Software Agent
-applications-
Outline
• Overview of agent applications
• Agent applications
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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?
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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.
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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
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Automated Office
Unified Messaging
Multimodal Maps
CommandTalk
ATIS-Web
Spoken Dialog
Summarization
7. Agent Development
Tools
8. InfoBroker
9. Rental Finder
10. InfoWiz Kiosk
11. Multi-Robot Control
12. MVIEWS Video Tools
13. MARVEL
14. SOLVIT
15. Surgical Training
16. Instant Collaboration
17.Crisis Response
18. WebGrader
19. Speech Translation
20-25+ ...
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Interface Agents
User
Interacts with
Communication
User feedback &
programming by example
Application
Interacts with
Observes &
imitates
User's
Agent
Asking
Other
Agent
A gent
R ole
Source
Letizia
WWW guide
Liebermann (1995)
Remembrance Agent
memory aid
Rhodes & Starner (1996)
NewT
UseNet news filter
Sheth & Maes (1993)
Yenta
matchmaking and
referrals
Foner (1996)
Kasbah
buy and sell items on
the WWW
entertainment selection
Chavez & Maes (1996)
schedule meetings
Dent et al. (1992)
Ringo/HOMR
Calender Apprentice
(CAP)
Shardanand & Maes (1995)
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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 applicationlevel 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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AgentSpace (Ichiro Sato): Applications
• Text editor
• Clock
• Mail service
• Chat Tool
• Paint tool
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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)
Multi robot control
Automated office
Speech recognition
over the web
Wizard Info.
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The Open Agent Architecture: Applications (2)
Multimodal maps
Agent Development Tools
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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
assistants - lifelike animated characters that interact with a user in a natural spoken
dialogue
– The work is built upon the Whisper speaker-independent continuous speech
recognition system and a broad coverage English understanding system, both also
developed at Microsoft Research
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Web Search
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Recommendation
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Work Assistant
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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,
especially 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 TaskStructured 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
scheduled 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
Goal and Task
Specifications
Results
Interface Agent 1
Interface Agent 2
Interface Agent i
Tasks
Solutions
Task Agent 1
Info & Service
Requests
Task Agent 2
Information Integration
Conflict Resolution
Middle Agent 2
Advertisements
Information
Agent 1
Queries
Task Agent t
Info
Source 1
Replies
Information
Agent n
Answers
Info
Source 2
Info
Source m
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RETSINA
Agent Description
• Interface Agents
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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:
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Agent Name Service
Matchmaker
Broker
MAS Interoperator
(White Pages)
(Yellow Pages)
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Application: ModSAF
RETSINA
• Modular Semi- Automated
Forces
• “Real world” events are simulated
in Agent Storm by interaction with
ModSAF
• minefield discovery
• encountering Threat platoon
• announcements of passed
checkpoints
• RETSINA Mission Agents control
ModSAF platoon.
• route directions
• marching orders
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Application: RETSINA De-mining System
Without Team-Aware Coordination
• Using simple homogenous strategy
• Robots interfere with each other
• Robots attempt to de-mine same mine
RETSINA
With Team-Aware Coordination
• 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
http://www.cs.cmu.edu/~softagents/demining.html
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Application: MORSE
RETSINA
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Application: RCAL
RETSINA
• RETSINA Calendar Agent and Electronic Secretary
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RETSINA
Application: MokSAF
Charlie’s Shared Route
Bravo’s Shared Route.
Note that this route
initially support’s
Charlie’s route, then
crosses to intercept
Alpha’s route.
Alpha’s Shared Route
Information about
shared routes…
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Application: PalmSAF
RETSINA
• 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
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Game AI, Bots and Agents
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Nintendogs
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Massive (www.massivesoftware.com)
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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
providing 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
documents, there is a lot of potential for automated FAQ servers.
– Expertise finders: Suppose I want to know about people interested in temporal
belief 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
understand 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 식으
로 수행하고 필요한 기술은 무엇일까
– 최봉환: 사용자의 의도를 어떻게 인식하는가
– 이승현
• 사용자로부터의 불충분한 정보를 바탕으로 무언가를 어떻게 효율적으로 검색해서 사용자
가 원하는 결과를 내주는가
• 웹 페이지 연결에 있어서 URL을 통한 것이 아니라 "학교 홈페이지" 등 기능이나 이름을 통
한 연결
– 노홍찬: 어떻게 에이젼트가 사용자의 성향에 대해 학습할 수 있는가
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주요 기능
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일정관리
문서 검색 및 관리
정보추천
상황인식(시각/청각)
전화연결/자동응답
문맥관리
대화기능(음성인식/대화관리/음성합성)
아바타 관리
입력
인간기능 통합모델
출력
대화기능
사용자 질의
사용자 응답
시각
청각
온도
습도
추론
상황인식
판단
행위
학습
계획 모델링
행동기능
서비스
Etc.
지식관리
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