Transcript Agent Technology Group - Overview

Nokia Research Center
Agent Technology Group
Oxygen Collaboration
Mark Adler, Ora Lassila
Nokia Research Center
January 9, 2002
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Outline
• Vision: Internet and Mobility
• Enabling Technologies
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Artificial Intelligence
Semantic Web
Ubiquitous Computing
• Semantic Gadgets
• Oxygen Contributions
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Demo
Future Work
• Questions
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Internet and Mobility: a Future
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The Future Intelligent World
The future will contain:
• Wireless "smart" devices in the home, office, automobile, and
pocket
• New devices that can be introduced or removed at any time
• Environments accommodating the user - agent technology and
machine learning techniques that allow devices and
environments to adapt to the user
• Intelligent software agents (personal assistants) with the
capability of enabling services that draw on the user’s personal
preferences, the user’s location and local context
• Agents that improve their performance over time by utilizing
user emotions, as well as planning and machine learning
techniques
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Mobility Makes Things Different
• Device location is a completely new dimension
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more information about the user and the usage context available
new applications and services are possible
• Devices are different
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reduced capabilities: smaller screens, slow input devices, lower
bandwidth, higher latency, worse reliability, …
trusted device: always with you and has access to your private data
• Usage contexts and needs are different
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awkward usage situations (e.g., in the car while driving)
specific needs (“surfing” unlikely)
you are always “on” (= connected)
• Dilemma:
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the Internet represents a departure from physical reality
BUT mobility grounds services and users to the physical world
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Critical Components of Mobile Internet
• Access to internet-based services from small
handheld terminals
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first step: WAP (quick build-up of a large user base)
initial applications include personal information management and
connectivity, “infotainment”, (mobile) e-commerce, vertical
applications, and access to corporate intranet data
• Dynamic synthesis of content
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first step: data in XML, transformations to suitable formats
device independence is key to long-term interoperability
• Context-dependence
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first step: customization and personalization
adaptation of services based on context
location is one dimension of a “context”, but there are others
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New Enabling Technologies
• Artificial Intelligence
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machine learning: allows us to customize, personalize, and adapt
without bothering the user
automated planning: enables autonomous operation (i.e., departure
from the “tool metaphor” to delegation of decision-making power)
• “Semantic Web”
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intelligent synthesis of personalized, context-dependent content
from multiple information sources (ad hoc and on demand)
explicit representation of semantics of data and services
• Ubiquitous Computing
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a paradigm shift in personal computing
LP RF networks, ad hoc networking, discovery of devices and
services, etc.
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Artificial Intelligence
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AI Technologies
• Avoid being overwhelmed by irrelevant information
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Rule-based filtering
Constraint-satisfaction
Collaborative filtering
Adaptation to individual's needs and preferences (Machine Learning)
• Putting it all together
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Put together plans from available information to achieve goals
Find new solutions as problems arise
• Delegate decision-making and control
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Move to appropriate platform
Simplify interactions autonomously
Improve and enhance user interfaces
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AI: Some Examples
• Context (location, user preference, task) determines
choices
• Schedule meetings, deliver information
• Locate "services," provide maps
• Mobile agent code to:
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enhance service and minimize power consumption
exploit richer environments, e.g., bandwidth, memory, computation cycles
but, must deal with issues of privacy, security, …
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Semantic Web
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Semantic Web: Motivation and Features
• Current WWW was built for humans, not for machines
• “Semantic Web” is like a global KB
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(cf. use of the WWW as an infrastructure)
better security and privacy will allow us to reason about trust,
enabling completely new kinds of services and businesses
content-with-semantics paves way for the use of software agents
• Hyperlinks with meaning
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agents can navigate the WWW by following semantic links
• What will happen when data comes with semantics?
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data from different sources can be combined
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new, perhaps unforeseen opportunities and functionality will result
machines can meaningfully use the WWW and perform tasks on our
behalf (“machine-understandable” content)
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Resource Description Framework
• RDF is a data model
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the model is domain-neutral, application-neutral and ready for
internationalization (i18n)
the model can be viewed as directed, labeled graphs or as an
object-oriented model (object/attribute/value)
can describe anything that has a URI
the specification provides an encoding (in XML) of the model
important: syntactic details are secondary, they are largely handled
by using XML (RDF defines a convention of XML usage)
• RDF data model is a conceptual layer on top of XML
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consequently, RDF is independent of XML
RDF data might not be stored in XML form
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it might reside, for example, in an RDB
XML relieves us of syntactic details when transporting RDF
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DARPA Agent Markup Language
DAML is a research program that
• Develops technologies for the Semantic Web
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DARPA program
broader effort (including EU)
• Adds logic layers on top of RDF
• Builds basic ontologies
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Ubiquitous Computing
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Ubiquitous Computing @ NRC/ATG
• Observing some general trends
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handheld computing devices
wireless communication
internet connects “everything”
but, technology is not necessarily becoming easier to use
• Smart rooms: earlier focus on “static” configurations
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how people really live and work has largely been ignored
functions: context identification, remote control
• Our goal: “things should just work”
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devices should automatically “figure out what to do”, form
communities and collaborate
environment should adapt to users, not vice versa
environments are “dynamic”: changes should cause minimal
disruption
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Agent Technologies
Study software agents (especially intelligent multiagent systems) and artificial
intelligence, with applications to ubiquitous computing and web technologies
and standards.
