Add to ORKGFinding the shortest plan for a given planning problem is extremely hard. We present a domain independent ap-proach for plan optimisation based on Genetic Program-ming. The algorithm is seeded with correct plans cre-ated by hand-encoded heuristic policy sets. The plans are very unlikely to be optimal but are created quickly. The suboptimal plans are then evolved using a genera-tional algorithm towards the optimal plan. We present initial results from Blocks World and found that GP method almost always improved sub-optimal plans, of-ten drastically
This paper deals with the problem of optimization of a production plan by using genetic algorithms. ...
Despite recent progress in planning, many complex domains and even larger problems in simple domains...
Despite recent progress in planning, many complex domains and even larger problems in simple domains...
Finding the shortest plan for a given planning problem is extremely hard. We present a domain indepe...
Declarative problem solving, such as planning, poses interesting challenges for Genetic Programming ...
Declarative problem solving, such as planning, poses interestig challenges for Genetic Programming ...
There are several ways of applying Genetic Programming (GP) to STRIPS-like planning in the literat...
There are many different approaches to solving planning problems, one of which is the use of domain ...
This paper describes a genetic planning system, i.e., a program capable of solving planning problems...
Despite recent progress in planning, many complex domains and even simple domains with large problem...
Proceedings of: 15th International Conference on Machine Learning, Madison (Wisconsin, USA), July 24...
Two near-optimum planarization algorithms are presented. The algorithms belong to a general class of...
AbstractThe purpose of this article is to present a multi-strategy approach to learn heuristics for ...
In this paper we describe SINERGY, which is a general-purpose, AI planning system that is based on t...
The purpose of this article is to present a multi-strategy approach to learn heuristics for planning...
This paper deals with the problem of optimization of a production plan by using genetic algorithms. ...
Despite recent progress in planning, many complex domains and even larger problems in simple domains...
Despite recent progress in planning, many complex domains and even larger problems in simple domains...
Finding the shortest plan for a given planning problem is extremely hard. We present a domain indepe...
Declarative problem solving, such as planning, poses interesting challenges for Genetic Programming ...
Declarative problem solving, such as planning, poses interestig challenges for Genetic Programming ...
There are several ways of applying Genetic Programming (GP) to STRIPS-like planning in the literat...
There are many different approaches to solving planning problems, one of which is the use of domain ...
This paper describes a genetic planning system, i.e., a program capable of solving planning problems...
Despite recent progress in planning, many complex domains and even simple domains with large problem...
Proceedings of: 15th International Conference on Machine Learning, Madison (Wisconsin, USA), July 24...
Two near-optimum planarization algorithms are presented. The algorithms belong to a general class of...
AbstractThe purpose of this article is to present a multi-strategy approach to learn heuristics for ...
In this paper we describe SINERGY, which is a general-purpose, AI planning system that is based on t...
The purpose of this article is to present a multi-strategy approach to learn heuristics for planning...
This paper deals with the problem of optimization of a production plan by using genetic algorithms. ...
Despite recent progress in planning, many complex domains and even larger problems in simple domains...
Despite recent progress in planning, many complex domains and even larger problems in simple domains...