scholarly journals Temporal Planning using Subgoal Partitioning and Resolution in SGPlan

2006 ◽  
Vol 26 ◽  
pp. 323-369 ◽  
Author(s):  
Y. Chen ◽  
B. W. Wah ◽  
C. Hsu
Keyword(s):  
Mutual Exclusion ◽  
Search Space ◽  
Global Constraints ◽  
Planning Problem ◽  
Temporal Planning ◽  
Space Reduction ◽  
Trade Offs ◽  
Constraint Partitioning ◽  
International Planning ◽  
Optimization Search

In this paper, we present the partitioning of mutual-exclusion (mutex) constraints in temporal planning problems and its implementation in the SGPlan4 planner. Based on the strong locality of mutex constraints observed in many benchmarks of the Fourth International Planning Competition (IPC4), we propose to partition the constraints of a planning problem into groups based on their subgoals. Constraint partitioning leads to significantly easier subproblems that are similar to the original problem and that can be efficiently solved by the same planner with some modifications to its objective function. We present a partition-and-resolve strategy that looks for locally optimal subplans in constraint-partitioned temporal planning subproblems and that resolves those inconsistent global constraints across the subproblems. We also discuss some implementation details of SGPlan4, which include the resolution of violated global constraints, techniques for handling producible resources, landmark analysis, path finding and optimization, search-space reduction, and modifications of Metric-FF when used as a basic planner in SGPlan4. Last, we show results on the sensitivity of each of these techniques in quality-time trade-offs and experimentally demonstrate that SGPlan4 is effective for solving the IPC3 and IPC4 benchmarks.

SUBGOAL PARTITIONING AND GLOBAL SEARCH FOR SOLVING TEMPORAL PLANNING PROBLEMS IN MIXED SPACE

2004 ◽  
Vol 13 (04) ◽  
pp. 767-790 ◽  
Author(s):  
BENJAMIN W. WAH ◽  
YIXIN CHEN
Keyword(s):  
Saddle Points ◽  
Extended Period ◽  
Global Search ◽  
Global Constraints ◽  
Benchmark Problems ◽  
Planning Problem ◽  
Global Constraint ◽  
Local Maxima ◽  
Temporal Planning ◽  
Variable Space

We study in this paper the partitioning of the constraints of a temporal planning problem by subgoals, their sequential evaluation before parallelizing the actions, and the resolution of inconsistent global constraints across subgoals. Using an ℓ1-penalty formulation and the theory of extended saddle points, we propose a global-search strategy that looks for local minima in the original-variable space of the ℓ1-penalty function and for local maxima in the penalty space. Our approach improves over a previous scheme that partitions constraints along the temporal horizon. The previous scheme leads to many global constraints that relate states in adjacent stages, which means that an incorrect assignment of states in an earlier stage of the horizon may violate a global constraint in a later stage of the horizon. To resolve the violated global constraint in this case, state changes will need to propagate sequentially through multiple stages, often leading to a search that gets stuck in an infeasible point for an extended period of time. In this paper, we propose to partition all the constraints by subgoals and to add new global constraints in order to ensure that state assignments of a subgoal are consistent with those in other subgoals. Such an approach allows the information on incorrect state assignments in one subgoal to propagate quickly to other subgoals. Using MIPS as the basic planner in a partitioned implementation, we demonstrate significant improvements in time and quality in solving some PDDL2.1 benchmark problems.

scholarly journals Optimum Distribution System Expansion Planning Incorporating DG Based on N-1 Criterion for Sustainable System

2021 ◽  
Vol 13 (12) ◽  
pp. 6708
Author(s):  
Hamza Mubarak ◽  
Nurulafiqah Nadzirah Mansor ◽  
Hazlie Mokhlis ◽  
Mahazani Mohamad ◽  
Hasmaini Mohamad ◽  
...  
Keyword(s):  
Power Systems ◽  
Distribution System ◽  
Industrial Revolution ◽  
Optimal Solution ◽  
Search Space ◽  
Planning Problem ◽  
System Expansion ◽  
Optimum Distribution ◽  
Optimal Sizing ◽  
Expansion Planning