Reasoning
• Focus on Smart Environments
• learning
• affective and context-aware
• planning
computing
• agents
• dynamic, distributed agent platforms
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adaptive, self-organizing networks
device and service discovery and
negotiation
machine learning
representation languages
• Consult on internal
technology
• Collaborate with academia
• Monitor web technologies
and influence standards
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Smart Environment
Representation
• models of devices
• models of services
• models of the world
• “Semantic Web”, DAML
Connectivity
• ad hoc networks
• service discovery
• wireless
• UPnP…
Low-level Discovery Services
• Large number of discovery/name/directory services
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file systems
DHCP, DNS
SLP, LDAP, X.500
crawlers, web search engines
• Each service uses different
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metadata
protocols
query language
• Disadvantages include
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proliferation of different tools and APIs
incomplete and inconsistent views of the same data
network management complications due to the above
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Role of Standardization
• Open standards are a prerequisite for interoperability
• Many initiatives for device, service, and capability
discovery
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UPnP (Microsoft et al), JINI (Sun), Salutation (several companies), …
Service Location Protocol SLP (IETF)
CC/PP (W3C)
• But, standards will “only get us so far”
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beyond, we need “reasoning”
many emerging standards are in trouble because of vocabularies
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CC/PP, P3P (adoption hindered by lack of vocabularies)
proliferation of (specialized) XML DTDs
Dublin Core (4 years, 15 attributes!)
lack of tools for maintaining (e.g., merging) vocabularies
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Semantic Gadgets
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What Are Semantic Gadgets?
• Combine ubiquitous computing & the Semantic Web
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devices capabilities and service functionality explicitly represented
everything is addressable (using URIs)
Semantic Web is the basis for “semantic interoperability”
• Critical components
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connectivity
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representation
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wireless, ad hoc networks + service discovery
models of devices, services, users, environments, etc.
reasoning
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learning
planning
• Other useful technologies
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sensors, context-awareness, mobile code, …
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Smart Communities of Devices
• All devices advertise their services
• A device can extend its functionality by
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discovering missing functionality offered by another device
contracting the use of the service
• Everything can be discovered
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including “reasoning services” or who is going to develop overall
plans for integrating devices into larger, task-oriented “teams”
(OK, we are still working on this…)
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MIT Oxygen and NRC
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Personnel
• 10 people
• 5 summer interns, including 3 from Oxygen:
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Mike Oltmans (AI)
Nick Hanssens (AI)
Nissanka Bodhi Priyantha (LCS)
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Museum Guide Demo
• Metaglue environment
• Linux iPaqs
• Cricket location beacons
• Service discovery
• User preferences
• Affective computing
• Semantic Web
• Adaptation
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Collaborative Effort
• Oxygen Technology Transfer to Nokia
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Metaglue
Crickets
• Nokia Development
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Ad hoc networking
Device Discovery
• Integration at Nokia NRC
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Not only independently developed technology
But also LCS (Crickets) and AI (Metaglue)
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Personal Assistant
Trust In Me
Nokia’s Affective Personal Advisor
• Main ideas:
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Located on user’s handheld device
Uses information about user’s context and preferences to make
suggestions and to filter information
Context includes any relevant information about the user
(including affective state) and user’s surroundings
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Museum Entrance
• Access museum directory
• Using the museum directory,
the personal assistant
suggests exhibits based on
context and preferences
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Exhibit
• Assistant notes interest in
the current exhibit and
records it in its user profile
• Assistant suggests similar
exhibits and provides maps
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Affective Message
• Receives affective message
(sender can choose to
transmit her/his current
affective state)
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Gift Shop
• Accesses gift shop website
• Assistant uses Sonya’s
preference profile to
suggest suitable gifts for
her
• After selecting gift to
purchase, the transaction
is completed by his
assistant
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Customizable Restaurant
• Beam their food preferences
to the booth manager
• Booth manager displays a
tailored menu
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More Suggestions
• After receiving the order, the
booth manager displays a list
of wines that go with the food
selection
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Adapt Environment
• The booth manager can
adapt the environment
(lights, music) based on the
customers’ mood
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Future Work
• Web Services using DAML-S
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DAML-S is a DAML+OIL ontology for Web Services
Investigate this as a starting point for representing device
functionality as "invokable" services.
• Other stuff…
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for smart environments, we also need something like common
sense reasoning (naïve physics?)
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Questions?
Mail to:
• [email protected]
• [email protected]
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