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Intelligent Software Agents Group

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comparative prices for those legs. OVERVIEW. Vision. Approach. Selected Research Projects ... RETSINA Calendar Agent and Electronic Secretary. MoCHA ... – PowerPoint PPT presentation

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Title: Intelligent Software Agents Group


1
Intelligent Software Agents Lab
The Robotics Institute Carnegie Mellon University
5000 Forbes Avenue Pittsburgh, PA 15213-3890 (U
.S.A.)
2
OVERVIEW
  • Vision
  • Approach
  • Selected Research Projects

3
Accomplishing Tasks for Humans
  • Augment human teams via RETSINA-guided robots
  • Examples
  • Robots for urban search and rescue (USAR)
  • Coordination of robots in time-critical missions
  • Reduce the information overload for humans
  • Examples
  • Watch for any bad news about stocks in my
    portfolio.
  • Notify me when something will affect my plans.
  • Human users need only specify high-level
    objectives
  • Examples
  • Find and rescue any human survivors of this
    collapsed building.
  • Plan my trip from Pittsburgh to Trento.

4
Improve and Diffuse Accessibility
  • Any Time - Any Place Computing
  • Agents accessible from any device
  • Appropriateness of Human-Robot Interface (HRI)
  • Information conveyed on most appropriate device
  • Information conveyed at most appropriate time
  • Unobtrusive Computing
  • Reduce the overhead of humans having to specify
    their intentions
  • Agents proactively assist humans based on their
    awareness of the users goals and context

5
Transform the Internet to ServiceNet
  • from a network of information providers
  • user must find information sources
  • user must integrate information
  • to a network of service providers
  • agents find requested unanticipated information
    for the user
  • agents perform requested and implied services for
    the user
  • agents present finished product to user

6
Achieve Ideals of Software Engineering
  • Truly reusable software components
  • Accessible to lay-programmers
  • intuitive and imprecise
  • Scalable, reliable, robust, and fault-tolerant
    computing
  • Program by high-level service requirement
    descriptions
  • Example
  • To find the best flights,
  • find any airline reservation system
  • that publishes departure / arrival times
  • of four or more commercial airlines and
  • comparative prices for those legs.

7
OVERVIEW
  • Vision
  • Approach
  • Selected Research Projects

8
Approach
  • Consider technologies that will achieve our
    vision in an economically viable way.
  • Robotic Technologies
  • Network Technologies
  • Sophisticated Natural Language Technologies
  • Human / Agent Interactions
  • Agent / Agent Interactions
  • Agent-Oriented Software Engineering
  • Automatic Learning and Artificial Intelligence

9
Robotic Technologies
  • Team-Oriented Robot Mine Diffusers
  • Robots for Urban Search and Rescue
  • Physcial USAR lab
  • Simulated Robotic Search and Rescue
  • Robots that autonomously combine with each other
  • For climbing stairs or accessing hard to reach
    areas
  • Uncouple once the obstacle is surmounted

10
Necessary Network Technologies
  • Local Area Network Discovery
  • SSDP, SLP
  • Wide Area Network Discovery
  • Agent-to-Agent Discovery
  • Network Security
  • protection from malicious attacks and spoofing
  • Encryption, Authentication, Repudiation
  • Agent Location Schemes
  • White Pages, Yellow Pages, LDAP

11
Sophisticated Natural Language Technologies
  • Natural Language Understanding and Generation
  • Speech Recognition and Synthesis
  • Information Retrieval, Text Categorization
  • Topic Tracking and Detection, Text Summarization
  • Content and Concept Extraction

12
Human / Agent Interactions
  • Well-considered information presentation and
    solicitation techniques
  • Human users may reject non-intuitive agent
    solutions
  • Human users do not want to spend their time
    specifying preferences
  • Organization and management of context-sensitive
    preferences
  • Agents should prefer learning by observing
    rather than by asking humans
  • Reliable techniques where humans specify and
    delegate tasks to their agents
  • Understand the nature of human team formation
  • Model human team formation strategies as rules
    for agents

13
Agent / Agent Interactions
  • Automatic Task Decomposition and Delegation
  • Consider how agents recognize tasks to delegate
  • Team Coordination and Communication
  • Evaluate tradeoffs between teaming and not
    teaming
  • Applicability to Physical Robots
  • How well does agent situation-awareness improve
    robot performance?
  • Which agent coordination strategies are
    applicable to physical robots?

