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.)
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OVERVIEW

• Vision
• Approach
• Selected Research Projects

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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.

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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

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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

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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.

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OVERVIEW

• Vision
• Approach
• Selected Research Projects

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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

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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

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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

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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

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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

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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?

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RETSINA Today

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

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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.

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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

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

http//www.cs.cmu.edu/softagents/retsina.html
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MAS Infrastructure
Individual Agent Infrastructure
MAS Interoperation Translation Services
Interoperator Services
Interoperation Interoperation Modules
Capability to Agent Mapping Middle Agents
Capability to Agent Mapping Middle Agent
Components
Name to Location Mapping Agent Name Service
Name to Location Mapping ANS Component
Security Certificate Authority Cryptographic
Service
Security Security Module Private/Public Keys
Performance Services MAS Monitoring Reputation
Services
Performance Services Performance Service Modules
Multi-Agent Management Services Logging Activity
Visualization Launching
Management Services Logging and Visualization
Components
ACL Infrastructure Public Ontology Protocol
Servers
ACL Infrastructure Parser, Private Ontology,
Protocol Engine
Communications Infrastructure Discovery Message
Transfer
Communication Modules Discovery Message
Transfer Modules
Operating Environment Machines, OS, Network,
Multicast Transport Layer, TCP/IP, Wireless,
Infrared, SSL
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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
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
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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

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Task Agents

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

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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

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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

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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
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MAS Interoperators

• Translate between MAS architectures
• Advertisements
• Queries and replies
• Informational messages
• Achieve economic MAS scalability

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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

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OVERVIEW

• Vision
• Approach
• Selected Research Projects

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RETSINA supports component reuse across
application domains
See the ONR JoCCASTA video
Also view our CoABS TIE3 video
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JoCCASTA
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CoABS Control of Agent-Based SystemsNEO Non-comba
tent Evacuation OperationTIE3 Technical
Integration Experiment 3
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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

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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

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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
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MORSE
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RCAL RETSINA Calendar Agent and Electronic
Secretary
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MoCHA
Mobile Communication of Heterogeneous Agents

• Anytime, Anywhere Interfaces
• Context-sensitive preference management
• Integrates Devices and Agentified Services

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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.

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

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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

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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
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Contact Information
Prof. Katia Sycara Principle Investigator The
Robotics Institute Carnegie Mellon
University 5000 Forbes Avenue Pittsburgh, PA
15213-3890 (U.S.A.) Tel [phone] Fax
1 [phone] katia_at_cs.cmu.edu http//www.cs
.cmu.edu/katia
Joseph Giampapa Project Manager The Robotics
Institute Carnegie Mellon University 5000 Forbes
Avenue Pittsburgh, PA 15213-3890 (U.S.A.) Tel
1 [phone] Fax 1 (412)
268-5569 garof_at_cs.cmu.edu http//www.cs.cmu.edu/
garof