Knowledge discovery is a concept of the field of computer science that describes the process of automatically searching large volumes of data for patterns that can be considered knowledge about the data [1].
Knowledge representation
Knowledge representation is a central problem in artificial intelligence.
Knowledge management - glossary
Like all professions, the world of Knowledge Management has its own cryptic terminology. Words like "taxonomy" and of course the consultant's timeless favourite, "paradigm".
AI Knowledge At Your Fingertips
Forgive the promotion, but there have been more changes on the site - and I'm quite proud of the result! ...
Knowledge Management
Applications of AI to the Organization, Storage, and Dissemination of Corporate Knowledge
AITopics > Applications/Expert Systems > Expert Systems Applications > Knowledge Management ...
Knowledge representation and management
Rule-based systems
Representing knowledge through a KBS can be handled using two kind of programming approach: Procedural and Declarative.
Common Knowledge or Superior Ignorance?
Christopher Locke
Robotics Institute
Carnegie Mellon University
26 August 1990 ...
Goldin-Meadow and colleagues have previously shown that spontaneous gesture during speech contains a form of implicit knowledge - knowledge that cannot be verbally reported, but nonetheless affects performance.
Knowledge Representation and Reasoning: ontologies, foundations of knowledge representation and reasoning, representing and reasoning about objects, relations, events, actions, time, and space; predicate logic, situation calculus, ...
Knowledge
Information can be converted into knowledge about historical patterns and future trends.
Knowledge Sources
The independent modules containing information pertaining to a single part of the problem with each module using the technology best suited for solving that part of the problem.
KSAR ...
Knowledge Based Systems
Knowledge Based System allows a user to interact with a computer program in much the same way that the user would interact with a domain expert.
Knowledge Discovery Nuggets
KDnuggets is the Net's most comprehensive KD/Data Mining site. You'll find a wealth of information ranging from jobs in the field to recent publications.
Protecting the Warehouse ...
Knowledge Representation and Reasoning: In a reasoning problem, one has to reach a pre-defined goal state from one or more given initial states.
Knowledge Acquisition and Analysis
Expert system is all about applying human expertise into computer verse, which is based greatly to the integration of human knowledge with the system. Thus knowledge acquisition is the heart of expert system.
Knowledge Representation
Declarative Representations
Declarative representations are based on the assumption that complex entities can be described as collections of attributes and their associated values.
Knowledge Audit. The systematic analysis of an organization's information and knowledge entities and their key attributes, such as ownership, usage and flows, mapped against user and organizational knowledge needs.
Knowledge Management
To be added
Knowledge Representation
Knowledge representation is one of the two basic techniques of artificial intelligence, the other is the capability to search for end points from a starting point.
Knowledge representation
Main articles: knowledge representation and commonsense knowledge ...
knowledge engineer a person who develops an expert system by gathering knowledge of the domain from human experts and other sources, organizing the knowledge, choosing appropriate software tools, ...
12 Knowledge Representation
12.1 Ontological Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . 437
12.2 Categories and Objects . . . . . . . . . . . . . . . . . . . . . . . . . . . 440
12.3 Events . . . . . . . . . . . . . . . . .
0 Knowledge and Generality
We now turn to another set of questions connected with our long-range goal of understanding "general intelligence".
LOCAL KNOWLEDGE SYSTEMS - frameworks for interacting, understanding, interpreting, and constructing meaning based on local knowledge, oral traditions, and historical experiences of given area or group.
Knowledge base
A systematically structured set of knowledge from an individual field stored in an AI program, typically as concepts, facts and rules.
Knowledge representation ...
No knowledge is needed about the keys, such as an order or a hash function.
For small associative arrays, common in some applications, association lists can take less time and space than other data structures.
The knowledge base is created by knowledge engineers, who interview dozens of human experts in the field.
This knowledge about similarity and dissimilarity is necessary for data mining, pattern recognition, machine intelligent, artificial intelligent and multi-agents system fields. However, the application is not only limited to computer science field.
Expert knowledge
"Uniform" distribution of initial prototypes
Maximizing initial distances ...
Domain knowledge is also critical for outlier detection needed to clean data and avoid classic problems such as a juvenile crime committed by a 80-year-old "child".
I assume knowledge of partitioning - if you are in need of such knowledge, I recommend you start here.
Diagrams
In the following diagrams, colours have the following significance: ...
-No prior knowledge of the underlying statistical nature of the problem is required.