14
RETSINA Today
  • Assumptions
  • Use Available Resources
  • RETSINA Agent Architecture
  • RETSINA MAS Infrastructure
  • RETSINA MAS Architecture

15
Assumptions
  • Open and Dynamic Environments
  • agents / services will not always exist
  • agent locations change
  • system load balancing
  • agent mobility
  • agent identity changes
  • cannot predict its name
  • cannot predict the vocabulary used to describe
    it
  • Assume Service Redundancy
  • multiple/ competing service providers
  • differentiate on service parameters
  • speed, price, security, reliability, reputation,
    etc.

16
Use Available Resources
  • For ubiquity, accessibility, scalability,
    viability
  • Use current and evolving standards
  • Discovery SLP, SSDP, DNS, dDNS, Gnutella, etc.
  • ACLs KQML, FIPA, DAML, etc.
  • Representation XML, HTML, RDF, etc.
  • Agents in any computing environment
  • Languages C/C, Java, Perl, Prolog, Python,
    etc.
  • Applications ModSAF, MSOffice, etc.
  • Devices cell phones, PDAs, tablets, laptops,
    etc.
  • Necessitates a Robust Interface Architecture

17
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 scheduled plan
  • swaps-out plans for those with higher priorities

http//www.cs.cmu.edu/softagents/retsina.html
18
MAS Infrastructure
Individual Agent Infrastructure
MAS Interoperation Translation Services Interope
rator Services
Interoperation Interoperation Modules
Capability to Agent Mapping Middle Agents
Capability to Agent Mapping Middle Agent Componen
ts
Name to Location Mapping Agent Name Service
Name to Location Mapping ANS Component
Security Certificate Authority Cryptographic Se
rvice
Security Security Module Private/Public Key
s
Performance Services MAS Monitoring Reputation
Services
Performance Services Performance Service Modules
Multi-Agent Management Services
Logging Activity Visualization Launching
Management Services Logging and Visualization Com
ponents
ACL Infrastructure Public Ontology Protocol Ser
vers
ACL Infrastructure Parser, Private Ontology, Prot
ocol Engine
Communications Infrastructure Discovery Message
Transfer
Communication Modules Discovery Message Transf
er Modules
Operating Environment Machines, OS, Network, M
ulticast Transport Layer, TCP/IP, Wireless,
Infrared, SSL
19
RETSINA Functional Architecture
User 1
User 2
User u
Goal and Task Specifications
Results
Interface Agent 1
Interface Agent 2
Interface Agent i
Solutions
Tasks
Task Agent 1
Task Agent 2
Task Agent t
Info Service Requests
Information Integration Conflict Resolution
Replies
Middle Agent 2
Information Agent n
Advertisements
Information Agent 1
Answers
Info Source m
Info Source 1
Info Source 2
Queries
20
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

21
Task Agents
  • Know what to do and how to do it
  • Responsible for task delegation
  • May enlist the help of other task agents

22
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

23
RETSINA Matchmakers
  • Enable an agent to find another agent
  • by functionality, capability, availability, time
    to completion, etc.
  • without knowing who or where the provider agent
    might be
  • Enables multi-agent systems MASs
  • to dynamically reconfigure themselves to suite a
    need
  • reduce agent systems administration overhead
  • to scale in the number of agents that are
    distributed in a computer network
  • RETSINA has two main types of Matchmakers
  • RETSINA Matchmaker
  • http//www.cs.cmu.edu/softagents/matchmaker.html

  • Please try it http//www.cs.cmu.edu/softagents
    /a-match/index.html
  • LARKS Matchmaker
  • Language for Advertisement and Request for
    Knowledge Sharing
  • http//www.cs.cmu.edu/softagents/larks.html