Demand for continuous and reliable power supply has significantly increased, especially in this Industrial Revolution 4.0 era. In this regard, adequate planning of electrical power systems considering persistent load growth, increased integration of distributed generators (DGs), optimal system operation during N-1 contingencies, and compliance to the existing system constraints are paramount. However, these issues need to be parallelly addressed for optimum distribution system planning. Consequently, the planning optimization problem would become more complex due to the various technical and operational constraints as well as the enormous search space. To address these considerations, this paper proposes a strategy to obtain one optimal solution for the distribution system expansion planning by considering N-1 system contingencies for all branches and DG optimal sizing and placement as well as fluctuations in the load profiles. In this work, a hybrid firefly algorithm and particle swarm optimization (FA-PSO) was proposed to determine the optimal solution for the expansion planning problem. The validity of the proposed method was tested on IEEE 33- and 69-bus systems. The results show that incorporating DGs with optimal sizing and location minimizes the investment and power loss cost for the 33-bus system by 42.18% and 14.63%, respectively, and for the 69-system by 31.53% and 12%, respectively. In addition, comparative studies were done with a different model from the literature to verify the robustness of the proposed method.

Automated Design of Image Operators that Detect Interest Points

2008 ◽  
Vol 16 (4) ◽  
pp. 483-507 ◽  
Author(s):  
Leonardo Trujillo ◽  
Gustavo Olague
Keyword(s):  
Computer Vision ◽  
Search Space ◽  
Search Problem ◽  
Interest Points ◽  
Fitness Evaluation ◽  
Modern Computer ◽  
New Perspective ◽  
Processing Techniques ◽  
Point Detection ◽  
Optimization Search

This work describes how evolutionary computation can be used to synthesize low-level image operators that detect interesting points on digital images. Interest point detection is an essential part of many modern computer vision systems that solve tasks such as object recognition, stereo correspondence, and image indexing, to name but a few. The design of the specialized operators is posed as an optimization/search problem that is solved with genetic programming (GP), a strategy still mostly unexplored by the computer vision community. The proposed approach automatically synthesizes operators that are competitive with state-of-the-art designs, taking into account an operator's geometric stability and the global separability of detected points during fitness evaluation. The GP search space is defined using simple primitive operations that are commonly found in point detectors proposed by the vision community. The experiments described in this paper extend previous results (Trujillo and Olague, 2006a,b) by presenting 15 new operators that were synthesized through the GP-based search. Some of the synthesized operators can be regarded as improved manmade designs because they employ well-known image processing techniques and achieve highly competitive performance. On the other hand, since the GP search also generates what can be considered as unconventional operators for point detection, these results provide a new perspective to feature extraction research.

scholarly journals SMT-Based Contention-Free Task Mapping and Scheduling on 2D/3D SMART NoC with Mixed Dimension-Order Routing

2022 ◽  
Vol 19 (1) ◽  
pp. 1-21
Author(s):  
Daeyeal Lee ◽  
Bill Lin ◽  
Chung-Kuan Cheng
Keyword(s):  
System Performance ◽  
Search Space ◽  
Satisfiability Modulo Theories ◽  
Low Latency ◽  
Task Mapping ◽  
Single Cycle ◽  
Space Reduction ◽  
Reduction Techniques ◽  
2D And 3D ◽  
Mixed Dimension

SMART NoCs achieve ultra-low latency by enabling single-cycle multiple-hop transmission via bypass channels. However, contention along bypass channels can seriously degrade the performance of SMART NoCs by breaking the bypass paths. Therefore, contention-free task mapping and scheduling are essential for optimal system performance. In this article, we propose an SMT (Satisfiability Modulo Theories)-based framework to find optimal contention-free task mappings with minimum application schedule lengths on 2D/3D SMART NoCs with mixed dimension-order routing. On top of SMT’s fast reasoning capability for conditional constraints, we develop efficient search-space reduction techniques to achieve practical scalability. Experiments demonstrate that our SMT framework achieves 10× higher scalability than ILP (Integer Linear Programming) with 931.1× (ranges from 2.2× to 1532.1×) and 1237.1× (ranges from 4× to 4373.8×) faster average runtimes for finding optimum solutions on 2D and 3D SMART NoCs and our 2D and 3D extensions of the SMT framework with mixed dimension-order routing also maintain the improved scalability with the extended and diversified routing paths, resulting in reduced application schedule lengths throughout various application benchmarks.

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