Add to ORKGA number of current planners make use of automatic domain analysis techniques to extract information such as state invariants or necessary goal orderings from a planning domain. There are als
Planning domain analysis provides information which is useful to the domain designer, and which can ...
Humans exhibit a significant ability to answer a wide range of questions about previously unencounte...
Abstract – One of the most important problems of traditional A.I. planning methods such as non-linea...
TALPLANNER is a forward-chaining planner that uti-lizes domain-dependent knowledge to control search...
Current planners show impressive performance in many real world and artificial domains by using plan...
Intelligent problem solving requires the ability to select actions autonomously from a specific stat...
There are a lot of approaches for solving planning prob-lems. Many of these approaches are based on ...
As planning is applied to larger and richer domains the e ort involved in constructing domain descri...
Automated planning is a central area of artificial intelli-gence, involving the design of languages ...
Automated, domain-independent planning is a research area within Artificial Intelligence that is use...
We describe some new preprocessing techniques that enable faster domain-independent planning. The fi...
TALplanner is a forward-chaining planner that relies on domain knowledge in the shape of temporal lo...
TALplanner is a forward-chaining planner that relies on domain knowledge in the shape of temporal lo...
Intelligent agents solving problems in the real world require domain models containing widespread kn...
Abstract: "Intelligent problem solving requires the ability to select actions autonomously from a sp...
Planning domain analysis provides information which is useful to the domain designer, and which can ...
Humans exhibit a significant ability to answer a wide range of questions about previously unencounte...
Abstract – One of the most important problems of traditional A.I. planning methods such as non-linea...
TALPLANNER is a forward-chaining planner that uti-lizes domain-dependent knowledge to control search...
Current planners show impressive performance in many real world and artificial domains by using plan...
Intelligent problem solving requires the ability to select actions autonomously from a specific stat...
There are a lot of approaches for solving planning prob-lems. Many of these approaches are based on ...
As planning is applied to larger and richer domains the e ort involved in constructing domain descri...
Automated planning is a central area of artificial intelli-gence, involving the design of languages ...
Automated, domain-independent planning is a research area within Artificial Intelligence that is use...
We describe some new preprocessing techniques that enable faster domain-independent planning. The fi...
TALplanner is a forward-chaining planner that relies on domain knowledge in the shape of temporal lo...
TALplanner is a forward-chaining planner that relies on domain knowledge in the shape of temporal lo...
Intelligent agents solving problems in the real world require domain models containing widespread kn...
Abstract: "Intelligent problem solving requires the ability to select actions autonomously from a sp...
Planning domain analysis provides information which is useful to the domain designer, and which can ...
Humans exhibit a significant ability to answer a wide range of questions about previously unencounte...
Abstract – One of the most important problems of traditional A.I. planning methods such as non-linea...