Transcript Some Thoughts to Consider 1

Some Thoughts to Consider 1
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What is so ‘artificial’ about Artificial Intelligence?
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Just what are ‘Knowledge Based Systems’
anyway?
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Why would we ever want to study this stuff?
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Mind is what the brain does.
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‘Brains cause minds’ - Searle.
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Software is to machine as mind is to brain.
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‘Knowledge is power’ - Francis Bacon.
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Can machines think?
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What does it mean to build software systems
that are ‘people-literate’, rather than having
people be ‘computer-literate’?
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Does the study and use of AI help us better
understand how people think and act?
Anticipated Benefits of Investing in
Emerging Technologies
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Particularly the technologies of:
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Knowledge based systems
Agent oriented systems
Service oriented architectures
Neural networks
Genetic algorithms
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Move people to a new level of problem solving.
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Raise business concepts and operations to a
higher level of understanding.
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Manage the increased complexity of running the
business.
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Reduce the time required to field new
applications.
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Produce more intelligent performance
enhancement applications.
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Reduce long term system maintenance time.
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Provide bottom-line value to clients and profit for
the corporation.
The Main Design Issues
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Representation
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What structures or ‘metaphors’
shall be used?
Knowledge
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Where and how shall it be
represented?
Process Control Flow
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Where in the architecture shall it
reside?
Types of Knowledge
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Facts
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Process Knowledge
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Operational Know-How
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Market Knowledge
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Technology/System/Database Knowledge
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Dependency Knowledge
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Causality Knowledge
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Conflict Knowledge
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Constraint Knowledge
Types of Knowledge
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Concept Knowledge (Objects, Nodes)
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Physical objects
Actions
Events
Categories
Relationship Knowledge (Links, Arcs)
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A-kind-of
Part-of
Instance-of
Cause-of
Acts-on
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Descriptive Knowledge (Attributes)
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Procedural Knowledge (Algorithms)
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Inheritance Knowledge (Classes)
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Heuristic Knowledge (Rules of Thumb)
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Inference Knowledge (Strategies)
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Emergent Knowledge (Neural Nets)
Types of Representation
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Declarative (Facts)
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Procedural (Instructions)
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Inferential (Implied by Reasoning)
Mechanisms of Representation
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Rules
Frames
Predicate Logic
Semantic Networks
Classes – Objects – Methods
Actors – Agents
Neural Nets
Genetic Algorithms
Key Knowledge Engineering Activities
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Knowledge Acquisition
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Interviewing experts
Protocol analysis
Prototype iteration
System acquisition of knowledge (learning)
Knowledge Representation
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Categories of the knowledge
Structure of the knowledge
Tool selection
Knowledge Utilization
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Control structure – “knowledge flow”
Reasoning strategies
Justification and explanation
Dealing with uncertainty and incompleteness
System validation
So, What About Decision Support?
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We are evolving a new kind of product.
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One that is knowledge-enriched, with locally-authored
decision support.
Rather than a vendor-supplied, predetermined package
of software logic and data structures.
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This requires intense knowledge engineering
and knowledge representation that is
substantially different from traditional
programming practice.
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Knowledge is represented declaratively in a
knowledge base such that customers can
customize it for local use.
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Knowledge is not represented in programming language
code.
Model Based Software Design
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Represents a different way of thinking about
software design and implementation.
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Takes the clinical (business) knowledge out of
the Java code.
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Moves the problem solving process to a higher
level of abstraction.
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Models become the vernacular for clinical
(business) architecture discussions.
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Representation is ‘outside’ the Java classes,
rather than ‘inside’ the Java classes.
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The Java classes become more like ‘engines’
that manage and reason over the external
representations.
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The movement to XML, RDF, and OWL is
movement in this design direction.
Motivation for Model Based Architecture
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We’re growing out of traditional ‘database-toscreen’ types of product.
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We are faced with providing more
‘knowledge-rich’ products.
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Customers require customization of the
content we deliver for their specific product
venue.
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More and more of our traditional products
require integration and interoperability.
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Analysts are required to participate more in
the design of representational structures.
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Developers are required to participate more in
product design.
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The level of complexity of our products is
increasing beyond what is manageable by
traditional development means.