A Stochastic Tree-Search Algorithm for Generative Grammars1

2012 ◽  
Vol 12 (3) ◽  
Author(s):  
Matthew I. Campbell ◽  
Rahul Rai ◽  
Tolga Kurtoglu
Keyword(s):  
Conceptual Design ◽  
Search Algorithm ◽  
User Interaction ◽  
Decision Making Process ◽  
Tree Search ◽  
Design Decisions ◽  
Definition Of ◽  
Tree Search Algorithm ◽  
Stochastic Tree ◽  
Single Configuration

This paper presents a new search method that has been developed specifically for search trees defined by a generative grammar. Generative grammars are useful in design as a way to encapsulate the design decisions that lead to candidate solutions. Since the candidate solutions are not confined to a single configuration or topology and thus useful in conceptual design, they may be difficult to computationally analyze. Analysis is achieved in this method by querying the user. A formal definition of a rule-based interactive tree-search is presented in this paper. The user interaction is kept to 30 pair-wise comparisons of candidates. From the data gathered from the comparisons, a stochastic decision-making process infers what candidate solutions best match the known optimal. The method is implemented and applied to a grammar for tying neckties. It is shown through 21 user experiments and 4000 automated experiments that the method consistently finds solutions within the 99.8 percentile. The computational complexity of the proposed algorithm is also studied. The implications of this method for conceptual design are expounded on in the conclusions.

A Stochastic Graph Grammar Algorithm for Interactive Search

2009 ◽  
Author(s):  
Matthew I. Campbell ◽  
Rahul Rai ◽  
Tolga Kurtoglu
Keyword(s):  
Conceptual Design ◽  
User Interaction ◽  
Generative Grammar ◽  
Search Method ◽  
Graph Grammar ◽  
Decision Making Process ◽  
Design Decisions ◽  
Interactive Search ◽  
Stochastic Graph ◽  
Single Configuration

This paper presents a new search method that has been developed specifically for search trees defined by a generative grammar. Generative grammars are useful in design as a way to encapsulate the design decisions that lead to candidate solutions. Since the candidate solutions are not confined to a single configuration or topology and thus useful in conceptual design, they may be difficult to computationally analyze. Analysis is achieved in this method by querying the user. The user interaction is kept to a maximum of thirty pair-wise comparisons of candidates. From the data gathered from the comparisons, a stochastic decision making process infers what candidate solutions best meet the user’s preference. The method is implemented and applied to a grammar for tying neckties. It is shown through 21 user experiments and 4000 automated experiments that the method consistently finds solutions within the 99.8 percentile. The implications of this method for conceptual design are expounded on in the conclusions.

scholarly journals Extended Increasing Cost Tree Search for Non-Unit Cost Domains

2018 ◽  
Author(s):  
Thayne T. Walker ◽  
Nathan R. Sturtevant ◽  
Ariel Felner
Keyword(s):  
Search Algorithm ◽  
Unit Cost ◽  
Tree Search ◽  
Optimal Solutions ◽  
Time Step ◽  
Unit Costs ◽  
Movement Models ◽  
Multi Agent ◽  
Definition Of ◽  
Tree Search Algorithm

Multi-agent pathfinding (MAPF) has applications in navigation, robotics, games and planning. Most work on search-based optimal algorithms for MAPF has focused on simple domains with unit cost actions and unit time steps. Although these constraints keep many aspects of the algorithms simple, they also severely limit the domains that can be used. In this paper we introduce a new definition of the MAPF problem for non-unit cost and non-unit time step domains along with new multiagent state successor generation schemes for these domains. Finally, we define an extended version of the increasing cost tree search algorithm (ICTS) for non-unit costs, with two new sub-optimal variants of ICTS: epsilon-ICTS and w-ICTS. Our experiments show that higher quality sub-optimal solutions are achievable in domains with finely discretized movement models in no more time than lower-quality, optimal solutions in domains with coarsely discretized movement models.

scholarly journals Incorporation of Potential Fields and Motion Primitives for the Collision Avoidance of Unmanned Aircraft

