1. Some Thoughts to Consider 1 What is so ‘artificial’ about Artificial Intelligence? Just what are ‘Knowledge Based Systems’ anyway? Why would we ever want to study this stuff? Mind is what the brain does. ‘Brains cause minds’ - Searle. Software is to machine as mind is to brain. ‘Knowledge is power’ - Francis Bacon. Can machines think? What does it mean to build software systems that are ‘people-literate’, rather than having people be ‘computer-literate’? Does the study and use of AI help us better understand how people think and act?

2. Anticipated Benefits of Investing in Emerging Technologies Particularly the technologies of: Knowledge based systems Agent oriented systems Service oriented architectures Neural networks Genetic algorithms Move people to a new level of problem solving. Raise business concepts and operations to a higher level of understanding. Manage the increased complexity of running the business. Reduce the time required to field new applications. Produce more intelligent performance enhancement applications. Reduce long term system maintenance time. Provide bottom-line value to clients and profit for the corporation.

3. The Main Design Issues Representation What structures or ‘metaphors’ shall be used? Knowledge Where and how shall it be represented? Process Control Flow Where in the architecture shall it reside?

4. Types of Knowledge Facts Process Knowledge Operational Know-How Market Knowledge Technology/System/Database Knowledge Dependency Knowledge Causality Knowledge Conflict Knowledge Constraint Knowledge

5. Types of Knowledge Concept Knowledge (Objects, Nodes) Physical objects Actions Events Categories Relationship Knowledge (Links, Arcs) A-kind-of Part-of Instance-of Cause-of Acts-on Descriptive Knowledge (Attributes) Procedural Knowledge (Algorithms) Inheritance Knowledge (Classes) Heuristic Knowledge (Rules of Thumb) Inference Knowledge (Strategies) Emergent Knowledge (Neural Nets)

6. Types of Representation Declarative (Facts) Procedural (Instructions) Inferential (Implied by Reasoning) Rules Frames Predicate Logic Semantic Networks Classes – Objects – Methods Actors – Agents Neural Nets Genetic Algorithms Mechanisms of Representation

7. Key Knowledge Engineering Activities Knowledge Acquisition Interviewing experts Protocol analysis Prototype iteration System acquisition of knowledge (learning) Knowledge Representation Categories of the knowledge Structure of the knowledge Tool selection Knowledge Utilization Control structure – “knowledge flow” Reasoning strategies Justification and explanation Dealing with uncertainty and incompleteness System validation

8. So, What About Decision Support? We are evolving a new kind of product. One that is knowledge-enriched, with locally-authored decision support. Rather than a vendor-supplied, predetermined package of software logic and data structures. This requires intense knowledge engineering and knowledge representation that is substantially different from traditional programming practice. Knowledge is represented declaratively in a knowledge base such that customers can customize it for local use. Knowledge is not represented in programming language code.

9. Model Based Software Design Represents a different way of thinking about software design and implementation. Takes the clinical (business) knowledge out of the Java code. Moves the problem solving process to a higher level of abstraction. Models become the vernacular for clinical (business) architecture discussions. Representation is ‘outside’ the Java classes, rather than ‘inside’ the Java classes. The Java classes become more like ‘engines’ that manage and reason over the external representations. The movement to XML, RDF, and OWL is movement in this design direction.

10. Motivation for Model Based Architecture We’re growing out of traditional ‘database-to- screen’ types of product. We are faced with providing more ‘knowledge-rich’ products. Customers require customization of the content we deliver for their specific product venue. More and more of our traditional products require integration and interoperability. Analysts are required to participate more in the design of representational structures. Developers are required to participate more in product design. The level of complexity of our products is increasing beyond what is manageable by traditional development means.