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Domain adaptation[1][2][3] is a field associated with machine learning and transfer learning. This scenario arises when we aim at learning a model from a source data distribution and applying that model on a different (but related) target data distribution. For instance, one of the tasks of the common spam filtering problem consists in adapting ...
Sample domain model for a health insurance plan. In software engineering, a domain model is a conceptual model of the domain that incorporates both behavior and data. [1] [2] In ontology engineering, a domain model is a formal representation of a knowledge domain with concepts, roles, datatypes, individuals, and rules, typically grounded in a description logic.
Domain analysis. In software engineering, domain analysis, or product line analysis, is the process of analyzing related software systems in a domain to find their common and variable parts. It is a model of wider business context for the system. The term was coined in the early 1980s by James Neighbors. [1][2] Domain analysis is the first ...
Blocks world. The blocks world is a planning domain in artificial intelligence. The algorithm is similar to a set of wooden blocks of various shapes and colors sitting on a table. The goal is to build one or more vertical stacks of blocks. Only one block may be moved at a time: it may either be placed on the table or placed atop another block.
Data modeling is a process used to define and analyze data requirements needed to support the business processes within the scope of corresponding information systems in organizations. Therefore, the process of data modeling involves professional data modelers working closely with business stakeholders, as well as potential users of the ...
A Bayesian network (also known as a Bayes network, Bayes net, belief network, or decision network) is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG). [1] While it is one of several forms of causal notation, causal networks are special cases of Bayesian ...
Such planners are called "domain independent" to emphasize the fact that they can solve planning problems from a wide range of domains. Typical examples of domains are block-stacking, logistics, workflow management, and robot task planning. Hence a single domain-independent planner can be used to solve planning problems in all these various ...
Planning Domain Definition Language. The Planning Domain Definition Language (PDDL) is an attempt to standardize Artificial Intelligence (AI) planning languages. [1] It was first developed by Drew McDermott and his colleagues in 1998 mainly to make the 1998/2000 International Planning Competition (IPC) possible, and then evolved with each ...