Ch. 13 MIS

Heuristics

consist of common sense, rules of thumb, educated guesses, instinctive judgments -not formal knowledge but helps finding a solution to a problem without following a rigorous algorithm -used by expert systems

Artificial intelligence

consists of related technologies that try to simulate and reproduce human thought behavior, including thinking, speaking, feeling, and reasoning. -apply computers to areas that require knowledge, perception, reasoning, understanding, and cognitive abilities -generating and displaying thoughts and facts -encompasses many related technologies like robotics, expert systems, fuzzy logic, ANN, NLP *more intelligent than traditional info systems; getting computers to perform tasks usually handled by humans

Uses for ANNs

deep learning, large data sets -bankruptcy prediction, credit rating, investment analysis, oil/gas exploration, target marketing, computer and network security

Natural Language Processing (NLP)

developed so users could communicate with computers in human language

Virtual catalog

displays product descriptions based on customers' previously experiences and preferences

Factual knowledge

facts related to a specific discipline, subject, or problem

Shopping and Information Agents

help users navigate through the vast resources available on the Web and provide better results in finding information. These agents can navigate the Web much faster than humans and gather more consistent, detailed information. They can serve as search engines, site reminders, or personal surfing assistants.

Meta-knowledge

knowledge about knowledge; enables an expert system to learn from experience and examine/extract relevant facts to determine the path to a solution

Soft robot

made of elastomer (rather than the hard physical materials of conventional robots), is simpler to make and less expensive and is used for an increasing number of applications -used for: high-speed food handling, precise pick and place, adaptive grasping, warehouse logistics, advanced assembly, medical field

Expert systems

mimic human expertise in a particular field to solve a problem in a well-defined area -used in medicine, geology, education, and oil exploration

Artificial Neural Network (ANN)

networks that learn and are capable of performing tasks that are difficult with conventional computers, such as playing chess, recognizing patterns in faces and objects, and filtering spam and email -used for poorly structured problems-- when data is fuzzy and uncertainty is involved -CANNOT supply explanations for solution like an expert system does -creates a model based on input and output

Robots

one of the most successful applications of AI; perform well at simple, repetitive tasks and can be used to free workers from tedious or hazardous jobs -used commonly in assembly lines -limited mobility

Personal agents

perform specific tasks for a user, such as remembering info for filling out web forms or completing email addresses after the first few characters are typed

Explanation facility

performs tasks similar to what a human expert does by explaining to end users how recommendations are derived -helps gives users confidence in the system's results

Case-based reasoning (CBR)

problem-solving technique that matches a new case (problem) with a previously solved case and its solution, both stored in a database After searching for a match, a CBR system offers a solution; if no match is found, even after supplying more info, the human expert must solve the problem *Retrieve, reuse, revise, retain

Machine learning

process and procedure by which knowledge is gained through experience; in other words, computers learn without being explicitly programmed

User interface

provides user-friendly access to the expert system

Contextual computing

refers to a computing environment that is always present, can feel our surroundings, and-- based on who we are, where we are, and whom we are with-- offer recommendations

Heuristic knowledge

rules related to a problem or discipline

Genetic algorithms (GAs)

search algorithms that mimic the process of natural evolution. They are used to generate solutions to optimization and search problems using such techniques as mutation, selection, crossover, and chromosome. -used for optimization problems that deal with many input variables

Knowledge base management system

similar to DBMS; used to keep the knowledge base updated, with changes to facts, figures, and rules

Knowledge base

similar to a database, but in addition to storing facts and figures, it keeps track of rules and explanations associated with facts Must include: factual knowledge, heuristic knowledge, meta-knowledge

Inference engine

similar to the model base component of a decision support system; by using different techniques, such as forward and backward chaining, it manipulates a series of rules

Intelligent agents (bots)

software capable of reasoning and following rule-based processes; they are becoming more popular, especially in e-commerce ex. Siri, Alexa

Knowledge acquisition facility

software package with manual or automated methods for acquiring and incorporating new rules and facts so the expert system is capable of growth

Backward chaining

the expert system starts with the goal-- the "then" part and backtracks to find the right solution

Monitoring and surveillance agents

usually track and report on computer equipment and network systems when a system crash or failure might occur

Data-mining agents

work with a data warehouse, detecting trends and discovering new information and relationships among data items that were not readily apparent

2 Main activities of a NLP

1. Interfacing- accepting human language as input, carrying out the corresponding command, and generating the necessary output 2. Knowledge acquisition- using the computer to read large amounts of text and understand the information well enough to summarize important points and store info so the system can respond to inquiries about the content

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

Layers of ANN

1. Output layer 2. Input layer 3. Middle (hidden) layer

Techniques used in genetic algorithms

1. Selection or survival of the fittest- gives preference or a higher weight to better outcomes 2. Crossover- combines good portions of different outcomes to achieve a better outcome 3. Mutation- tries combinations of different inputs randomly and evaluates the response 4. Chromosome- a set of parameters that defines a proposed solution to the problem the GA is trying to solve

Robots can take over these jobs:

1. Telemarketer 2. Logan officer 3. Credit analyst 4. Cashier 5. Line cook 6. Paralegal 7. Roofer 8. Bus Driver

IT used in many decision-making analyses

1. What-is: used in transaction processing/mgmt info systems 2. What-if: used in decision support systems

Integrated (or intelligent) DSS

DSS with expert systems, natural language processing, and artificial neural networks; more efficient, powerful DSS -Expert systems=explanation capabilities -ANN= learning capability -NLP= interface easier to use

Criteria for using expert systems

-A lot of human expertise is needed, but a single expert can't tackle the problem -Knowledge is rules or heuristics; a well-defined algorithm is not available -Has already been handled successfully by human experts before -Subject is limited -Requires consistency and standardization -Involves many rules -Scarcity of experts in the organization or key experts are retiring

Characteristics of sophisticated intelligent agent

1. Adaptability- learn from previous knowledge and go beyond info given previously; make adjustments 2. Autonomy- able to operate w/ minimum input 3. Collaborative behavior- able to work and cooperate w/ other agents 4. Humanlike interface- able to interact w/ users in more natural language 5. Mobility- able to migrate from one platform to another with minimum human intervention 6. Reactivity- able to select problems or situations that need attention and act on them

Uses of expert systems

Airline industry, forensics lab work, banking and finance, education, food industry, personnel management, security, US Government, Agriculture.

Forward chaining

a series of if-then-else condition pairs is performed -"if" condition is evaluated first

Fuzzy logic

allows a smooth, gradual transition between human and computer vocabularies and deals with variations in linguistic terms by using a degree of membership - how relevant an item or object is to a set; higher number=more relevant