IS chapter 10
three kinds of knowledge
-structured -semistructured -tacit knowledge (unstructured)
knowledge base
collection of data, rules, procedures, and relationships that must be followed to achieve value or the proper outcome
knowledge base (scripts)
describe a sequence of events
semistructured
e-mail, voice mail, digital pictures, bulletin-board postings, designs, memos, graphics
structured
explicit knowledge that exists in formal documents or rules (reports, presentations, manuals, books, proposals)
tacit knowledge (unstructured)
knowledge residing in heads of employees, rarely written down; personal or informal knowledge - often represents an organization's best practices
explanation facility
performs tasks similar to what a human expert does by explaining to end users how recommendations are derived
backward chaining
process of starting with an answer or conclusion and working backward to the supporting facts -is "goal-driven" -attempts to justify the result or conclusion by determining if the facts in the situation would support the conclusion
forward chaining
process of starting with the facts (data) and working forward to the conclusion -is "data-driven" -starts with information from the user, then searches the knowledge base for rules, relationships that are relevant -series of "if-then-else" conditions
Learning management systems (LMS)
provide tools for management, delivery, tracking, and assessment of various types of employee learning and training
user interface
provides user-friendly access to the expert system
knowledge base (frames)
store conditions or facts about the topic
knowledge acquisition facility
the part of the expert system SW used to acquire and add new knowledge, rules and facts
knowledge based information systems
use knowledge about a specific complex application area to act as an expert consultant to end users
knowledge base management system
used to keep the knowledge base updated with changed to facts, figures, and rules; works with the knowledge acquisition facility
expert systems
useful for dealing with problems of classification in which there are relatively few alternative outcomes and in which these possible outcomes are known in advance
NOT building expert systems/ not using expert systems
-very few rules -too many rules -problems are in areas that are too wide and shallow -well-structured numerical problems are involved -disagreement among experts -problems are solved better by human experts
haptic interface
relays the sense of touch and other physical tracker (or other haptic device)
knowledge base (rules)
rule-conditional statement that links given conditions to actions or outcomes
human-like inferences
(logical conclusion, a conclusion based on reasoning) about knowledge contained in a special knowledge base
expert systems
-computerized advisory programs that imitate the reasoning processes of experts in solving difficult problems -used to make decisions usually made by more experienced employees or an expert in the field -replicate/mimic human expertise in a field to solve a well-defined problem
components of an expert system
-knowledge acquisition facility -knowledge base -knowledge base management system -user interface -explanation facility -inference engine
expert system challenges
-limited to relatively narrow problems -cannot readily deal with "mixed" knowledge -cannot update its own knowledge/learn on its own -has no "common sense"- doesn't know when to break the rules -difficult to incorporate human judgement, experience, intuition -may have high development &maintenance costs -may raise legal and ethical concerns -over time may weaken the human expertise in the organization
expert systems advantages/benefits
-never become distracted, forgetful, or tired -duplicate and preserve the expertise of scarce experts -preserve the expertise of employees who are retiring or leaving an organization -can provide portable expertise-more accessible -create consistency in decision making -can often outperform a human expert -improve the decision- making skills of non-experts -can be used to train others to become more knowledgeable in that area of expertise
building expert systems/using expert systems
-scarcity of human experts -human expertise is needed but one expert cant investigate all the dimensions of a problem -knowledge can be represented as rules or heuristics & the subject domain is limited enough to capture -decision or task has already been handled successfully by human experts -decision or task requires consistency and standardization -high payoff involved
inference engine
-the part of the expert system that seeks information and relationships from the user and the knowledge base and then provides answers, predictions and suggestions the way a human expert would -provides the "reasoning or thinking" -combines the facts of the situation at hand with the knowledge in the knowledge base to come up with an answer -searches the rules of other forms of knowledge in the knowledge base and "fires" those that are triggered by facts entered by the user
