Transcript PowerPoint - Carnegie Mellon University

Intelligent Software Agents Lab
The Robotics Institute
Carnegie Mellon University
5000 Forbes Avenue
Pittsburgh, PA 15213-3890 (U.S.A.)
OVERVIEW
• Vision
• Approach
• Selected Research Projects
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.”
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 user’s goals and context
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
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.
OVERVIEW
• Vision
• Approach
• Selected Research Projects
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
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
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
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
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
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?
RETSINA Today
•
•
•
•
•
Assumptions
Use Available Resources
RETSINA Agent Architecture
RETSINA MAS Infrastructure
RETSINA MAS Architecture
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.
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
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
MAS Infrastructure
MAS Infrastructure
Individual Agent Infrastructure
MAS Interoperation
Interoperation
Translation Services Interoperator Services
Interoperation Modules
Capability to Agent Mapping
Capability to Agent Mapping
Middle Agents
Middle Agent Components
Name to Location Mapping
Name to Location Mapping
Agent Name Service
ANS Component
Security
Security
Certificate Authority Cryptographic Service
Security Module
Private/Public Keys
Performance Services
Performance Services
MAS Monitoring Reputation Services
Performance Service Modules
Multi-Agent Management Services
Management Services
Logging Activity Visualization Launching
Logging and Visualization Components
ACL Infrastructure
ACL Infrastructure
Public Ontology Protocol Servers
Parser, Private Ontology, Protocol Engine
Communications Infrastructure
Communication Modules
Discovery Message Transfer
Discovery
Message Transfer Modules
Operating Environment
Machines, OS, Network, Multicast Transport Layer, TCP/IP, Wireless, Infrared, SSL
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
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 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
The Matchmaking Process
2. Request for service
Requester
Matchmaker
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
MAS Interoperators
• Translate
between MAS
architectures:
• Advertisements
• Queries and replies
• Informational messages
• Achieve
economic MAS
scalability
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
OVERVIEW
• Vision
• Approach
• Selected Research Projects
RETSINA supports component reuse across application domains
See the ONR
JoCCASTA video
Also view our
CoABS TIE3
video
JoCCASTA
CoABS Control of Agent-Based Systems
NEO
Non-combatent Evacuation Operation
TIE3
Technical Integration Experiment 3
Agent Storm Simulated in ModSAF
Modular SemiAutomated 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
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
RETSINA De-mining System
Without Team-Aware Coordination
• Using simple homogenous strategy
• Robots interfere with each other
• Robots attempt to de-mine same mine
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
MORSE
RCAL:
RETSINA Calendar Agent and
Electronic Secretary
MoCHA
• Anytime, Anywhere
Interfaces
• Context-sensitive preference
management
• Integrates Devices and
Agentified Services
Mobile Communication of Heterogeneous Agents
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.
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…
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
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
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
Contact Information:
Prof. Katia Sycara
Principle Investigator
Joseph Giampapa
Project Manager
The Robotics Institute
Carnegie Mellon University
5000 Forbes Avenue
Pittsburgh, PA 15213-3890 (U.S.A.)
The Robotics Institute
Carnegie Mellon University
5000 Forbes Avenue
Pittsburgh, PA 15213-3890 (U.S.A.)
Tel: +1 (412) 268-8825
Fax: +1 (412) 268-5569
Tel: +1 (412) 268-5245
Fax: +1 (412) 268-5569
[email protected]
http://www.cs.cmu.edu/~katia
[email protected]
http://www.cs.cmu.edu/~garof