24
The Matchmaking Process
2. Request for service
Matchmaker
Requester
3. Unsorted full description of (P1,P2, , Pk)
1. Advertisement of capabilities
service parameters
4. Delegation of service
5. Results of service request
Provider 1
Provider n
25
MAS Interoperators
  • Translate between MAS architectures
  • Advertisements
  • Queries and replies
  • Informational messages
  • Achieve economic MAS scalability

26
Information Agents
  • Present information sources to MAS
  • Port MAS output to external data stores
  • Represent data and events
  • Four well-known and reusable behaviors
  • Single-Shot Query
  • Active Monitor Query
  • Passive Monitor Query
  • Update Query

27
OVERVIEW
  • Vision
  • Approach
  • Selected Research Projects

28
RETSINA supports component reuse across
application domains
See the ONR JoCCASTA video
Also view our CoABS TIE3 video
29
JoCCASTA
30
CoABS Control of Agent-Based SystemsNEO Non-comba
tent Evacuation OperationTIE3 Technical
Integration Experiment 3
31
Agent Storm Simulated in 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 checkpoints
  • RETSINA Mission Agents control ModSAF platoon.
  • route directions
  • marching orders

32
Agent Storm Scenario
  • Threat forces are in retreat
  • Three tank platoon commanders must patrol an
    area
  • Chase any Threat stragglers out of the area
  • May need to engage if necessary
  • Agents help humans
  • Plan the mission
  • Gather and use intelligence to re-plan mission
  • Actively monitor patrol area during execution
  • De-mine an area

33
RETSINA De-mining System
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

http//www.cs.cmu.edu/softagents/demining.html
34
MORSE
35
RCAL RETSINA Calendar Agent and Electronic Sec
retary
36
MoCHA
Mobile Communication of Heterogeneous Agents
  • Anytime, Anywhere Interfaces
  • Context-sensitive preference management
  • Integrates Devices and Agentified Services

37
Warren
  • Portfolio Management Application
  • Tracks price per share and beta values
  • Warns user when portfolio exceeds bounds
  • Provides web search of holdings in portfolio
  • Current research Text Classification of
    whether the news article reports good news or bad
    news about a company.

38
MokSAF
Charlies Shared Route
Bravos Shared Route. Note that this route init
ially supports Charlies route, then crosses to
intercept Alphas route.
Information about shared routes
Alphas Shared Route
39
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

40
ATLAS / DAML
  • Human and machine-readable web markup
  • Improve web searches via semantic indexing
  • Based on XML, RDF, Oil, Shoe
  • Specify DAML-S
  • Future basis for advertising and ACL

41
RETSINA Visual Recognition Agent
  • Reconnaissance Satellite Agent
  • triggers on asynchronous events
  • recognizes Threat tanks
  • agents autonomously locate it via a Matchmaker
  • agents subscribe to it via the RETSINA Passive
    Monitor Query
  • RETSINA Information Agent
  • demonstrates that the information agent protocol
    model is applicable to both data and event
    sources
  • Used / Reused in Many Projects
  • MURI 98 Joccasta
  • CoABS 99 NEO TIE
  • MURI 00 Agent Storm

http//www.cs.cmu.edu/softagents/visrec.html
42
Contact Information
Prof. Katia Sycara Principle Investigator The
Robotics Institute Carnegie Mellon University 50
00 Forbes Avenue Pittsburgh, PA 15213-3890 (U.S.A
.) Tel 1 (412) 268-8825 Fax 1 (412) 268-5
569 katia_at_cs.cmu.edu http//www.cs.cmu.edu/k
atia
Joseph Giampapa Project Manager The Robotics I
nstitute Carnegie Mellon University 5000 Forbes
Avenue Pittsburgh, PA 15213-3890 (U.S.A.) Tel
1 (412) 268-5245 Fax 1 (412) 268-5569 gar
of_at_cs.cmu.edu http//www.cs.cmu.edu/garof
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