CH 10 PT 2

sammie_cuellar
Flashcards by sammie_cuellar, updated more than 1 year ago
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Flashcards on CH 10 PT 2, created by sammie_cuellar on 05/07/2013.

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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 Knowledge-based information systems that use knowledge about a specific complex application area to act as an expert consultant to end users Make human-like inferences (logical conclusion, a conclusion based on reasoning) about knowledge contained in a special knowledge base 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
Knowledge-based Information Systems use knowledge about a specific complex application area to act as an expert consultant to end users
Human-like Interferences logical conclusion, a conclusion based on reasoning) about knowledge contained in a special knowledge base
Components of an expert system 1. Knowledge Acquisition Facility 2. Knowledge Base 3. Knowledge Base Management System 4. User Interface 5. Explanation Facility 6. Inference Engine
Knowledge acquisition facility the part of the Expert System SW used to acquire and add new knowledge, rules and facts
Knowledge base collection of data, rules, procedures, and relationships that must be followed to achieve value or the proper outcome
To organize the knowledge in a knowledge base: - Rules - Frames -Scripts
Rules - Rule – conditional statement that links given conditions to actions or outcomes - If-then statements – rules that suggest certain conditions - May be combined with probabilities
Frames Store conditions or facts about the topic
Scripts describe a sequence of events
3. Knowledge base Management System used to keep the knowledge base updated with changes to facts, figures, and rules; works with the Knowledge Acquisition Facility
4. User Interface Provides user-friendly access to the expert system
5. Explanation Facility Performs tasks similar to what a human expert does by explaining to end users how recommendations are derived
6. Interface 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 or other forms of knowledge in the knowledge base and “fires” those that are triggered by facts entered by the user
Reasoning Strategies used by the Inference Engine: Forward chaining Backward chaining
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
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 ~ To achieve this goal/outcome, what conditions must be met?
Criteria for Using Expert Systems/When to Build Them - Human expertise is needed but one expert can’t investigate all the dimensions of a problem - Scarcity of human experts - 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
Criteria for NOT Using Expert Systems/When NOT to Build Them 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
Expert System 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
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 judgment, experience, intuition - May have high development & maintenance costs - May raise legal and ethical concerns - Over time may weaken the human expertise in the organization
Case-based Reasoning - Knowledge and past experiences of human specialists are represented as cases (situations, events) and stored in a database for later retrieval - System searches for stored cases with problem characteristics similar to new one, finds closest fit, and applies solutions of old case to new case - System can query the user for clarification or more information in order to find a match - Successful and unsuccessful applications are tagged and linked in database for future referral - Used in medical diagnostic systems, customer support
Fuzzy Logic - A mathematical method of handling imprecise or subjective information - Allows shades of gray - does not require everything to be simple black or white, yes/no, T/F - Allows the system to make “educated guesses” based on the likelihood or probability that they are right - Allows for the consideration of approximate values, incomplete or ambiguous data - Designed to help computers simulate vagueness and uncertainty in common situations - More closely resembles human reasoning - Provides solutions to problems requiring expertise that is difficult to represent in the form of crisp IF-THEN rules
Neural Networks - Attempt to emulate the way the human brain works & learns - Use hardware and software that parallel the processing patterns of a biological brain – modeled after the brain’s mesh-like networks of interconnected neurons - Network “learns” patterns from large quantities of data by searching for relationships, building models, and correcting over and over again the model’s own mistakes. - Humans “train” the network by feeding it data for which the inputs produce a known set of outputs or conclusions. - Useful for solving complex, poorly understood problems for which large amounts of data have been collected.