2021 ◽  
Vol 11 (7) ◽  
pp. 3103
Author(s):  
Kyuman Lee ◽  
Daegyun Choi ◽  
Donghoon Kim
Keyword(s):  
Collision Avoidance ◽  
Search Algorithm ◽  
Unmanned Aircraft ◽  
Artificial Potential Field ◽  
Tree Search ◽  
Local Minima ◽  
Collision Risk ◽  
Motion Primitives ◽  
Multiple Uavs ◽  
Tree Search Algorithm

Collision avoidance (CA) using the artificial potential field (APF) usually faces several known issues such as local minima and dynamically infeasible problems, so unmanned aerial vehicles’ (UAVs) paths planned based on the APF are safe only in a certain environment. This research proposes a CA approach that combines the APF and motion primitives (MPs) to tackle the known problems associated with the APF. Since MPs solve for a locally optimal trajectory with respect to allocated time, the trajectory obtained by the MPs is verified as dynamically feasible. When a collision checker based on the k-d tree search algorithm detects collision risk on extracted sample points from the planned trajectory, generating re-planned path candidates to avoid obstacles is performed. After rejecting unsafe route candidates, one applies the APF to select the best route among the remaining safe-path candidates. To validate the proposed approach, we simulated two meaningful scenario cases—the presence of static obstacles situation with local minima and dynamic environments with multiple UAVs present. The simulation results show that the proposed approach provides smooth, efficient, and dynamically feasible pathing compared to the APF.

scholarly journals Enhanced Reinforcement Learning Method Combining One-Hot Encoding-Based Vectors for CNN-Based Alternative High-Level Decisions

2021 ◽  
Vol 11 (3) ◽  
pp. 1291
Author(s):  
Bonwoo Gu ◽  
Yunsick Sung
Keyword(s):  
Reinforcement Learning ◽  
Search Algorithm ◽  
Classification Criteria ◽  
Tree Search ◽  
Learning Method ◽  
Board Game ◽  
Ancient China ◽  
Monte Carlo Tree Search ◽  
High Level ◽  
Tree Search Algorithm

Gomoku is a two-player board game that originated in ancient China. There are various cases of developing Gomoku using artificial intelligence, such as a genetic algorithm and a tree search algorithm. Alpha-Gomoku, Gomoku AI built with Alpha-Go’s algorithm, defines all possible situations in the Gomoku board using Monte-Carlo tree search (MCTS), and minimizes the probability of learning other correct answers in the duplicated Gomoku board situation. However, in the tree search algorithm, the accuracy drops, because the classification criteria are manually set. In this paper, we propose an improved reinforcement learning-based high-level decision approach using convolutional neural networks (CNN). The proposed algorithm expresses each state as One-Hot Encoding based vectors and determines the state of the Gomoku board by combining the similar state of One-Hot Encoding based vectors. Thus, in a case where a stone that is determined by CNN has already been placed or cannot be placed, we suggest a method for selecting an alternative. We verify the proposed method of Gomoku AI in GuPyEngine, a Python-based 3D simulation platform.

scholarly journals CASA: An Efficient Automated Assignment of Protein Mainchain NMR Data Using an Ordered Tree Search Algorithm

2005 ◽  
Vol 33 (4) ◽  
pp. 261-279 ◽  
Author(s):  
Jianyong Wang ◽  
Tianzhi Wang ◽  
Erik R. P. Zuiderweg ◽  
Gordon M. Crippen
Keyword(s):  
Search Algorithm ◽  
Automated Assignment ◽  
Tree Search ◽  
Nmr Data ◽  
Ordered Tree ◽  
Tree Search Algorithm

Highly efficient tree search algorithm for irreducible site-occupancy configurations

2022 ◽  
Vol 105 (1) ◽  
Author(s):  
Ji-Chun Lian ◽  
Yuan Si ◽  
Tao Huang ◽  
Wei-Qing Huang ◽  
Wangyu Hu ◽  
...  
Keyword(s):  
Search Algorithm ◽  
Site Occupancy ◽  
Tree Search ◽  
Highly Efficient ◽  
Tree Search Algorithm
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