scholarly journals Inner entanglements: Narrowing the search in classical planning by problem reformulation

2019 ◽  
Vol 35 (2) ◽  
pp. 395-429 ◽  
Author(s):  
Lukáš Chrpa ◽  
Mauro Vallati ◽  
Thomas Leo McCluskey
Keyword(s):  
Problem Reformulation

Creative Design Methodology and the SIT Method

1997 ◽  
Author(s):  
Roni Horowitz ◽  
Oded Maimon
Keyword(s):  
Design Methodology ◽  
Search Strategy ◽  
Sufficient Conditions ◽  
Strategy Selection ◽  
Step Procedure ◽  
Creative Solutions ◽  
General Search ◽  
Problem Reformulation ◽  
Creative Problem ◽  
The Given

Abstract The paper presents SIT (Structured Inventive Thinking) — a structured method for enhancing creative problem solving in engineering design. The method is a three step procedure: problem reformulation, general search strategy selection, and an application of idea provoking techniques. The most innovative part of the method is the problem reformulation stage. The given problem is modified through the application of objectively defined and empirically tested set of sufficient conditions for creative solutions. The paper describes the sufficient conditions and the empirical study that demonstrates their appropriateness. Then the whole SIT mechanism is presented with illustrative examples.

Synthesis of efficient constraint-satisfaction programs

2001 ◽  
Vol 16 (1) ◽  
pp. 69-84 ◽  
Author(s):  
STEPHEN J. WESTFOLD ◽  
DOUGLAS R. SMITH
Keyword(s):  
Constraint Satisfaction ◽  
Original Problem ◽  
Lattice Structure ◽  
Solution Space ◽  
Search Space ◽  
Constraint Propagation ◽  
Space Representation ◽  
Solution Methods ◽  
Problem Reformulation ◽  
Effective Representation

In this paper we describe the framework we have developed in KIDS (Kestrel Interactive Development System) for generating efficient constraint satisfaction programs. We have used KIDS to synthesise global search scheduling programs that have proved to be dramatically faster than other programs running the same data. We focus on the underlying ideas that lead to this efficiency. The key to the efficiency is the reduction of the size of the search space by an effective representation of sets of possible solutions (solution spaces) that allows efficient constraint propagation and pruning at the level of solution spaces. Moving to a solution space representation involves a problem reformulation. Having found a solution to the reformulated problem, an extraction phase extracts solutions to the original problem. We show how constraints from the original problem can be automatically reformulated and specialised in order to derive efficient propagation code automatically. Our solution methods exploit the semi-lattice structure of our solution spaces.

Memetic Multi-agent optimization with Problem Reformulation by Coordinate Rotation

2020 ◽  
Author(s):  
Sun Mingyang ◽  
Hou Yaqing ◽  
Zhang Qiang ◽  
Ge Hongwei ◽  
Yang Xin ◽  
...  
Keyword(s):  
Coordinate Rotation ◽  
Multi Agent ◽  
Problem Reformulation

Accelerating Large-Scale Multiobjective Optimization via Problem Reformulation

2019 ◽  
Vol 23 (6) ◽  
pp. 949-961 ◽  
Author(s):  
Cheng He ◽  
Lianghao Li ◽  
Ye Tian ◽  
Xingyi Zhang ◽  
Ran Cheng ◽  
...  
Keyword(s):  
Multiobjective Optimization ◽  
Large Scale ◽  
Problem Reformulation
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