Genetic Algorithms - Attempt to find the optimal solution for a specific problem by examining very large number of alternative solutions for that problem - Based on techniques inspired by evolutionary biology: inheritance, mutation, selection, and so on - Use adaptive procedures based on the evolutionary natural selection and survival of the fittest processes to generate increasingly better solutions to a problem - Used to solve complex problems that are very dynamic and complex, involving hundreds or thousands of variables or formulas
Intelligent Agents - Programs that work in the background without direct human intervention to carry out specific, repetitive, and predictable tasks for users - Use a limited built-in or learned knowledge base to perform rule-based processes - Often referred to as “bots” - Examples/uses: web marketing, virtual catalogs, shopping and information agents, personal agents, data mining agents, monitoring & surveillance agents
Knowledge Management - The processes developed for creating, storing, transferring, and applying knowledge - Increases the ability of organization to learn from environment and to incorporate knowledge into business processes and decision making - Knowing how to do things effectively and efficiently in ways that other organizations cannot duplicate is major source of profit and competitive advantage - Self-generated business knowledge is a strategic asset and can provide a competitive advantage
Knowledge Mgt- Three kinds of knowledge 1. Structured 2. Semistrutured 3. Tacit Knowledge (unstructured)
1. Structured explicit knowledge that exists in formal documents or rules (reports, presentations, manuals, books, proposals)
2. Semistructured e-mail, voice mail, digital pictures, bulletin-board postings, designs, memos, graphics, bulleting board postings
3. Tacit knowledge (unstructured) - knowledge residing in heads of employees, rarely written down; personal or informal knowledge -Tacit knowledge often represents an organization’s best practices
Enterprise-wide knowledge management systems - Deal with all three types of knowledge - General-purpose, firm-wide systems that collect, store, distribute, and apply digital content and knowledge
Enterprise-wide knowledge management systems Include: Enterprise Content Management Systems Digital Asset Management Systems Knowledge Network Systems Collaboration Tools Social Bookmarking Learning Management Systems
Enterprise content management systems - Have capabilities for knowledge capture, storage, retrieval, distribution and preservation - Have repositories for documents and best practices - Capabilities for collecting and organizing semistructured knowledge such as e-mail - Use classification schemes to organize information into meaningful categories so it can be “tagged” for easy retrieval
Digital asset management systems Manage unstructured digital data like photographs, graphic images, video, audio
Knowledge network systems (Expertise location and management systems) - Used to organize tacit knowledge - Provide online directory of corporate experts in well-defined knowledge domains - Use communication technologies to make it easy for employees to find appropriate expert in firm.
Social bookmarking - is a collaboration tool -allows users to save their bookmarks publicly to Web pages and tag with keywords
Folksonomies - is a collaboration tool - user-created taxonomies created for shared bookmarks and social tagging
Learning management systems (LMS) - is a collaboration tool - provide tools for management, delivery, tracking, and assessment of various types of employee learning and training
Knowledge Work Systems (KWS) - Specialized systems for knowledge workers - Requirements of knowledge work systems: ~ Specialized tools such as powerful graphics, analytical tools, and communications and document management ~ Computing power to handle sophisticated graphics or complex calculations ~ Access to external databases ~ User-friendly interfaces ~ Knowledge workstations are often designed and optimized for the specific tasks to be performed
Examples of Knowledge Work Systems - Computer-aided design (CAD) systems - Investment workstations - Virtual reality systems
Virtual Reality - “Artificial Reality”, computer-simulated reality - Goal of virtual reality (VR): ~ Create an environment in which users can interact and participate as they do in the real world
VR Technology Uses computer-generated, three-dimensional images to create the illusion of interaction in a real-world environment Allows one or more users to move and react/interact in a computer-simulated environment
Visual and aural systems - Head Mounted Display with eye screens, position trackers to monitor location and movement - Stereo earphones
Manual control for navigation & selection Data glove, game controller, data suit with position trackers
A Haptic Interface relays the sense of touch and other physical sensations by using a glove and position tracker (or other haptic device)
Locomotion tracker – Component of VR system - a walker or other input device (sensor-enabled boots, shoes, rug, mat, etc.) to capture and track foot movement
Central coordinating processor and software system - generates and manipulates high quality graphics in real-time - is a component of VR system - generates and manipulates high quality graphics in real-time
Virtual Reality Applications - Military flight simulations - Virtual medicine - Entertainment - Education and training - Marketing- Real estate, tourism, product trials - Design and testing
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