In Soar, the knowledge is stored in long-term memory. Soar uses the chunking mechanism to create productions that are stored in long-term memory. A chunk is nothing but a large production that does the work of an entire sequence of smaller ones.
And this new knowledge puts to rest the old, contrived argument of which is more important, nature or nurture, genes or environment. Do we come into the world fully programmed to act the way we do or are we blank slates waiting to be written upon?
Professor of Knowledge-Based Systems, University of Edinburgh
Technical Director of Artificial Intelligence Applications Institute
B.A. (Hons.) Computer Studies, First Class, Lancaster University
Ph.D. Machine Intelligence, University of Edinburgh ...
common sense knowledge and reasoning This is the area in which AI is farthest from human-level, in spite of the fact that it has been an active research area since the 1950s. While there has been considerable progress, e.g.
data mining
Knowledge extraction mechanism devoted to knowledge discovery in data bases. Some classical applications are: bio-informatics, intruder detection in network.
[close the glossary] ...
Knowledge
Resources
Site
The AI Depot is a website dedicated to helping you solve problems using artificial intelligence. Less hype, more results! ...
Observational knowledge of our world is described by associative rules organized in our heads, and some of these (fabricated) concepts such as marriage serves as guidelines in our social lives. Social life consists of rules, i.e.
Given some a priori knowledge about the cyclical factors affecting the series (e.g., business cycles), the estimates for the different components can be used to compute forecasts for future observations.
However, Turing also notes the importance of being in relationship for the acquisition of knowledge or intelligence.
The significance of DENDRAL was that it was the first successful knowledge-intensive system: its expertise derived from large numbers of special-purpose rules.
Of course we can reason in terms of images, smells and sounds but we also posses the (complex) knowledge of common language (unlike animals, we can also learn things about ourselves, from our parents and DNA; we also posses history encyclopaedias) ...
More usefully there are other AIs customised for specific areas of knowledge. These are not usually referred to as AIs but instead as expert systems.
Using Scientific and Engineering Knowledge
In this case, the above model makes sense based on our definition of the random walk. That is, a random walk is the cumulative sum of uniformly distributed data points.
It then discusses the use of Joint Distributions for representing and reasoning about uncertain knowledge.
What happens when we can easily access all of the world's knowledge? Will this be the end of learning, and exams? Will we become intellectually lazy?
An expert system can solve real-world problems using human knowledge and following human reasoning skills. Knowledge and thinking processes of experts are collected and encoded into a knowledge base.
Currently, sufficiently detailed knowledge of the subcellular workings of the brain does not yet exist, ...
is it knowledge? memory? logical reasoning? thinking speed? pattern recognition? feeling and intuition? all of the above? can we quantify intelligence and what makes it up?
A philosophical doctrine that holds that the mind is born with ideas/knowledge, and that therefore the mind is not a 'blank slate' at birth, as early empiricists such as John Locke claimed.
Pages are intended to be used for learning about genetic algorithms without any previous knowledge from this area. Only some knowledge of computer programming is assumed.
The data mining process,
The knowledge discovery process,
Business process management (BPM) programs,
Knowledge management systems, and
Business ecosystems management processes.
The Data Mining Process ...
connectionism Connectionism is the neural network approach to solving problems in artificial intelligence - the idea being that connectionists feel that it is appropriate to encode knowledge in the weighted connections between nodes in a neural ...
However, the pathfinder has no knowledge of these shorter paths, so its decisions won't be optimal.
A sufficient statistic summarizes all of the information in a random sample so that knowledge of the individual values in the sample is irrelevant in searching for a good esimator for theta.
Nonparametric regression/density estimation An approach to regression/density estimation that doesn't require much prior knowledge but only a large amount of data.
minimal requirements for domain knowledge to determine input parameters;
ability to deal with noise and outliers;
insensitivity to order of input records; ...
Deterministic Occurring in a non-random manner such that the next state of a system depends only on prior states of the system or the environment. Perfect knowledge of previous states implies perfect knowledge of the next state.
McCulloch-Pitts neurone
Micro Interpreter for Knowledge Engineering
N
natural language processing
neats vs. scruffies ...
Greedy Approach: Policy: Always pick the arm with the largest estimated value. This is called exploiting your current knowledge.
Non-Greedy Approach: If you select a nongreedy approach then you are said to be exploring.
 See also: Artificial intelligence, AI, Neural network, Agent, Machine learning
  
|
RSS
