a distributed environment.
• Agents communication: multimodal cooperation and interactions
• OAA agent libraries exist for the following languages and platforms:
Quintus Prolog SunOs 4.1.3, Solaris 2.5+, Windows 95
ANSI C (Unix, Microsoft, Borland) SunOs 4.1.3, Solaris 2.5+, SGI IRIX, Windows 95
Common Lisp (Allegro & Lucid) SunOs 4.1.3, Solaris 2.5+
Java Any Java platform
Borland Delphi Windows 3.1, Windows 95
Visual Basic Windows 3.1, Windows 95
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The Open Agent Architecture: Applications (1)
Wizard Info.
Automated office
Multi robot control
Speech recognitionover the web
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Interfacing Agents
• Talking heads– Naturalistic figures– Many possible platforms:
• Web, mobile device, set-top box– Applications
• News reading, signing, interactive digital TV programme guide• Electronic Virtual Assistants in e-commerce
• Vandrea– Thin client solution, that reads news scripts live from ITN
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Microsoft Agent
• Components– Toolbox of technologies
• Speech rec, text-to-speech– Scripting language
• Controls animation at high-level• Good but rigid
– Pre-designed characters• Rather cutesy, but you can create your own
– Only viewable with Active X
• Microsoft Persona Project– The project is developing the technologies required to produce conversational as-
sistants - lifelike animated characters that interact with a user in a natural spoken dialogue
– The work is built upon the Whisper speaker-independent continuous speech recog-nition system and a broad coverage English understanding system, both also de-veloped at Microsoft Research
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Agents in Defense
• Used to represent human military operators
– Fighter Pilots (enemy and friendly), commanders, sensor operators etc
• Training
• Human factors research
• Military operations research
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Operations Research and Military Simulation
• Provide advice to the ADF– Influence $billion decisions– Impact on tactical decisions in actual operations
• Highly sophisticated systems
• Expensive/dangerous to operate
• Unknown/uncertain futures
• So we tend to work in virtual spaces – simulation– Purposes
• Tactics development and experimentation• Acquisitions
– Style• Heavyweight, BDI, usually less than 32• Not neural net, learning, or agent based distillation
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F/A-18 Hornet Tactics
• Needed to develop tactics for use with RAAF F/A-18 Hornets, espe-cially with new weapons
• Client was F/A-18 squadrons (esp. 2 OCU)
• 2 OCU – Hornet pilot training, and FCI Course– FCI = Fighter Combat Instructor
• FCI is Australian equivalent of TOP GUN• Contributed to Australian Hornet TACMAN
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RETSINA Agent Architecture
• Reusable Environment for Task-Structured Intelligent Networked Agents
• Four parallel threads:– Communicator for conversing with
other agents– Planner matches “sensory” input
and “beliefs” to possible plan actions– Scheduler schedules “enabled”
plans for execution– Execution Monitor executes sched-
uled plan & swaps-out plans for those with higher priorities http://www.cs.cmu.edu/~softagents/retsina.html
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RETSINA Functional Architecture
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
Middle Agent 2Middle Agent 2
Information Agent n
Information Agent n
InfoSource 2
InfoSource 2
InfoSource m
InfoSource m
Goal and TaskSpecifications Results
SolutionsTasks
Info & ServiceRequests
Information IntegrationConflict Resolution Replies
AdvertisementsInformation
Agent 1
Information Agent 1
Queries
Answers
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Agent Description
• Interface Agents– Solicit input from user for the agent system– Present output to the user– Frequently part of task agent– Often representative of a device
• Task Agents– Know what to do and how to do it– Responsible for task delegation– May enlist the help of other task agents
• Middle Agents– Infrastructure agents that aid in MAS scalability– Many have been identified in Sycara & Wong ‘00– Most common:
• Agent Name Service (White Pages)• Matchmaker (Yellow Pages)• Broker• MAS Interoperator
RETSINA
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Application: ModSAF
• Modular Semi- Automated Forces
• “Real world” events are simulated in Agent Storm by interaction with ModSAF
• minefield discovery• encountering Threat platoon• announcements of passed check-
points
• RETSINA Mission Agents control ModSAF platoon.
• route directions• marching orders
RETSINA
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Application: RETSINA De-mining SystemRETSINA
http://www.cs.cmu.edu/~softagents/demining.html
Without Team-Aware Coordination With Team-Aware Coordination
• Using simple homogenous strategy• Robots interfere with each other• Robots attempt to de-mine same mine
• Using simple homogenous strategy and rule that they cannot diffuse the same mine• Robots do not interfere with each other• A path is more rapidly cleared
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Application: MokSAFRETSINA
Alpha’s Shared Route
Charlie’s Shared Route
Information about shared routes…
Bravo’s Shared Route.
Note that this route initially support’s Charlie’s route, then crosses to intercept Alpha’s route.
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Application: PalmSAF
• Miniaturized form of MokSAF for hand-held computers
• Full RETSINA multi-agent system available to PalmSAF user
• Technical challenges:– little memory– very few communication ports– intermittent communication connections
RETSINA
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Summary
• (Etzioni & Weld, 1995) identify the following specific types of agent that are likely to appear soon:
– Tour guides: The idea here is to have agents that help to answer the question ‘where do I go next’ when browsing the WWW. Such agents can learn about the user’s preferences in the same way that MAXIMS does, and rather than just provid-ing a single, uniform type of hyperlink actually indicate the likely interest of a link.
– Indexing agents: Indexing agents will provide an extra layer of abstraction on top of the services provided by search/indexing agents such as LYCOS and InfoSeek. The idea is to use the raw information provided by such engines, together with knowledge of the users goals, preferences, etc., to provide a personalized service.
– FAQ-finders: The idea here is to direct users to FAQ documents in order to answer specific questions. Since FAQS tend to be knowledge intensive, structured docu-ments, there is a lot of potential for automated FAQ servers.
– Expertise finders: Suppose I want to know about people interested in temporal be-lief logics. Current WWW search tools would simply take the 3 words ‘temporal’, ‘belief’, ‘logic’, and search on them. This is not ideal: LYCOS has no model of what you mean by this search, or what you really want. Expertise finders ‘try to under-stand the users wants and the contents of information services’, in order to provide a better information provision service.
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Discussion on Apple’s Knowledge Navigator
• Hardware platform?
• Intelligent functions?
• Present vs. future?
• Proposal topics for discussion– 김용준 : 사용자의 명령을 인식하고 의도를 파악한 후에 실제로 그 의도를 어떤 식으로 수행하고 필요한 기술은 무엇일까
– 최봉환 : 사용자의 의도를 어떻게 인식하는가 – 이승현
• 사용자로부터의 불충분한 정보를 바탕으로 무언가를 어떻게 효율적으로 검색해서 사용자가 원하는 결과를 내주는가
• 웹 페이지 연결에 있어서 URL을 통한 것이 아니라 "학교 홈페이지 " 등 기능이나 이름을 통한 연결
– 노홍찬 : 어떻게 에이젼트가 사용자의 성향에 대해 학습할 수 있는가
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주요 기능
• 일정관리• 문서 검색 및 관리• 정보추천• 상황인식 (시각 /청각 )• 전화연결 /자동응답• 문맥관리• 대화기능 (음성인식 /대화관리 /음성합성 )• 아바타 관리
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입력 인간기능 통합모델 출력
행위
서비스
시각
청각
온도
습도
Etc.
사용자 질의 사용자 응답
학습판단
계획
추론
모델링
대화기능
행동기능상황인식
지식관리
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